Nordic Journal of Urban Studies , volume 2

Transkript

1 Vol. 2

2 Nordic Journal of Urban Studies , volume 2 Cities and sustainable urban development have been raised as key research topics internationally. Urban studies has emerged as a broad field of research that includes studies in several disciplines, with emphasis on social geography, planning and development studies, sociology, environmental science and political science. Research here is largely about exploring structures, expansion and transformation of cities, urban processes of inclusion, exclusion, marginalization and liberation, as well as economic and political processes. Research environments are strengthened by interdisciplinary arenas for dialogue on such issues. Still, many of the discussions within urban research in the Nordic countries take place either in disciplinary national journals or in interdisciplinary international journals. The Nordic Journal of Urban Studies represents a new Nordic academic arena for interdisciplinary urban studies and welcomes empirical and theoretical papers that contribute to our understanding of cities. The journal is Open Access and is published twice a year by Scandinavian University Press. Editors Gro Sandkjær Hanssen, Senior researcher, NIBR, OsloMet and professor II, NMBU Cecilie Sachs Olsen, Senior Researcher, NIBR, OsloMet Marikken Wullf-Wathne, Doctoral student, Division of Urban and Regional Studies, KTH Royal Institute of Technology and NIBR, OsloMet Bengt Andersen, Senior researcher, AFI, OsloMet Per Gunnar Røe, Professor, Department of Sociology and Human Geography, UiO Jørn Ljunggren, Senior Research Manager, Ipsos Håvard Haarstad, Professor, Department of Geography, UiB Lisbet Harboe, Associate Professor, Institute of Urbanism and Landscape, The Oslo School of Architecture and Design Editorial secretary Cecilie Sachs Olsen, Senior Researcher, NIBR, OsloMet Contact the editors: cecilies@oslomet.no Design og sats: Type-it AS, Trondheim Forsidedesign: ISSN online: Universitetsforlaget 2022

3 Content Articles By eller forstad? En diskusjon av barnefamiliers flyttemotiver Bengt Andersen Joar Skrede Why are some families with children leaving the inner city and other staying? Rolf Barlindhaug Swedish conditions? Characteristics of locations the Swedish Police label as vulnerable Manne Gerell Mia Puur Nicklas Guldåker Urban planning for car-free housing and ideas of future desired states Lina Berglund-Snodgrass Commentary articles Language dilemmas: a culture of bilingualism David Jordhus-Lier Elin Børrud Ryan Gever Yngve Heiret Siddharth Sareen Eira Witsø Towards a Posthuman Practice for Architecture and Urbanism? Matthew Dalziel 82 90

4 article Nordic Journal of Urban Studies Fagfellevurdert Vitenskapelig publikasjon VITENSKAPELIG PUBLIKASJON Volume 2, No , p ISSN online: DOI: By eller forstad? En diskusjon av barnefamiliers flyttemotiver Bengt Andersen Forsker 1, Arbeidsforskningsinstituttet, OsloMet (Førsteforfatter) bengt.andersen@oslomet.no Joar Skrede Forsker 1, Norsk institutt for kulturminneforskning, NIKU (Andreforfatter) joar.skrede@niku.no Sammendrag Oslo kommune har som uttalt mål at indre by skal være et attraktivt bosted for barnefamilier. Flere kommentatorer, inkludert forskere, har hevdet at reurbanisering er en tydelig trend. Det vil si at bylivet er populært, særlig for middelklassen inkludert barnefamilier. Likevel viser forskning at denne gruppen er ustabil, da en stor andel barnefamilier flytter ut før barna når skolealderen. Samtidig som de store boligbyggerne i Oslo bygger nye leiligheter, finnes det i hovedstadens omland veletablerte småhusområder som tiltrekker seg mange av de tidligere urbane barnefamiliene. I denne artikkelen drøfter vi ulike faktorer som kan tenkes å påvirke barnefamiliers valg av bosted. Nøkkelord flyttehistorier, boligbygging, markedsføring, fortetting, bærekraft Abstract The municipality of Oslo has expressed a goal of the inner city to become an attractive place to live for families with children. Several commentators have argued that reurbanisation is a trend; that urban life is popular, especially for the middle class, including families with children. Nevertheless, research has also shown that this group of dwellers is unstable. For different reasons, a large proportion of the families with children have moved out before the children reach school age. The property developers in Oslo are primarily constructing apartments, while families with children often prefer to reside in suburban houses. In this article, various factors that may influence families choice of residence are discussed. Keywords residential decisions, housing construction, marketing, densification, sustainability Introduksjon Representanter for Oslo kommune har ved flere anledninger uttrykt et ønske om at indre by skal være et sted hvor også barnefamiliene skal ønske å bo (f.eks. Johansen, 2018). Samtidig har det i flere år vært diskusjoner om hvorfor barnefamilier ofte flytter fra indre by. De vitenskapelige bidragene har hovedsakelig vært kvantitativt-orienterte analyser av flyttemønstre Copyright 2022 Author(s). This is an open access article distributed under the terms of the Creative Commons CC BY-NC 4.0 License ( ).

5 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No (f.eks. Galster & Turner, 2017; Wessel & Lunke, 2021; Wessel & Nordvik, 2019) samt kvantitativt-orienterte analyser av survey-materiale (Andersson et al., 2017). Kvalitativt-orienterte bidrag av enkeltpersoners flyttehistorier er det langt færre av. I denne artikkelen søker vi å bidra til å fylle et kunnskapshull om barnefamiliers erfaringer med Oslo som bo- og oppvekststed. Ved å trekke på data fra dybdeintervjuer med oslobeboere og utflyttere fra hovedstaden, samt ved å fortolke de åpne svaralternativene i spørreundersøkelser vi distribuerte til beboere i og utflyttere fra sentrumsnære nabolag, forsøker vi å belyse hvordan ulike elementer kan tenkes å påvirke barnefamiliers bostedspreferanser. Basert på foreliggende forskning kan vi konstatere at flyttevalg påvirkes av husholdningens økonomiske ressurser, utvalget på det aktuelle boligmarkedet og andre makro- og mesofaktorer (Andersen & Skrede, 2017; Coulter et al., 2016; Sampson, 2019). Samtidig spiller andre faktorer inn, som stedstilhørighet, livsfase, livsstil og minner (Andreotti & Le Galès, 2008; Savage, 2008; Winstanley et al., 2002). For å studere flyttevalg er det derfor hensiktsmessig å kartlegge både enkeltaktørers valg og handlinger, samt de strukturelle rammene aktørene handler innenfor (Galster & Turner, 2017). For de som har (økonomiske) ressurser til å velge bosted, kan en analyse av flyttemotiver få frem hvordan en rekke faktorer har hatt betydning. Det å avgjøre hva som er viktigst av f.eks. nabolagets sosiale og etniske profil (Burke, 2012), skolekrets (Bernelius & Vilkama, 2019) eller områdets arkitektur og design (Andersen, 2008), kan være utfordrende. Det er også fordi mer «vage» fenomener som kultur og oppfatninger av steder er uforutsigbare (f.eks. Andersen, 2014; Andersen et al., 2021; Coulter et al., 2016; Sharp & Warner, 2018; Wnuk et al., 2021). Gjennom analysene av vårt datamateriale trådde en kompleksitet i barnefamilienes flyttevurderinger og nabolagsvalg frem. Vi har derfor anlagt en eklektisk teoretisk tilnærming for å konseptualisere det vi hevder er signifikante elementer i flyttehistoriene. Fra Rossi (1980) og Galster (1987) har vi livssyklusbegrepet, men vi mener at «livsfase» er mer presist om en vil få frem de ulike valgene personer kategorisert f.eks. som småbarnsforeldre kan ta (f.eks. Kadasia et al., 2020), mens fra Ærø (2006) henter vi ideen om «livsstil». Videre trekker vi på Eriksens (2019) analyser av «security» og «insecurity» (oversatt her til henholdsvis «trygghet» og «usikkerhet»/ «uro»), som kobles til Kohns (2008) diskusjon av «tillit» og Barnes (1954), som utviklet begrepet «sosiale nettverk» i analysen av lokalsamfunn. Disse ser vi igjen i sammenheng med teorier om kulturell reproduksjon (f.eks. Harrell-Levy & Harrell, 2019). Disse teoriene vil kunne befrukte hverandre; vi viser f.eks. at det er nyttig å se Ærø (2006) i sammenheng med Harrell-Levy og Harrell (2019). I tillegg lener vi oss på Shirazi med flere (2020) og deres diskusjon av sosial bærekraft for å få frem det vi hevder er en spenning mellom barnefamilienes preferanser eller verdier og Oslo kommunes bypolitiske fortettingsstrategi en bypolitikk som kan sies å utgjøre en av de «strukturelle rammene» for våre informanters handlinger. Vi argumenterer for at denne rammen kan antas å påvirke våre informanter, men i tråd med Ericksons (2004) kritikk av blant annet Bourdieu og Foucault har ikke rammen nødvendigvis en disiplinerende effekt i den forstand at den tvinger personer til å bli boende i byen. I flere europeiske storbyer har forskere funnet at de sentrale byområdene er det foretrukne bostedet for mange velstående borgere (Andreotti et al., 2015). Samtidig har studier av botilfredshet avdekket at de som bor i eneboliger, generelt er mer fornøyde enn de som bor i leiligheter (Emami & Sadeghlou, 2020, s. 13). Mangel på gode grøntområder samt støyende omgivelser og høy tetthet er ofte koblet til lavere trivsel (Emami & Sadeghlou, 2020, s ). Likevel har studier fra Oslo også vist at leilighetsbeboere i kompakte byområder trives godt (Mouratidis, 2018; 2019) selv om man i Oslo finner at bosatte «i de tetteste bydelene er minst fornøyd med tilgang til parker og friområder for barn» (Nordbakke, 2018, s. 26). Det «sosiale limet» ser også ut til å være svakere i de tettere delene av Oslo-regionen

6 6 BENGT ANDERSEN AND JOAR SKREDE (Mouratidis & Poortinga, 2020; se også Robbins, 2005). En mulig delforklaring kan være at mange av de som bor mer urbant, ser for seg å flytte ut når de går over i en ny livsfase, og av den grunn investerer mindre i sosiale aktiviteter (se f.eks. Rye, 2006; Wessel & Lunke, 2021). Folk som er bosatt i «utsatte» områder av Oslo (Andersen & Brattbakk, 2020, s. 131), som Tøyen og Grønland (Kadasia et al., 2020), synes også å ha svakere forankring i sitt eget nabolag enn beboere i mer velstående deler av byen (Mouratidis, 2020, se også Andersen et al., 2017). Nylig viste Mouratidis og Yiannakou (2021) at selv om mange beboere trives i de tettere delene av Oslo-området, synes «nabolagslykke» hvordan man emosjonelt erfarer nærmiljøet å være sterkere i de mindre tette områdene. Det er med andre ord ikke kun klassetilhørighet og fysiske trekk ved egen bolig eller i nabolaget som påvirker om en trives dere en bor, om det lokale fellesskapet er sterkt, eller om man heller drømmer om å flytte til andre bomiljøer (se Andersen et al., 2021). Oslos høye boligprisererallmentkjent,ogdeterblantannethevdetatnyereprosjekter,somsørenga,erlitetilgjengelige (Cavicchia, 2021, s. 10). Innenfor Oslos kommunegrense kan det også bli vanskelig for mange i middelklassen å realisere drømmen om hus og hage (Løwe, 2002, s. 18; Opinion, 2020; Sandlie, 2005), også fordi mange av hagene har blitt gjenbygd (f.eks. Andersen et al., 2018). Ulike fortettingsprosjekter, i tråd med politiske føringer (Andersen & Skrede, 2017), har medført at nesten samtlige nye boliger som bygges, er leiligheter. Om man ønsker et hus med hage, er det lettere å finne i forstaden (Skrede & Andersen, 2021). I hvert fall ser det ut til at mange av middelklassens barnefamilier søker seg dit (Wessel & Lunke, 2021). Etter en gjennomgang av den Oslo-baserte forskningen på flytteprosesser, inkludert noen henvisninger til relevante studier av områdetransformasjoner, redegjør vi for vår forskningsstrategi og empiriske datamateriale. Deretter diskuterer vi noen såkalte «push»- og «pull»-faktorer for å belyse hvorfor mange barnefamilier flytter ut av indre by. Vi konsentrerer oss i denne artikkelen om barnefamilier tilhørende middelklassen, da blant annet Hjorthol og Bjørnskau (2005, s. 359) tidligere har påpekt hvordan indre by har blitt et mer attraktivt bosted for denne kategorien. Vi har en bred tolkning av «middelklasse», og enkelte av våre informanter vil nok like gjerne kunne omtales som en slags kulturell overklasse bestående av blant annet arkitekter og kunstnere (Hansen & Toft, 2021, s. 6 7). Blant våre informanter har vi imidlertid ikke representanter for den økonomiske overklassen og ei heller det som omtales som ufaglærte arbeidere eller velferdsavhengige (Hansen & Toft, 2021, s. 7). Avslutningsvis fletter vi de ulike trådene sammen og konkluderer med at selv om mange trives i den mer klassiske urbane byen, er det mange faktorer som dytter eller trekker folk ut av den. Vi peker ikke på én hovedforklaring, men argumenterer for at flytte- og boligforskere burde se på samspillet mellom personlige, emosjonelle, biografiske, sosiale, kulturelle, økonomiske, materielle og strukturelle elementer. Forskning på flytteprosesser og nabolagsendringer I internasjonal forskning er det å få barn forbundet med flytting. Det er fordi småbarnsfamilier føler et behov for en større og annen type bolig etter hvert som familien blir større (Dieleman, 2001; Rossi, 1980; jf. Sampson, 2012, s. 298). Lignende funn er gjort i Norge, inkludert Oslo (Barlindhaug et al., 2018, s. 46; Bråthen et al., 2007, s. 56). I tillegg til fysiske egenskaper ved boligen og nærmiljøet er det tidligere vist at spesielt foreldre i de østlige områdene i indre by er bekymret for barnas oppvekstvilkår (Andersen, 2014; Andersen et al., 2017; Kadasia et al., 2020). Studier har påpekt hvordan en relativt høy grad av inn- og utflytting fra bestemte nabolag har negativ innvirkning på det sosiale livet i lokalsamfunnet. Dette kan også bidra til høyere

7 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No kriminalitetsrater (Sampson, 2012, s ). For vår del er det aktuelt å rette blikket mot Oslos indre by der særlig utflyttede barnefamilier bidrar til ustabilitet: «Familier som blir værende i indre [Oslo] må forholde seg til lav bostabilitet, og dermed markante utfordringer for tillit, lokalt engasjement og sosial samhandling» (Wessel & Lunke, 2021, s. 146, med referanse til Sampson et al., 1999). Med «indre by» mener vi bydelene Frogner, Gamle Oslo, Grünerløkka, Sagene og St. Hanshaugen inkludert Sentrum (jf. illustrasjonen i Wessel & Lunke, 2021, s. 135). Vi har intervjuet beboere og utflyttere i flere av disse bydelene, men dataene vi har analysert i denne artikkelen, er hovedsakelig hentet fra nåværende og tidligere beboere i Gamle Oslo. Selv om det er store materielle og sosiale forskjeller internt i en bydel som Gamle Oslo, med områder som Ensjø, Grønland, Tøyen og Sørenga, ser vi også at flere av våre respondenter i disse fire områdene opplever det vi under omtaler som «urban uro» (jamfør Eriksen, 2019). Dette er områder som fortettes og transformeres gjennom omfattende leilighetsbygging, i tråd med ideene bak kompakt byutvikling (Næss, 2021, s. 20) eller fortettingsstrategier (Mete & Xue, 2020, s. 14), som igjen kan kobles til bærekraftvisjoner (Andersen & Skrede, 2017; Cavicchia, 2021; Skrede & Andersen, 2022). Vi hevder derfor at det er fornuftig også å inkludere et område som Ensjø i en diskusjon av urbanisme versus suburbanisme som bostedsvalg (jf. Gans, 1969). Vi vil presisere at Osloregionen på mange måter kan sies å utgjøre et sammenhengende hele, og at bygningstyper eller arkitektonisk uttrykk utenfor Oslo ikke alltid skiller seg fra det som er innenfor kommunegrensen. Likevel er kommunegrensen av betydning i vår sammenheng. Foreldre er f.eks. opptatt av skolekvalitet, og en eventuell utflytting fra Oslo vil ta barna til andre inntakskommuner. I en rapport fra Statistisk sentralbyrå vises det til at «[i] de indre bydelene flytter nesten 3 4 av nullåringene ut før de når skolealder, mens utflyttingen er klart mindre i de ytre byområdene» (Stambøl, 2015, s. 4; se også Wessel & Barstad, 2016). I en artikkel i Oslospeilet vises det til at andelen barn som bor i indre by, de siste årene har økt fra 22 % til 27 %, men her «mer enn halveres antall barn per fødselskohort fra de er 0 år til de blir 6 år» (Strand, 2021, s. 17). At barnefamilier utgjør en «ustabil» kategori i indre by, er også tema for Wessel og Lunke (2021). De konkluderer med at førstegangsbarnefamilier i Oslo følger en klassisk tidrom-bane fra indre by til forstedene (s. 144). De finner også at majoritetsnorske foreldre har en høyere grad av bostabilitet om de bor i områder med høyere andeler store boliger (s. 144), noe som kan ses i sammenheng med Kalsås (2020; 2021) funn om at leilighetsnormen, som gir føringer om å bygge flere store leiligheter, har ført til et større aldersmangfold i indre by, selv om «stabiliteten [blant barnefamiliene ikke har] blitt bedre» (Kalsås, 2021, s. 47). Selv om det foregår en utstrakt barnefamilieutflytting fra indre by, er det flere forskere som vektlegger at området også er attraktivt for middelklassen. Dette viser også våre data. Områder som Grünerløkka og deler av Gamle Oslo har gjennomgått materielle og kulturelle strøksendringer gjennom flere år (se Andersen et al., 2021; Kadasia et al., 2020). I litteraturen skildres det hvordan positive stedskvaliteter som gå- og sykkelvennlighet, kulturtilbud og andre tjenester er attraktive for middelklassen en klasse som dermed antas å søke tilbake til sentrale byområder, slik at man får en «reurbanisering» (Hjorthol & Bjørnskau, 2003; 2005). Selv om det er en nettoutflytting av barnefamilier, blir mange boende, og det er også flytting internt i indre by. En som hadde flyttet fra et indre by-nabolag til et annet, var Emma, en gift småbarnsmor vi intervjuet etter at familien nettopp hadde solgt sin blokkleilighet og kjøpt seg ny bolig i et mer «småhus-preget» område i samme bydel. Gjennom Emmas flyttehistorie ble det klart at det ikke var den fysiske tettheten som først og fremst kjentes påtrengende eller fremmedgjørende, men den sosiale og kulturelle (se også Wirth, 1938). Hun reagerte særlig på områdets etniske mangfold og hvordan dette i hennes øyne ble feiret og trukket

8 8 BENGT ANDERSEN AND JOAR SKREDE frem på bekostning av «norsk kultur» i alle lokale arrangementer. Emmas flytteforklaring kan knyttes til en bekymring for om det var mulig å sikre en kulturell reproduksjon om de ble værende i et mangfoldig område. For Emma bidro usikkerheten til å erodere hennes stedstilhørighet og identifikasjon med stedet. Familien endte derfor med å flytte til et mer homogent område ikke langt unna. Forskningsstrategier Våre data og analyser inkluderer en fortolkning av en markedsføringskampanje fra eiendomsutvikleren Block Watne. Kampanjen «Flytt ut av byen» ble hengt opp på Oslos busser, trikker og t-baner i Den ble også distribuert i aviser og sosiale medier. I vår analyse av kampanjen trekker vi på begreper fra sosialsemiotikken. Siden en representasjon skriftlig, visuell eller gjennom andre uttrykksformer ikke kan få med samtlige aspekter av en begivenhet, er det interessant å undersøke hva som er fremhevet, underordnet, fjernet eller tillagt (Skrede, 2021, s. 11). Vi har også gjennomført dybdeintervjuer med beboere og utflyttere og samlet inn data fra digitale spørreundersøkelser som vi distribuerte til beboere på og utflyttere fra Tøyen, Kampen, Grønland, Sørenga, Ensjø og Helsfyr. I denne artikkelen konsentrerer vi oss om en fortolkning av de «kvalitative» svarene fra undersøkelsenes åpne spørsmål. Respondentene ble blant annet rekruttert gjennom informasjonsskriv i postkasser og via SMS (til utflyttere). Våre data stammer fra ulike forskningsprosjekter gjennomført i Oslo og noen omegnskommuner. De omtalte spørreundersøkelsene ble besvart av totalt personer. Vi har gjennomført over 100 dybdeintervjuer (mellom én og to timers varighet) med bosatte i eller utflyttere fra indre by. Hovedsakelig har vi intervjuet foreldre alene, men vi har også gjennomført enkelte gruppeintervjuer, blant annet med en barselgruppe i Tøyen-Grønland-området. Fra studier som Galster og Turner (2017) og Wessel og Lunke (2021) vet vi en del om betydningen av materielle eller økonomiske faktorer for flytting i Oslo. Mange av våre informanter koblet flytting til et behov for mer plass, spesielt etter å ha fått barn. Videre var økonomi en viktig ramme for valgmulighetene. Vi var derimot nysgjerrig på om det likevel kan tenkes å være andre, mer «uklare» elementer som virker inn på familienes bostedsvalg. I analysearbeidet leste vi derfor gjennom de transkriberte intervjuene i sin helhet. Når en vil fortolke mening, må fenomener og ytringer ses i den konteksten de inngår i (jamfør Geertz, 1993). Her trekker vi også på Smalls (2004, s ) analytiske strategi, der han vektlegger «alt» som kan sies å være betydningsfullt for å forklare handlinger. Målet er ikke å avdekke universelle mekanismer, men å få frem hva som har «betinget» handlinger i den situasjonen en studerer (f.eks. et nabolag) betingelser som også kan tenkes å være relevante i andre kontekster. Når f.eks. Pernille forklarer hvorfor familien flyttet fra indre by (for å få hus med hage) til Ski (slekt i nærområdet), er det interessant at hun i forbifarten forklarte at et sted som Lillestrøm var helt uaktuelt. Det er først etter at intervjuet er ferdig og lydopptaket er avsluttet, at Pernille forteller at hun hadde lest negative saker om Lillestrøm i avisene. Dette står ikke under noen av flyttespørsmålene i det transkriberte intervjuet, men inngår i en fotnote med tilleggsinformasjon om selve intervjusituasjonen eller konteksten. I tråd med Smalls (2004) anbefaling om å kartlegge om de identifiserte «betingelsene» gjelder i andre kontekster, har vi i tidligere, i andre prosjekter, sett nærmere på avisartiklers påvirkning på foreldres skolevalg (se Andersen, 2014). I den kommende gjengivelsen av informantutsagn er alle informanter anonymisert og gitt pseudonymer.

9 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No «Push»- og «pull»-faktorer i indre by Over viste vi kort til begrepet reurbanisering. I en studie avviser Hjorthol og Bjørnskau at økende byspredning er en trend, og fremstiller det heller slik at bykjernen er blitt den sterkeste magneten, særlig for middelklassen (Hjorthol & Bjørnskau, 2005, s. 353, 368). Mens småbarnsfamilier tidligere flyttet til forstedene, blant annet for å gi barna bedre oppvekstmuligheter, har drabantbyens fordeler nå blitt svekket i konkurranse med mer sentrale områders positive kvaliteter (Hjorthol & Bjørnskau, 2005, s. 359). Som også flere andre studier har vist, er det mange som finner Oslos mer urbane eller kompakte områder attraktive (Skrede & Andersen, 2022; Mouratidis, 2018). Da vi intervjuet tre mødre i en barselgruppe, var det flere faktorer de trakk frem som tiltrekkende ved det urbane nærområdet. Noen av disse kan med Ærø (2006) og Hjorthol og Bjørnskau (2005) sies å være forenlig med en urban middelklasselivsstil der normer, verdier, estetiske preferanser og «konsum av symboler» (Ærø, 2006, s. 110) vektlegges. Mødrene forklarte at de fant et mangfold av folk i «deres» sentrumsområde; man kunne gå i de klærne man ønsket, og det var kort vei til det de vurderte som attraktive butikker og serveringssteder (som ikke var «kjeder»). Samtidig var ikke slike livsstilsfaktorer nødvendigvis noe som determinerte tilhørighet. Betydningen av sosiale nettverk, livsfase, livssyklusendringer, kulturell reproduksjon og «uro» eller «usikkerhet» kom også frem i løpet av samtalen. Linda, en av mødrene, hadde allerede flyttet til en forstad, blant annet fordi en bygård uten heis gjorde det vanskelig å komme hjem med barnevogn. Nærskolen i byen ble heller ikke vurdert som aktuell. Dessuten var naboenes livsstil, med et høyt forbruk av alkohol og sigaretter, en kilde til mistrivsel. Valg av forstadsområde ble også gjort fordi ektemannen hadde slekt der. De to andre mødrene, som fortsatt bodde i indre by, så også for seg å flytte. Hovedgrunnen var et ønske om mer plass, og de drømte om hus, hage og garasje. For å realisere drømmen måtte de ut av indre by og trolig krysse kommunegrensen. Det var imidlertid ikke bare familieforøkelse som gjorde at de følte behov for en større bolig. Det handlet også om trygghet et element som av Mouratidis og Yiannakou (2021) kobles både til nabolagstilfredshet og nabolagslykke. Mens de før brukte byens tilbud på kvelds- og nattestid uten store bekymringer, var denne erfaringen nå endret, som eksemplifisert av Ella: Ja, nå som man har blitt mor, er det annerledes. Jeg har aldri vært bekymret for å gå fra Grünerløkka og hjem på kveldstid, eller i totida på natta, men i vinter, da jeg skulle gå med sønnen i barnevogna i ellevetiden på kvelden, tok jeg for første gang opp telefonen og ringte til mamma. Jeg snakket med henne hele veien hjem, for jeg tenkte at guri hva gjør jeg hvis noe skjer? Jeg kan ikke bare legge på spring med en barnevogn. Jeg har ikke opplevd noe selv, altså, men jeg pleier heller aldri å gå der det er skummelt (intervju juni, 2017). For Ella spilte utenbys slekt inn på hva som kanskje ville trekke henne og familien ut av Oslo. Ella forklarte at hennes søster også hadde flyttet ut av Oslo, slik at de hadde færre bekjente som kunne sitte barnevakt. Befolkningsmessig ustabilitet i indre byområder ser ut til å innvirke på betingelsene for de som (midlertidig) blir værende i indre Oslo (jamfør Wessel & Lunke, 2021). Vi ser altså at det også eksisterer en annen virkelighet enn reurbanisering som påvirker individuelle flytte- og nabolagshistorier. Dette mønsteret bekreftes også om vi ser hen til flyttestatistikken (Barlindhaug et al., 2018; Stambøl, 2015; Strand, 2021; Wessel & Lunke, 2021). Dette er en virkelighet som uroer lokale politikere i Oslo kommune. Blant annet sa byråd for byutvikling at «vi ønsker at flere barnefamilier skal bli boende i indre by. Både fordi det er bra med variasjon, det gjør byen bedre [og fordi] en by som er god for barn, [ ] også [er] en by som er god for resten av oss» (NRK, 2019). For å nå et slikt mål forklarte

10 10 BENGT ANDERSEN AND JOAR SKREDE byråden at de stilte krav om at det skulle bygges flere store leiligheter i indre by, og at man måtte ha trygge og gode uterom og skoleveier (NRK, 2019). Det kan likevel synes som om barnefamilienes preferanser ikke alltid er forenlige med hverken kommunens ønsker eller boligtilbudene i indre by. Det ser heller ikke ut som at eksisterende kommunale tiltak har vært tilstrekkelige. Gjennomførte tiltak ble nylig oppsummert i kommunens eget Oslospeilet (2021, s. 18): «Opprusting av lekeplasser, trafikkreduserende tiltak, sikring av skoleveier og regulering av andelen små og store leiligheter i boligprosjekter.» Gjennom våre feltarbeid, intervjuer og spørreundersøkelser ser vi likevel at såkalte «møteplasser» som lekeplasser også kan være kilde til en viss uro blant foreldre, da bruken av dem ikke kan dikteres gjennom planlegging. Hjemløse kan «okkupere» dem, rusmisbrukere etterlate brukerutstyr, og ungdommer kan «henge» der etter at barn har lagt seg. Dette er faktorer som kan bidra til at foreldre oppfatter nabolaget som støyete og/ eller utrygt. Maren, en middelklassekvinne og småbarnsmor som bodde i en høyblokk i indre by, forklarte i et intervju at familien trivdes godt i området. Boligen på 72 kvadratmeter vurderte hun som stor nok til tre, men Maren var mer usikker på om den var passende hvis de fikk et barn til. Både hun og mannen hadde gåavstand til jobben, sykkelavstand til barnehagen og brukte mange av nabolagets urbane tilbud, som parker og kafeer. Likevel var hun usikker på om de ville bli boende her. I tillegg til selve boligen var usikkerheten knyttet til kvaliteten på den lokale skolen, biltrafikken i nabolaget og noen lokale ungdomsgjenger ved lekeplassene. Da vi spurte om hva hun så for seg om fem år når datteren skulle begynne på skolen, sa hun: Det kommer an på ting. Jeg tror at vi har lyst på et barn til, men vi har vurdert også bare ett barn, og da kan vi faktisk fint bo her. Med to barn tipper jeg det er mest sannsynlig at vi flytter, men det er ikke sikkert at vi gjør det før om fem seks år. Flere av mine venninner har flyttet [til en Akershus-forstad]. [ ] Sosialt sett taler det jo for at vi flytter dit også, for jeg tipper at byen vil bli gradvis ribba for de vi kjenner. Men jeg er litt sta, så jeg kan være tilbøyelig til å ikke bukke under, men vi får bare se hva som føles mest riktig etter hvert (intervju januar, 2019). Selv om Maren og flere av våre informanter i indre by kanskje kunne passe inn i reurbaniseringsnarrativet, kom det likevel ofte frem en viss tvil knyttet til fremtiden en usikkerhet vi har funnet blant flere barnefamilier i Oslo-nabolag preget av «mangfold» og endring, inkludert fysiske endringer gjennom fortetting (f.eks. Andersen, 2014; Skrede & Andersen, 2022). Karen var en av de som svarte på vår spørreundersøkelse til beboerne på Ensjø. Hun bodde med mann og førskolebarn i en ny leilighet på 65 kvadratmeter. De hadde venner i nabolaget og dermed også en sosioaffektiv, i tillegg til materiell, forankring: Ved å kjøpe leilighet hadde familien investert økonomisk på Ensjø. Ved å treffe naboer ukentlig, personer Karen omtalte som venner, investerte hun også sosialt og emosjonelt i nabolaget (Andersen et al., 2017, s. 203). Derfor svarte Karen «ja» på spørsmålet om familien ønsket å bli boende. På oppfølgingsspørsmålet om hvor lenge de ønsket å bo her, svarte hun likevel «mellom 1 og 3 år». Karen sa ikke direkte hvorfor hun og mannen hadde en så kort tidshorisont, men ved å se på andre deler av de åpne svaralternativene kommer vi på sporet av en viss fremtidsuro og nåtidig mistrivsel. Karen mente at «østkantskolene er mindre trygge», hun var misfornøyd med Ensjø som oppvekststed og beskrev nabolaget som «forurenset, en del støy og parkene er ikke særlig godt vedlikeholdt». Samtidig skrev hun at kollektivtilbudet var godt, samt at det var tilstrekkelig med spisesteder i nærheten. I en rapport konkluderer Barlindhaug og medforfatterne med at boforholdene var det viktigste flyttemotivet i alle kategorier flyttere, men det å få barn var også viktig for en stor

11 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No del (Barlindhaug et al., 2018, s. 60). I en annen studie antyder Andersson med flere at skole nok både er en viktig «push»- og «pull»-faktor i flyttebeslutninger i en by som Oslo (Andersson et al., 2017, s. 499), noe flere dybdestudier av Oslos multietniske nabolag også fremhever (Andersen, 2014; Bragen, 2013; Kadasia et al., 2020; Kvamme, 2013; Vassenden, 2007). Gitt denne forskningen er det påfallende at Barlindhaug med flere ikke foretar en detaljert diskusjon av betydningen av skolekretser eller skolesegregasjon (se Rogne et al., 2021; Wessel & Nordvik, 2019). Forfatterne påpeker dog at «stabilt klassemiljø» blir vurdert som viktig av et flertall, mens den etniske elevsammensetningen blir vurdert som viktig for rundt 50 % av respondentene (Barlindhaug et al., 2018, s , jf. tabellene 10.6, 10.12). Videre finner de at «et trygt bomiljø for barn» var meget viktig for mange av utflytterne. «Lekemuligheter for barn» og «trygg skolevei» ble også rangert høyt (Barlindhaug et al., 2018, s ). Flere småbarnsfamilier ønsker altså en annen boligform enn leiligheter i blokker og bygårder. I Oslo er uansett den dominerende boligtypen blokkleiligheter. Ifølge Statistikkbanken til Oslo kommune var det i 2017 totalt boliger i Oslo (Oslo kommune, 2018). Det er særlig i indre by at leiligheter utgjør den klart største andelen boliger. Blant annet var det i Bydel Gamle Oslo hele boliger i blokker og leiegårder av en total boligmasse på enheter. Det samme mønsteret ser man i bydeler som Grünerløkka, St. Hanshaugen, Frogner og Sagene. Når byen vokser, er det stort sett gjennom oppføringen av flere leiligheter i blokker og leiegårder (se også Cavicchia, 2021; Kalsås, 2020). Dette kan ses i lys av Oslo kommunes vektlegging av en kompakt byutvikling, som igjen kan kobles til et mål om bærekraftig utvikling (Andersen & Skrede, 2017; Cavicchia, 2021). På denne bakgrunnen kan man spørre seg om boligtilbudet treffer barnefamiliene. Våre egne studier, samt studien til Wessel og Lunke (2021) og rapporten til Barlindhaug med flere (2018), gir i hvert fall grunn til å så tvil om det. Sistnevnte fant at de som flyttet fra indre by til drabantbyene, her Akershus, flyttet til en bolig som i snitt var 77 kvadratmeter større (fra indre by til Akershus øst) eller 91 kvadratmeter større (fra indre by til Akershus vest). Og det var primært «for liten bolig» utflytterne trakk frem om boligen når de ble spurt om flyttemotiv. Majoriteten flyttet dessuten fra leilighet uten privat uteareal til hus med privat uteareal, noe de også vektla som et viktig flyttemotiv (Barlindhaug et al., 2018, s , 81, 85 86). I vår spørreundersøkelse fra Tøyen og Kampen spurte vi blant annet om hva folk eventuelt ikke likte ved området. Mens noen la vekt på sosiale forhold, var det andre som vektla fysiske forhold, særlig trafikk, støv og støy noe også Barlindhaug med flere fant (2018, s. 81). Noen av våre respondenter knytter disse problemene til den pågående utbyggingen på Ensjø, ikke langt fra der de selv bodde på datainnsamlingstidspunktet. Blant de som relativt nylig har flyttet inn i nye boligprosjekter, er det i vår Ensjø-undersøkelse svar som både peker på bostedstilfredshet og misnøye, slik vi allerede har antydet gjennom en diskusjon av Karens svar over. En annen middelklassekvinne i en romslig bolig sa at hun satt mest pris på hvor sentralt det er med kort avstand til sentrum. Det hun var minst fornøyd med, var mangel på parkeringsplasser og at det var for mye trafikk og støy. Siden det også var for få grøntområder, vurderte hun og familien å flytte ut. Ola, en middelklasserespondent, bodde også i en ny, romslig leilighet. Han og kona hadde fått et barn etter at de flyttet inn. Familien var fornøyd med romløsningen, men ikke uterommene og tilbudene til barn og unge. Gitt antall blokker/boliger var det satset for lite på den sosiale og grønne infrastrukturen. Ola oppfattet ikke nabolaget som et sosialt fellesskap, da det var for mye inn- og utflytting. Hans sosiale relasjoner utenfor kjernefamilien strakk seg derfor, i romlig forstand, utenfor Ensjø-området. I spørreskjemaet oppga Ola at familien ville flytte ut av Oslo til et eneboligområde innen tre år, til et sted nærmere hans egne foreldre.

12 12 BENGT ANDERSEN AND JOAR SKREDE «Flytt ut av byen» en reklamekampanje fra Block Watne Vi vil nå foreta et stilistisk skifte i vår empiriske beskrivelse av flyttemotiver. Fra å ha gjengitt noen hovedfunn fra et utvalg dybdeintervjuer og surveysvar vil vi nå kort gjennomgå en reklamekampanje Block Watne lanserte i Kampanjen «Flytt ut av byen» var laget for å lokke osloborgere ut av byen og til forstedene. Et hovedmål er å illustrere hvordan Oslos «svakheter» tett, dyr og trang samt folks oppfatninger av byens demografiske sammensetning kan brukes strategisk for å få folk til å flytte ut av hovedstaden. Ved å studere noen av bildene fra kampanjen vil vi vise at det er en litt karikert fremstilling av by og land som konstrueres, der bostedstilfredsheten er å finne utenfor det urbane miljøet. Selv om fremstillingen kan oppfattes som dikotom, hadde kampanjen neppe fungert om den ikke hadde hatt klangbunn i folks erfaringer med å bo i indre Oslo. Foto: Block Watne. Brukt med tillatelse fra Block Watne På det første bildet (illustrasjon 1) ser vi to unge jenter med vannpistoler. Jentene er plassert i forgrunnen og engasjerer seg i løs og ledig lek etter å ha flyttet ut av byen. Det skaper en fornemmelse av at vi også kan få like mye glede i hverdagen som disse jentene så lenge vi flytter til «antiurbane» omgivelser (jf. Mouratidis & Yiannakou, 2021). Bakgrunnen er dekontekstualisert; vi ser noen furutrær som konnoterer noe landlig og norsk, men settingen kan i prinsippet være hvor som helst i Norge. Dekontekstualiserte bakgrunner er vanlige i markedsføring fordi de tillater publikum å lese inn sin egen kontekst i bildene (Machin, 2004). Videre ser vi to eksempler på moderne arkitektur til venstre og til høyre i bildet. Dette kan gi assosiasjoner til en distingvert livsstil som ikke har gått tapt, til tross for at en har flyttet ut fra bykjernen. Det er ingen forekomster av visuell støy å se, f.eks. nedslitt bordkledning, flassende maling, graffiti eller vanskjøttede grøntområder, noe vi ofte kan se i mer urbane strøk (Skrede & Andersen, 2020; 2021). Dette er et rent og trygt nabolag å vokse opp i. På et annet bilde (illustrasjon 2) ser vi en mann som driver med hageaktiviteter til venstre, og stell av en potteplante til høyre. Byfolket, kampanjens målgruppe, ser at mannen til venstre smiler mot oss med en liten hagespade i hånden. Nederst i bildet kan vi lese: «Dette kunne vært din mann.» Det er fort gjort å bli fortapt i den landlige drømmen, men om vi leser videre mot høyre, slår realiteten brutalt inn: Teksten lyder: «Men det er det ikke.» Hva vi ser til høyre, er ikke et ubekymret liv utenfor byen, det er vår urbane og beklemte hverdag. Dette er et semiotisk grep som spiller på folks misnøye med det urbane, i dette tilfellet trangboddhet som umuliggjør hagearbeid i indre by. Mannen har så liten balkong at han må lene ryggen mot veggen for å stelle potteplanten.

13 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No I vårt siste bilde (illustrasjon 3) ser vi en hund som er ute og løper fritt på en eng. Den har en ball i munnen, som indikerer at den og eieren er engasjert i lek. Den løper i fullt firsprang over gresset, og det er bare én labb som så vidt har kontakt med bakken. Scenen kan påkalle en metaforisk assosiasjon til frihet. Nederst i til venstre kan vi også lese: «Dette kunne vært din hund.» Om vi imidlertid ser til høyre i bildet, er situasjonen annerledes. Her sitter hunden på en trapp utenfor en bygård og stirrer tomt i luften. Den er fastbundet, altså det motsatte av den friheten hunden kan oppnå i mer rurale omgivelser. Uten at vi kjenner det faktiske inngangspartiet, er det umulig å si hvor dette er bortsett fra at det er en «typisk» urban setting. Det er en generisk fremstilling som konnoterer urbanitet en fornemmelse som styrkes ved at muren er påført tagging noe som underforstått ikke finnes i forstaden. Fremstillingen er ledsaget av teksten: «Men er det ikke.» Selv om vi intervjuet Linn og hennes familie flere år før kampanjen ble lansert, konstruerer Linn en lignende motsetning mellom det «problematiske» og det «passende» bo- eller oppvekststedet. Linns familie hadde flyttet fra Oslo til forstaden og forklarte hvordan det var å besøke indre øst: «Da jeg kom ut fra parkeringshuset, la jeg merke til det med én gang; den sterke lukten av krydder. Men her hjemme, så lukter det ingenting. Vel, du kan lukte bladene når du går inn i skogen her; det lukter som en søvnig forstad» (intervju, mai 2013). Linn vektla at forstaden også hadde et fravær av støy: «For meg er dette en drøm du blir ikke tvunget til å høre naboens musikk eller lukte hva de spiser til middag.» Familien forklarte at de flyttet ut av byen fordi de ikke ville at barna skulle vokse opp i et mangfoldig område i indre by, særlig med tanke på skolegang, og valgte denne forstaden blant annet fordi kommunen hadde blitt kåret til en av landets beste oppvekststeder. Dessuten hadde de familie i nærheten. Avsluttende diskusjon Flere byforskere har påpekt hvordan myndighetenes mål om bærekraftig byutvikling ofte blir utfordret, forsinket eller motvirket. Politikernes og planleggernes visjoner og strategier møter motstand fra medierepresentasjoner, innbyggernes praksiser og emosjoner, næringslivs- og utbyggerinteresser, historiske føringer, ulike kunnskapssystemer samt andre politiske mål og interesser (f.eks. Andersen, et al., 2021; Andersen & Røe, 2017a; 2017b; Andersen & Skrede, 2017; Cavicchia, 2021; Mete, 2021; Næss, 2019; Skrede & Andersen, 2022). I denne artikkelen har vi blant annet belyst hvordan Block Watnes markedsføringskampanje «Flytt ut av byen» illustrerer at det eksisterer et narrativ om et godt og et mindre godt oppvekstmiljø. En rekke tidligere urbane barnefamilier verdsetter mindre tette småhusområder, noe både kvalitativt- og kvantitativt-orienterte studier av utflyttere også har vist (Andersen, 2014; Barlindhaug et al., 2018). Det er dermed nærliggende å anta at annonsene traff et urbant markedssegment som var åpent for «drømmen» som ble tilbudt utenbys. Samtidig fortalte mange av våre informanter i indre by (f.eks. i bydelene Grünerløkka, St. Hanshaugen og Gamle Oslo) at de var i en livsfase (unge voksne uten barn) som innebar utstrakt bruk av barer, kafeer med mer. Likevel så flere i denne livsfasen for seg at om de fikk både partner og barn, ville dette fremtvinge et behov for større plass. Det vil i neste omgang innebære å flytte ut av byen til det de omtalte som «tryggere» oppvekststeder. Samtidig var det å bo i indre by for mange både moro og bra, noe som bygger opp under oppfatningen om byen som et attraktivt bosted. Uansett vil vi hevde at selv om foreliggende forskning og denne artikkelen viser hvordan materielle og sosiale realiteter innvirker på flyttevalg, så peker vår studie også mot hvordan et narrativ eller en diskurs (her fortellingen om forstaden som kvalitativt sett bedre enn byen) er en annen «virkelighet» som kan tenkes å påvirke

14 14 BENGT ANDERSEN AND JOAR SKREDE nybakte foreldres valg. F.eks. kan byens mange tilbud tilsi at en med fordel kan forbli urban, mens iøynefallende reklamekampanjer nærmest «gir beskjed» om at det «riktige» nå er å bli suburban. Her mener vi det er behov for videre studier, kanskje influert av Ericksons (2004) analyse av hvordan diskurser i offentligheten (f.eks. formidlet i avisene) tas opp i hverdagssamtaler rundt kjøkkenbordene (også Gullestad, 2001). Som nevnt domineres indre Oslo av leiligheter, mange av mindre størrelse, selv om leilighetsnormen ser ut til å ha medført at det er bygget flere større boliger (Kalsås, 2020). Til tross for en rekke miljøtiltak er det fortsatt mye biltrafikk i sentrumsgatene. Grøntområdene er også under press (Andersen, et al., 2018; Nordbakke, 2018). En satsing på møteplasser (Andersen & Brattbakk, 2020), som nye eller opprustede bibliotek (Fagerlid et al., 2021), ønskes velkommen av mange, men vi har også observert og blitt fortalt om konflikter på nettopp disse arenaene: friksjoner som kan gjøre noe med nabolagstilfredsheten. I tillegg er Oslos stadig mer mangfoldige befolkningssammensetning en faktor som ser ut til å gjøre foresatte usikre på om skolene vil gi deres barn en skolegang i tråd med hva foreldrene ønsker (Andersen, 2014; Kadasia et al., 2020). Våre informanter har fortalt at de oppfatter sosiale nettverk (Barnes, 1954) som positive ressurser som bidrar til lokal forankring. Likevel er ikke nødvendigvis nettverk nok til å holde en bofast, noe Karens surveysvar illustrerer. Blant annet var skolesituasjonen noe som bidro til å svekke troen på Ensjø som blivende oppvekststed. Med Eriksen (2019), Harrell-Levy og Harrell (2019) og Small (2004) er usikkerheten om skole og nabolag med på å utfordre flere foreldres tro på at indre by tilbyr de rette betingelsene for kulturell reproduksjon. Kanskje er vi her på sporet av en mulig forklaring på hvorfor det særlig er «nabolagslykke» som synes å være vanskelig å finne i den tette byen (Mouratidis & Yiannakou, 2021). Uten å kunne fastslå i hvilken grad faktorer som krydderlukt og kulturarrangementer påvirket flyttebeslutningene til noen av våre informanter, stiller vi oss tvilende til at politiske tiltak kan forebygge lignende reaksjoner fra andre byboere. Basert på nyere studier som Mouratidis og Yiannakou (2021) og Skrede og Andersen (2022) vil vi også bemerke at beboerens emosjonelle reaksjoner, eller personers følelser, er noe som i større grad burde trekkes inn i debatter om flytting, fortetting og byutvikling. For å parafrasere Cavicchia (2021, s. 11) planlegger kommunen effektivt for fortetting, men synes ikke å ha tilsvarende effektive planer for drømmen om bostedslykke (Skrede & Andersen, 2021). Det er blant annet mot denne bakgrunnen at vi bør forstå at mange tidligere byboere flytter fra indre by til forstedene eller nabokommunene (Kalsås, 2020; 2021; Strand, 2021; Wessel & Lunke, 2021). Det er trolig ikke nok med kommunale ønsker og tiltakspakker for at en større andel av barnefamiliene skal bli boende i byen. Kanskje må vi bare slå oss til ro med at ulike befolkningsgrupper i ulike livsfaser bor på forskjellige steder, samt at ikke alle vil bo i den sosialt og fysisk tette, mangfoldige byen? Referanser Andersen, B. (2008). Eksklusive fellesskap: Et kritisk blikk på nyurbanismen i lys av Kentlands, USA. FORMakademisk, 1(1), Andersen, B. (2014). Westbound and Eastbound. Managing Sameness and the Making of Separations in Oslo (doktoravhandling). Universitetet i Oslo. Andersen, B., Ander, H. E. & Skrede, J. (2021). The directors of urban transformation: The case of Oslo. Local Economy, 35(7), Andersen, B. & Brattbakk, I. (2020). Områdesatsinger i Norge. Et skjørt kunnskapsgrunnlag for strategier og tiltak. Tidsskrift for boligforskning, 3(2),

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18 18 BENGT ANDERSEN AND JOAR SKREDE Sampson, R. J. (2019). Neighbourhood effects and beyond: Explaining the paradoxes of inequality in the changing American metropolis. Urban Studies, 56(1), Sandlie, H. C. (2005). New Life Courses and Postponed Timing of Home Establishment. Open House International, 30(3), Savage, M. (2008). Histories, belongings, communities. International Journal of Social Research Methodology, 11(2), Sharp, G. & Warner, C. (2018). Neighborhood Structure, Community Social Organization, and Residential Mobility. Socius, 4, Shirazi, M. R., Keivani, R., Brownill, S. & Butina Watson, G. (2020). Promoting Social Sustainability of Urban Neighbourhoods: The Case of Bethnal Green, London. International Journal of Urban and Regional Research. Skrede, J. (2021). Visuell kommunikasjon. Oslo: Cappelen Damm Akademisk. Skrede, J. & Andersen, B. (2020). Selling homes: the polysemy of visual marketing. Social Semiotics, Skrede, J. & Andersen, B. (2021). A Suburban Dreamscape Outshining Urbanism: The Case of Housing Advertisements. Space and Culture, 24(4), / Skrede, J. & B. Andersen (2022). The emotional element of urban densification. Local Environment, Small, M. L. (2004). Villa Victoria: The Transformation of Social Capital in a Boston Barrio. University of Chicago Press. Stambøl, L. S. (2015). Flyttemønstre til og fra Oslo blant barn og unge. (Rapporter). SSB Strand, N. P. (2021). Forlater barnefamiliene Oslo? Oslospeilet, 31(1), Vassenden, A. (2007). Flerkulturelle forståelsesformer. En studie av majoritetsnordmenn i multietniske boligområder. Universitetet i Oslo. Wessel, T. & Barstad, A. (2016). Urbanisering og urbanisme. I I. Frønes & L. Kjølsrød (red.), Det Norske Samfunn (s ). Gyldendal. Wessel, T. & Lunke, E. B. (2021). Raising children in the inner city: still a mismatch between housing and households? Housing Studies, 36(1), Wessel, T. & Nordvik, V. (2019). Mixed neighbourhoods and native out-mobility in the Oslo region: The importance of parenthood. Urban Studies, 56(5), Winstanley, A. N. N., Thorns, D. C. & Perkins, H. C. (2002). Moving House, Creating Home: Exploring Residential Mobility. Housing Studies, 17(6), Wirth, L. (1938). Urbanism as a Way of Life. American Journal of Sociology, 44(1), Wnuk, A., Oleksy, T., Toruńczyk-Ruiz, S. & Lewicka, M. (2021). The way we perceive a place implies who can live there: Essentialisation of place and attitudes towards diversity. Journal of Environmental Psychology, 75, Ærø, T. (2006). Residential Choice from a Lifestyle Perspective. Housing, Theory and Society, 23(2),

19 article Nordic Journal of Urban Studies Fagfellevurdert Research publication RESEARCH PUBLICATION Volume 2, No , p ISSN online: DOI: Why are some families with children leaving the inner city and other staying? Rolf Barlindhaug Senior Researcher, Norwegian Institute for Urban and Regional Research (NIBR), Oslo Metropolitan University Abstract Around 70 percent of those born in the inner city of Oslo move away before reaching school age despite the municipal goal of keeping more of them there, for example by securing a certain share of new construction of larger dwellings. In this study, we investigate to what degree this out-mobility is a question of preference for suburban attributes and to what degree some of those moves are a result of a combination of low income and high house prices. A highly deregulated Norwegian housing market makes it difficult to achieve an acceptable housing standard in centrally located areas of the city. Based on a comprehensive survey and a logistic regression analysis where out-movers are compared with a group of stayers in the inner city, we find that typical movers are attracted by the attributes of suburban living. Stayers prefer short distances to work, desire to live close to friends and appreciate the ability to walk or cycle to different activities. Particularly among movers to Oslo outer east, we find households with lower incomes, lower education and with a relative high share of country background from Africa and Asia. Many of them would have preferred to stay in the inner city if they could have afforded a decent home there. Keywords residential mobility, housing and location preferences, families with children Introduction In some larger cities, an increasing number of children are born in the inner city. A normal pattern is the outmoving of young families with children under school age. This pattern can still be observed, but, simultaneously, a high number of families with children choose to live in the inner city. Of those born in the inner city of Oslo, about 70 percent leave before they reach school age (Stambøl, 2013). Wessel and Bjørnson Lunke (2021) compared two cohorts of first-time parents from 1995 and 2005 and found that the proportion of parents who remain in the inner city of Oslo had decreased and that they more often than before moved to low-rise houses. Generally, suburbs have undergone major changes over the last century. From being home to a small number of wealthy people, they became an attractive location for a growing middle class. Closest to the city centre, a mix of blocks and houses with few dwellings dominated; on the fringe there were contiguous areas of single-family houses (Baldasarre, 1992). In-between these areas, enclaves of older, poorly maintained, dense developments quite near the metro and train stations emerged. These developments easily attract low-income households and immigrants (Hochstenbach & Musterd, 2017; Tzaninis & Boterman, 2018). Couple formation and the birth of a first or second child increase space requirements and are often accompanied by a move to the suburbs (Rossi, 1955; Tzaninis & Boterman, 2018). Those leaving the inner city associate suburbs with being appropriate for bringing up chil- Copyright 2022 Author(s). This is an open access article distributed under the terms of the Creative Commons CC BY-NC 4.0 License ( ).

20 20 ROLF BARLINDHAUG dren, a better quality of life, lower density, reduced noise, a slower pace of life and lack of crime (Gkartzios & Scott, 2010). Suburbs offer more spacious, more affordable, and more owner-occupied housing, and so moving is also a question of price and tenure (Booi et al., 2021). For many families with children living in the inner city, who need more living space, there will often be a trade-off between lower house prices in the suburbs, better possibilities for increasing living space, and longer distances to work, often for two working adults. The concentrated mass development of housing in the 1950s and 1960s was a qualitatively good alternative for the inner-city inhabitants of Oslo who were living in overcrowded homes of a poor standard (Hansen & Brattbakk, 2005). These satellite cities were, for historical reasons, developed in outer-east areas of Oslo (Myhre, 2016). House prices there today partly reflect the distance from the city centre and partly the dense settlement between noise-deferred entrance roads from the rest of Norway, enhanced by a high share of lowincome households and non-western immigrants in owner occupation, who were attracted by the initially low house prices (Barlindhaug, 2016). Oslo has been shown to be a divided city, with the low-priced satellite cities in the outer east having a high share of non-western immigrants. The outer city west, with its high share of low-rise houses and a much higher price level, attracts those who are wealthier (ibid.). In the nearby county of Akershus, the housing stock does not vary so much between the eastern and the western municipalities, but the price level does. The relocation of manufacturing industries from central parts of cities, to both the suburbs and abroad, and a growth in the service sector, has made the inner city more attractive and gentrified (Kabisch et al., 2019). Many of those poorer inhabitants in the inner city, who previously had no opportunities to obtain better housing conditions in the suburbs, have been replaced by medium and high-income households. In Oslo, high-income households move partly into upgraded older developments and partly into newly built brownfield projects (Barlindhaug, 2003; Sæter & Ruud, 2005). Almost 60 percent of the residential construction in Oslo over the last 15 years has taken place in the inner city, especially the innercity east, partly as densification and partly as brownfield development, with strong emphasis on waterfront projects (Barlindhaug, 2016; Barlindhaug & Nordahl 2018). This is the main reason why the population growth has been higher in the inner city than in the outer city of Oslo, in contrast to many big cities in other countries (Tzaninis & Boterman, 2018). The latest master plan for Oslo expresses that facilitating families housing careers in their own urban district, with variation in house types and dwelling size, is important (Oslo Municipality, 2018). One of the instruments has been establishing a norm for the composition of new build dwellings according to size in the inner city. Having a certain share of large dwellings in each new housing project in the inner city does not, by the way, guarantee that families with children will be able to afford to buy them. In some other cities, an explanation for out-mobility of families with children is that there are too few large, owner-occupied dwellings in the inner city (Booi et al., 2021). Many studies of residential mobility focus on recent movers and how their motives for moving vary with life course and socio-economic characteristics. Some of these studies are limited to families with children, especially in an urban context where the choice of living in the city centre or in the suburbs is in focus. In this study, we have the possibility of comparing families with children who have moved from the inner city to the suburbs with families in the same life course who have chosen a more permanent stay in the inner city, based on a large survey of these two groups. Our focus is to illuminate to what degree these two groups have different housing and location preferences and to what extent some of the movers are so-called displaced movers, resulting from not having enough economic resources to reach a

21 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No satisfying housing standard in the central part of the city. In the event that the main explanation is that movers have other housing and location preferences than stayers, it could be difficult to find instruments for keeping a higher share of families with children in the inner city. In the next section, we give an overview of the literature on residential mobility, with a focus on families with children. Then follows a closer description of the characteristics of the Oslo region. The remaining sections outline the data, method used and then the results. The article ends with a conclusion and a discussion. Residential mobility, urbanisation, and re-urbanisation In the literature on mobility, there is a distinction between intra-urban and inter-urban mobility, where the latter is called migration or long-distance moving, often triggered by job-related or human capital reasons (Li & Tu, 2011). Theories of intra-urban mobility are rooted in sociology and geography disciplines, where the focus is on dissatisfaction with the original location triggered by life cycle changes (Li & Tu, 2011). Later, economists tried to model the decision-making process using neo-classical, micro-economic senses of utility maximisation on housing consumption mismatch resulting from an unexpected economic or demographic shock. This tradition also included a wealth effect, the effect of mortgage constraints and risk aspects connected to transactions in the owner-occupied sector (ibid.). Many studies stress the importance of seeing the demand for housing as a joint mobilitytenure decision. Much of the literature on residential mobility departs from individuals going through different life stages (Rossi, 1955). Housing needs change with life stages, and dissatisfaction will trigger moves and adjustments in housing consumption. These adjustments can be related both to housing and neighbourhood characteristics in addition to accessibility. Behind the judgements of existing and desired housing consumption are changes in personal economy, births, and changes in marital status (Clark & Huang, 2003). In addition, there may exist some push and pull factors connected to the existing and future neighbourhoods. The actual move is also influenced by income, wealth, and the housing market structure, especially when changing tenure to owner-occupation (Clarc & Onaka, 1983; Clark & Lisowski, 2017; Gkartzios & Scott, 2010; Booi & Boterman, 2020). Families with children adjust their housing consumption according to the number of children, age of children and economic resources. Location is also a question of housing supply in the form of price, size, and tenure (Booi et al., 2021). There is quite some evidence that people will move back to, or near to, the place they grew up when they want to settle down (Feijten et al., 2008). But it is also the case that those coming to the city for education and work typically stay for quite some time. Having invested in a social network in the city, often established in the period before having children, is a reason for staying in the inner city and making it a permanent living area (Karsten, 2003; 2007). Mulder (2007) sees location as a question of living close to family, but location usually strongly correlates to house prices. For high-income households, there are two forces drawing in opposite directions. Rich households want larger homes and are attracted by lower housing prices in the suburbs. Simultaneously, their opportunity cost of time influences commuting costs. Brueckner et al. (1999) assume that the ratio of commuting cost to housing consumption falls with income, which leads to wealthy households to locate in the suburbs. Preferences for location also vary with socio-economic variables. When a centre has strong amenity advantages over the suburbs, wealthy households are more likely to live in central locations. Amenities in the centre can then pull affluent households towards

22 22 ROLF BARLINDHAUG it. This model is used to explain why high-income residents in US urban areas tend to live in suburbs and that these income groups tend to live more centrally in Europe (Flambard, 2017). Flambard (2017) finds that an increase in income, age, size of household or housing cost to income ratio increases the probability of being an owner of a house in the suburbs. In some of the cities studied, amenities in the city centre make up for a less spacious dwelling and make households, to a larger degree, prefer to locate themselves in the inner city. Cities where most suburban inhabitants drive their car to work on free highways prefer to locate in suburbs. Mulalic and von Ommeren (2017) show that an average household in Denmark who had a doubling of income will reduce the distance from home to workplace from 18 to 16 kilometres. Some families with children continue to stay in the city because population density, good amenities, and public transport, in addition to a tolerant atmosphere, random encounters and small shops, still attract them (Lilius, 2014). Those who choose to stay may also have special perceptions about life in suburbs being boring, with high social control, less tolerance for deviation and less supply of goods and services (Danielsen, 2005). Boterman and Bridge (2015) find that if a household s best friends locate in the inner city, the probability of moving out is reduced. If many families with children decide to move, the children of some of the intended stayers would lose their friends in school, kindergarten and in the neighbourhood. This could also function as a driver for some of these families to move. Often, the most attractive cultural events happen in the inner city, where the level of cultural diversity is also highest. Households with low cultural capital and high economic capital are the ones who most often move out of the inner city, while stayers have high cultural capital and low economic capital (Boterman, 2012; Boterman & Bridge, 2015). Families with children are usually negotiating between what is the best area for bringing up children and the adults preferences concerning commuting to work, access to an established network of family and friends and easy access to different cultural arrangements. Families with children moving out of the inner city can be divided into ex-urbanists, displaced urbanists, and anti-urbanist movers (Mitchell, 2004). Ex-urbanists want to live close to the city and to maintain contact with it through work, cultural and social events, and prefer better and bigger housing within reasonable commuting distance. Anti-urbanists will escape the supposed negative effects of the city, while displaced urbanists do not want to leave the city but feel financially forced to move and leave with a feeling of regret (ibid.). Gkartzios and Scott (2010) studied counter-urbanisation floods in the Greater Dublin area and found that the main motives for moving to rural areas were living in better, larger, and cheaper houses and that these areas were the most appropriate for bringing up children. Rural areas were also associated with a better quality of life, lower density, reduced noise, and there was a slower pace of life and lack of crime. The pull factors dominated in explaining the move, especially the social environment in rural areas but also the physical environment and lower house prices. Bergstrøm et al. (2010) find that households with children most likely move to areas where there are many similar families, partly because that is where they find the largest houses and partly because they want to live with other families with children. Residential location and school choice among families with children is seen as a simultaneous decision, especially in countries like Norway where location determines which public school the children in elementary school belong to (Ely & Teske, 2015). The quality of the school, the distance to it, the mix of pupils and the safety of the route are of importance. In Oslo, a high share of minority pupils in school creates more worry than a high minority share in the neighbourhood (Hewitt, 2013). School quality can be capitalised upon

23 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No in house prices, reducing low-income households access to neighbourhoods with higher quality schools (Machin & Salvanes, 2016). Good school quality can be based on rumours. Grades can say something about educational standards of a school and, perhaps most, who the pupils are. Meanwhile, indicators measuring the contribution to the actual quality of the school are more independent of the mix of pupils, one example being measuring the degree to which average grades improve over a period of time. In contrast to earlier research, Cuddy et al. (2020) find that the freedom to choose both school and residential location could be more imaginary than real and that other attributes are more important in the location decision. The Oslo region The urban districts in Oslo municipality are divided into four areas according to east/west dimension and centrality, as shown in figure 1. Oslo outer city west had around 182,000 inhabitants in 2017 and Oslo outer city east nearly 230,000. The former county of Akershus is split into two parts. The western part consists of the municipalities Asker and Bærum and has nearly 185,000 inhabitants. The remaining municipalities lie in Akershus east, which constitutes about 420,000 inhabitants. Many of the municipalities in Akershus east are further away from Oslo than the two western municipalities. Oslo: Inner city east: Gamle Oslo, Grünerløkka, Sagene Akershus: Inner city west: St. Hans Haugen, Frogner Akershus west: Asker, Bærum (Bygdøy excluded) Akershus east: The rest of Akershus Outer city west: Nordre Aker, Vestre Aker, Ullern, Nordstrand Outer city east: Bjerke, Grorud, Stovner, Alna, Østensjø, Søndre Nordstrand Figure 1 Urban districts in Oslo and municipalities in Akershus

24 24 ROLF BARLINDHAUG After World War II, the population in Oslo increased but was strongly reduced in the inner city as high housing construction in suburbia led many families to move away from the overcrowding and poor housing conditions. During the 1970s and 1980s, the total population in Oslo was diminishing before enjoying strong, continuous growth. Figure 2 shows the development in the child population of the inner city between 2004 and Around 70 percent of the children in the inner city live in inner city east and constitute a higher share of the total population there than children in inner city west: 13 percent and 9 percent respectively. In addition, figure 2 shows the share of each birth cohort that has moved out of the inner city within the first six years, i.e., before school starts. Figure 2 Births, out-migration rates, and child-population in Oslo inner city Source: Statistics Norway/microdata.no own computations, Oslo municipality statistic bank Both age groups exhibited strong growth during the period a result of births and net outmigration. The number of births grew in the first half of the period and have thereafter been stable at slightly over 4,000 per year. The high number of births is mainly a result of a strong increase in young, single people moving to the inner city for education and work who later form couples and have babies (Ogden & Hall, 2000; Booi & Botermann, 2020). Children born in the inner city in 2007 showed the highest out-mobility among the birth cohorts: 81 percent left after 6 years. This share was reduced to 72 percent for the 2013 and 2014 birth cohort. The lowest level was in the urban district of Gamle Oslo and the highest in the urban district of Sagene. In Helsinki, there has also been an increase in the number of children under six years of age, but there it is explained by a reduction in moving out of the city (Lilius, 2014). Compared to the out-migration, the in-migration of children to the inner city of Oslo has been considerably lower. Those of six years of age living in the inner city, and born outside, constituted a little more than 15 percent of those born in the inner city six years earlier (Stambøl, 2013). The age group 0 5 years seems to reach a peak around 2017 in the inner city of Oslo, while the age group 6 15 years is steadily increasing. The number of children aged 0 5 years grew by 58 percent in the period , while the total population in the inner city increased by 51 percent. The age group 6 15 years increased 67 percent in the same period. House price differences between the inner city and the suburbs, as well as travel expenses and time spent on travel between home and work will influence the choice of staying or

25 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No moving. In figure 3, we see the lowest house price level in the eastern part of Akershus and the highest in Oslo inner west. Figure 3 House price level in the Oslo region in Index: Oslo inner east = 100 Source: Data from finn.no from 2013 and Own hedonic price analyses, controlled for house type, size, construction year and sales year. Data and method This study is based on results from a survey undertaken in 2018 among households with children aged 0 10 years who, in the period , were moving from the inner city to Oslo s suburbs or to the surrounding county of Akershus. A group of families who had stayed in inner city for four years or more with children aged up to 14 years in 2018, and who did not have moving plans, were contrasted to the movers. The response rate was 28 percent, based on 2,845 answers. Register data on age, sex, country background and moving year were available for the gross sample. 1 Based on these variables for the gross sample, weights were constructed separately for movers and stayers. Only a few of the questions in the survey were similar for movers and stayers. These questions are used in the logistic regression analysis and shown in the appendix. Other questions from the survey, documented in Barlindhaug et al. (2018), are used to further illuminate the motives behind the decision to move or stay. The questionnaire was partly related to motives and preferences for moving to the new dwelling, the new location, and the environment (pull factors). Another part of the questionnaire was related to motives and preferences for moving away from the former dwelling and location (push factors). Stayers were asked questions about motives and preferences for not havingplanstomoveoutoftheinnercity.intheanalyses,weconstructedexplanatoryvariables representing the preferences of both movers and stayers (see table A1 in the appendix). These variables are based on the importance of pull factors among movers, and for stayers, 1. Country background is constructed based on information about the country of birth for three generations and refers to private, or the mother s, father s or grandparents foreign country of birth. For people born abroad, this is (with few exceptions) their own native land. For people born in Norway, this is their parents country of birth. In cases where the parents have different countries of birth, the mother s country of birth is chosen. Source:

26 26 ROLF BARLINDHAUG these variables are based on questions that express the importance for a future stay in the inner city. The importance of noise is one example; movers were asked about the importance of low noise in the area they moved to, while stayers were asked about the importance of low noise for not having plans to move. An interpretation problem can arise, and the results in the regression analyses must therefore be carefully interpreted. 2 Questions related to attributes that could easily be changed by moving inside the inner city were not used, for example which floor the dwelling is located on and whether or not it has a balcony. Respondents who represented couples were divided into three household income categories with the same number of respondents in each category: those with low, medium, and high household incomes. The same division of single parents according to household income was made. Results A brief description of movers and stayers after moving direction, based on questions from the survey (Barlindhaug et al. 2018), is shown in table 1. The table shows the housing situation before and after the move for four types of movers in addition to the actual housing situation for stayers. We also see that the weighed distribution of movers from the survey is almost the same as the actual distribution of households with children who moved during The mobility data show that two out of three families who moved to Akershus east moved to a municipality that borders Oslo. Of the households with children moving from the inner city to Oslo outer city or Akershus, a lower share moved to Akershus east and a higher share moved in the other three directions compared to all other household types (not shown in the table). 3 Table 1 Housing conditions of movers and stayers, before and after moving Inner city to Oslo outer west Inner city to Oslo outer east Inner city to Akershus west Inner city to Akershus east Stayers without moving plans M 2 floor area before 86 (43) 69 (26) 80 (26) 71 (21) - M 2 floor area after 146 (66) 104 (46) 171 (71) 148 (59) 95 (43) Share of low-rise houses, % Before After Owner-occupiers % Before After N= Movers only: Sum Weighted distribution (survey) % Household moved from inner city %. * (standard deviation) * All households with children who moved from inner city to Oslo outer city and Akershus during Source: Statistic Norway, Microdata.no 2. The whole questionnaire in Norwegian can be provided by contacting the author 3. Source: microdata.no

27 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Movers to the suburban areas make a considerable square metre increase to their living space. In particular, those moving to Akershus moved to large dwellings. The survey showed that 70 percent of the movers indicated that the lack of a private garden or outdoor space was quite or very important for the moving decision and 55 percent cited play areas for children. Two out of ten reported that it was quite or very important for the moving decision that other families with children had moved (Barlindhaug et al., 2018). Most of the movers were owner-occupiers both before and after the move, except for movers to Oslo outer east, where the home-ownership rate rose by 25 percentage points (up to 83 percent), close to the rate of stayers. For some households, high central house prices only make home ownership possible if moving in the cheaper easterly direction. Stayers had dwellings that were around 20 square metres larger than the dwellings left by the movers. One explanation of this difference is that movers probably needed a larger dwelling and had to decide whether to stay in the inner city or move out. From the survey, we find that 69 percent of the movers did not want to stay in the inner city because they thought it was quite or very important to move to a single-family house or a house with few dwellings. One third moved because it was quite or very important not to live in the inner city with children in school age. Half of the movers, many of them moving east, reported that economic resources or high house prices were quite or very important for not staying in the inner city (Barlindhaug et al., 2018). We also find that nearly 50 percent of the movers would have stayed if the central prices had been lower a sort of spatial displacement according to Hamnett (2009). In table 2 we present the frequencies on the independent variables in the logistic regression. The share of single parent households is relatively high among stayers. Movers, more than stayers, report that a safe living environment and play areas for children, low noise, and the opportunity to use a car for various activities are important. Stayers, more than movers, report that a stable school class environment, living close to friends and the opportunity to walk to different activities are important. Due to the sample procedure of movers and stayers, where stayers are supposed to have lived in the inner city with children for at least four years, stayers are somewhat older than movers. In the following logistic regression analysis, only socio-economic factors such as age, household type, country background, education, household income and where the respondent grew up are used as independent variables in model 1. In model 2, we introduce preference variables that express the respondents attitudes to (a) the residential environment for children, including school; (b) noise and pollution; (c) the importance of living close to family or friends; (d) the importance of the social environment and who the neighbours are and (e) the importance of different commuting alternatives to daily activities. In four additional models, we do separate analyses for movers in the easterly and westerly directions anddividethemintomoverstotheoslooutercityandmoverstoakershus(east:models 3a and 3b, and west: models 4a and 4b). The group of stayers is the same in all models. Multicollinearity between independent variables was checked using the VIF-procedure in SAS. The model is tested for the following hypotheses: car use is more important for couples than singles, and school variables are most important for high income households, without finding significant results. Results from the logistic regression analysis are presented in table 3. Model 1 shows that having low income slightly reduces the probability of moving compared to those with middle incomes. Families with country background from Africa or Asia move out of the inner city more often than others, while single parents stay to larger extent than couples. Time spent on travelling to work and taking children to school or kindergarten is an explanation of why single parents may be stayers (Booi & Boterman, 2020). Moving

28 out of the inner city decreases with age. Model 2 controls for a range of preference variables and shows the same or stronger explanatory power on the socio-demographic variables than model 1. As expected, movers place emphasis on more space, safe environment for children and absence of noise. They are family orientated and engaged in the opportunity of parking their car where they live. For stayers, it is important to live close to friends, and they place more emphasis on who the neighbours are and the opportunity of walking to different activities. Table 2 Descriptive statistics on movers and stayers, after moving direction (shares) Before eventually moving live in Oslo inner east (ref. west) Inner city to outer west Inner city to outer east Inner city to Akershus west Inner city to Akershus east Stayers without moving plans Low household income High household income Age in years (mean) 38 (5.5) 38 (6.5) 37 (4.6) 37 (5.3) 43 (6.7) Country background from Africa/Asia Single parent High education (university/university college for more than 4 years) Grown up in outer Oslo or Akershus One bedroom for each child very important A safe living environment for children very important Safe play areas adjacent to the dwelling very important Safe way to and from school very important Stable school class environment very important Prop. of pupils in school who are native speakers of Norwegian very imp Low noise very important Low pollution very important Closeness to the immediate family very important Closeness to friends very important Conditions of the social environment very important Who lives in the surrounding area/neighbours very important Opportunities to use a car for various activities very important Good parking facilities for cars very important Opportunities to use public transport for various activities very imp Opportunities to use bicycles for various activities very important Opportunities to park a bike where you live very important Opportunities to walk to different activities very important N= (Standard deviation) 28 ROLF BARLINDHAUG

29 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Parents are aware of traffic in the inner city and try to find safe solutions for taking children to and from school. Stayers are asked if a safe way to school is an important reason for not moving from the inner city, while movers were asked if this was a reason for moving. A positive sign on this variable in the analyses may be the result of stayers more often living in areas of the inner city where the way to school is safe, while many movers did not. 4 Another explanation is that movers often leave the inner city before school start and have not yet reflected on the topic. In the next four models, we investigate how the preference variables vary with moving direction. The socio-economic composition of movers, reflected in income and country background, vary between moving directions. A comparison of movers in easterly directions and all stayers in the inner city shows that high income clearly reduces the probability of moving out of the inner city in this direction. Among movers in westerly directions, we find that low income strongly decreases the probability of moving, especially to Oslo outer west. This pattern also reflects the structure of the existing housing market, where the western side has considerably higher house prices and a higher share of single-family houses compared to the eastern side. The importance of preference variables related to raising children are nearly the same when comparing movers to the east and movers to the west, but the strength of some of them vary between eastern and western movers. Among the variables increasing the probabilities of moving rather than staying, movers in the westerly direction put more weight on the importance of safe residential areas and play areas for children in addition to the importance of living close to family and the opportunity of walking to different activities. For movers in the easterly direction, the importance of one bedroom for each child strongly increases the probability of moving from the inner city. Comparing movers to Oslo outer east and Akershus east, we more often find that lowincome households from Africa or Asia locate in Oslo outer east. Movers in this direction are more often single parents, and they find school aspects and the use of a car for different activities important. Based both on the regression analyses and findings in Barlindhaug et al. (2018), movers in the easterly direction can be separated into two main groups. In the first group we find those who have strong preferences for small houses with private gardens and move far enough from the inner city to implement those preferences at an acceptable price. Movers in the other group report that they first looked for a larger dwelling in the inner city but that high house prices and their economic resources led to a more reluctant move to the outer eastern part of Oslo. There, they found a larger dwelling, but the desire to stay close to the city centre made them continue to live in flats. They expressed that this choice was a more preliminary solution than was reported among other movers. These two groups are quite similar to what Mitchell (2004) calls ex-urbanists and displaced urbanists. High incomes among westerly movers put them in a position where they, more than others, have the opportunity of choosing to continue living in larger central flats or to move out of the city. They also reported that their chosen housing alternative was a more permanent housing choice in a situation with children (Barlindhaug et al., 2018). 4. In the survey, households in the inner city can be distributed across around 30 sub-districts. We find that outmobility among families with children varies between both urban districts and the sub-districts inside each urban district.

30 30 ROLF BARLINDHAUG Table 3 Logistic regression analyses of the probability to move out of the inner city with children, in contrast to staying * p < 0.05, ** p < 0.01, *** p < Model 1 Model 2 Model 3a Oslo outer east Model 3b Akershus east Model 4a Oslo outer west Model 4b Akershus west B Sig B Sig B Sig B Sig B Sig B Sig Intercept Before evt. moving live in Oslo inner east (ref. west) * *** Low income (ref. middle income) * ** *** *** High income (ref. middle income) * *** * Age, years *** *** *** *** *** *** Country background from Africa/Asia (ref. others) ** *** *** Single parent (ref. couples) *** *** * *** *** *** High education Grown up in outer Oslo or Akershus (ref. other places) One bedroom for each child very important *** *** *** * * A safe living environment for children very important *** *** *** * Safe play areas adjacent to the dwelling very important ** ** Safe way to and from school very important * ** * Stable school class environment very important Prop. of pupils in school, native speakers of No. v.imp *** *** *** Low noise very important * ** * ** Low pollution very important Closeness to the immediate family very important * Closeness to friends very important *** *** *** *** *** Conditions of the social environment very important ** ** Who lives in the surrounding area/neighbours v. imp ** * ** * Opportunit. to use a car for various activities very imp ** Good parking facilities for cars very important *** *** *** ** Opport. to use pub. transp. for various activities v. imp ** ** ** Opport. to use bicycles for various activities very imp Opportunit. to park a bike where you live very imp ** Opportunit. to walk to different activities very imp *** *** *** *** ** N used = Nagelkerke R

31 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Discussion and conclusion Families who move from the inner city increase their housing consumption in square meters and frequently settle into detached, semi-detached, or terraced houses. Living space increases in relation to the distance from the city centre. Compared to stayers, a typical mover locates in a low-rise house and is of the opinion that one bedroom for each child, a safe residential environment for children and safe play areas close to the dwelling are important. Stayers differ from movers by putting emphasis on the social environment, living close to friends, and having the opportunity to walk to different activities. Oslo is a divided city with an affluent western side. Also, the two municipalities in the western part of Akershus have higher income levels and house prices than the rest of Akershus (Barlindhaug, 2016). When splitting movers from the inner city in two moving directions, those who moved in westerly directions have higher incomes and higher housing consumption in square meters, and most of them belong to the majority population. In the moving direction from the inner city to the outer city east, the share of owner-occupation rose significantly when moving. Many of those movers had low incomes, a country background from Africa or Asia and bought a home in a location where they could afford to buy. Income plays separate roles in easterly and westerly moving directions when comparing movers with stayers. High incomes increase the probability of moving in a westerly direction compared with staying. Households with high incomes are looking for locations in affluent areas (Galster & Magnusson Turner, 2017). Having low incomes means that the probability of moving in an easterly direction increases. For many, this means giving up their desire to continue living in the more expensive inner city housing market in a more spacious dwelling that is suitable for their needs. These families could be characterised as displaced urbanists (Mitchell, 2004). Single parents seem to belong to this group, but many of them have chosen not to move out of the inner city and have located in a residence they can afford. The results from this study show that families with children in urban areas, belonging to the same life course, do not have homogenous housing and location preferences. For different reasons (shown above), some seem to prefer to live in the inner city and others in the suburbs or in the neighbouring county. Long distance movers prefer large living spaces in low-rise houses with private outdoor areas. Such housing conditions are most widespread outside the inner city and can be provided at much lower prices than similar houses that are more centrally located. They also report that these locations are most suitable for bringing up children and place the advantages of their location ahead of the disadvantages connected to the daily commute to and from work. This is in line with earlier research based on mapping families motives for moving (Gkartzios & Scott, 2010). Many wealthy families, with the opportunity of choosing location, prefer attributes connected to suburban living. Low-rise housing and private outdoor areas are especially hard to achieve in more central locations. Both stayers in the inner city and movers strongly care for their children s playing opportunities and their school environment and think that short distances to different activities both for children and adults, in addition to independence of daily car use, result in a meaningful life. Many families with children are leaving the inner city before school age, but some choose to stay. In line with Kartsten (2007) and Boterman and Bridge (2015), we find that having invested in a network of friends in the inner city strongly reduces the probability of leaving. The advantage of these networks of friends is strengthened by short distances to different activities, both for children and adults, and being independent of daily car use. But since so many families actually leave the inner

32 32 ROLF BARLINDHAUG city, both children and adults who stay often lose their close friends (Karsten, 2007; Boterman, 2012). For the local authority of Oslo there is a goal to reduce the out-mobility of families with children from the inner city. Expensive compact city developments, sometimes at the expense of private and public green spaces, with norms for dwelling sizes to secure a high share of large flats in new developments, do not keep many low-income families with urban preferences in the inner city. New housing projects in the inner city of Oslo are also criticised for being inflexible during varying childhood needs and for not having enough private outdoor areas reserved for the inhabitants. Some question the consequences of further densification on the growing-up environment and suggest that parks cannot compensate for missing local green spaces (Dale Nordbakke, 2018; Barlindhaug et al., 2018). Traffic in the inner city, noise, pollution, and a general lack of safety connected to crime and drugs will still be important push factors for moving, despite a possible improved housing policy for keeping more families with children in the inner city. Owner-occupation is a politically stimulated and economically preferable tenure form in Norway. Regulating a part of this market to provide more affordable houses for low- and middle-income groups is on the political agenda and could be a possible instrument for keeping more families with children in the inner city. There is, though, limited space for new construction in the inner city, especially if developments are to be child friendly. A question for future research would be to illuminate the consequences of keeping more of the outmigrating families in the inner city. Should it be at the expense of young single people who strongly prefer a central location? If these really are conflicting interests, a greater acceptance of the high outmigration today is a pattern that could be seen as a part of families housing careers, as it always has been. A focus on more effective transport solutions from satellite cities into central Oslo would be an alternative solution instead of regulating much of the housing supply in inner city, thus avoiding many problems connected to the access to a scarce asset. Another topic for future research could be to focus on possible differences in preferences for location among young children and adults in the same household. Funding information The work for this article is funded by the Research Council of Norway, Acknowledgements The author wishes to thank the three anonymous reviewers for their useful feedback on an earlier draft of this work.

33 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Appendix: Questions in the survey used in the logistic regression analyses In construction of the following dummy variables in the logistic regression, questions 25, 26, 28 and 29 to the movers and questions 39, 40, 41 and 42 to the stayers without moving plans were combined. Also included is question 38, which defines stayers without moving plans. Table A1 Combination of questions from the questionnaire for construction of dummyvariables in the regression Dummy-variables Movers Stayers One bedroom for each child very important A safe living environment for children very important Safe play areas adjacent to the dwelling very important Safe way to and from school very important Stable school class environment very important Proportion. of pupils in school who are native speakers of Norwegian very imp Low noise very important Low pollution very important Closeness to the immediate family very important Closeness to friends very important Conditions of the social environment very important Who lives in the surrounding area/neighbours very important Opportunities to use a car for various activities very important Good parking facilities for cars very important Opportunities to use public transport for various activities very important Opportunities to use bicycles for various activities very important Opportunities to park a bike where you live very important Opportunities to walk to different activities very important The respondents were asked to give the following questions/lines one of the alternatives: Very important Quite important Rather important or unimportant Quite unimportant Very unimportant Do not know In construction of dummy variables for the regression analyses, a division between very important and other categories was done.

34 34 ROLF BARLINDHAUG Movers Q 25 What was important for your household when it came to the dwelling and residential area you moved to? Family 1 Closeness to the immediate family (children, siblings or parents you do not live with) 2 Closeness to friends Movers Q 26 What was important for your household when it came to the dwelling and residential area you moved to? Living environment and leisure 1 A safe living environment for children 2 Safe way to and from school 3 Play possibilities for children 4 Low noise 5 Low pollution 6 Conditions of the social environment 7 Who lives in the surrounding area/neighbours 8 Opportunities to park a car where you live 9 Opportunities to park a bike where you live 10 Opportunities to use a car for various activities 11 Opportunities to use public transport for various activities 12 Opportunities to use bicycles for various activities 13 Opportunities to walk to different activities Movers Q 28 What was important for your household when it came to the dwelling and residential area you moved to? School and kindergarten 1 Stable class environment 2 Proportion of pupils in school who are native speakers in Norwegian 3 Getting to the desired/desirable school district

35 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Movers Q 29 What was important for your household when it came to the dwelling you moved to? 1 Price per square metre 2 The dwelling is not overlooked (no opposite neighbour) 3 The dwelling has a view 4 The dwelling has a private balcony/terrace 5 The dwelling has its own private outdoor area 6 The dwelling has a common outdoor area with neighbours 7 The dwelling has two bathrooms/wc 8 The dwelling has its own bedroom for each child 9 The dwelling has a large enough total area 10 The dwelling is located on the lowest floors 11 The dwelling is located on the upper floors 12 The dwelling has good physical standard 13 The dwelling has an elevator 14 The dwelling has an entrance on the ground floor 15 Safe play areas adjacent to the dwelling Stayers: Those who have confirmed that they have lived in the inner city with children for at least four years All stayers Q 38 Are you planning on moving out of the inner city? 1 Has clear relocation plans to move from inner city 2 Expect to move from inner city within 3 years 3 Expect to live in this dwelling or another dwelling in the inner city for at least 3 more years 4 Has no plans to move from inner city Those who answered 3 or 4 are stayers without moving plans in the analysis Stayers Q 39 How important or unimportant are the following for your household having no plans to move from the inner city in the next few years? Conditions related to economy, travel to work and family 1 We do not have sufficient equity to move to another/desired dwelling 2 Short journey/time to current job 3 Opportunities to travel by public transport to work 4 Opportunities to cycle to work 5 Opportunities to walk to work 6 Closeness to the immediate family (children, siblings or parents you do not live with) 7 Closeness to friends 8 Good parking facilities for cars 9 Good parking facilities for bicycles

36 36 ROLF BARLINDHAUG Stayers Q 40 How important or unimportant are the following for your household having no plans to move from the inner city in the next few years? Conditions related to living environment and leisure 1 A safe living environment for children 2 Safe way to and from school 3 Good school environment 4 Play possibilities for children 5 Low noise 6 Low pollution 7 Conditions of the social environment 8 Who lives in the surrounding area/neighbours 11 Opportunities to use a car for various activities 12 Opportunities to use public transport for various activities 13 Opportunities to use bicycles for various activities 14 Opportunities to walk to different activities Stayers Q 41 How important or unimportant are the following for your household having no plans to move from the inner city in the next few years? Conditions related to walking distances to different chores and school/kindergarten 1 Walking distance to public transport 2 Walking distance to school 3 Walking distance to kindergarten 4 Walking distance to shops/shopping centres 5 Walking distance to cafes and restaurants 6 Walking distance to cultural life (museum, theatre, cinema etc.) 7 Walking distance to nature and the environment (field, sea, etc.) 8 Walking distance to park 9 Cycling distance to daily chores 10 Stable class environment 11 Proportion of pupils in school who are native speakers of Norwegian

37 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Stayers Q 42 How important or unimportant are the following for your household having no plans to move from the inner city in the next few years? Conditions related to the dwelling. 1 The dwelling has a view 2 The dwelling has its own private outdoor area 3 The dwelling has a common outdoor area with neighbours 4 The dwelling has a balcony/terrace 5 The dwelling has a closed kitchen 6 The dwelling has two bathrooms/wc 8 The dwelling has its own bedroom for each child 10 The dwelling is located on the lowest floors 11 The dwelling is located on the upper floors 12 The dwelling has good physical standard 13 The dwelling has an elevator 14 The dwelling has an entrance on the ground floor 15 Safe play areas adjacent to the dwelling References Baldassare, M. (1992). Suburban communities. Annual Review of Sociology 18, Barlindhaug, R. & Nordahl, B. (2018). Developers price setting behaviour in urban residential redevelopment projects. JournalofEuropean RealEstateResearch, Vol. 11 Issue: 1, Barlindhaug, R., Langset, B., Nygaard, M. & Ekne-Rud, M. (2018). Bo- og flyttemotiver blant barnefamilier i indre Oslo. [Moving and staying motives of young Oslo families with children]. NIBR-rapport 2018:14 (Oslo: NIBR). Barlindhaug, R. (2016). Boligmarked og flytting betydning for segregasjon. In Ljunggren, J. (ed.) Oslo ulikhetenes by. Cappelen Damm Akademisk Barlindhaug, R. (2003). Eldres boligkarriere og formuesforvaltning. Prosjektrapport 350. Norges byggforskningsinstitutt. Bergström, L., van Ham, M. & Manley, D. (2010). Neighbourhood Choice and Neighbourhood Reproduction. Working paper. Institute for the Study of Labor, IZA DP nr /a43453 Booi, H. & Boterman, W.R (2020). Changing patterns in residential preferences for urban or suburban living of city dwellers. Journal of Housing and the Built Environment, 35: Booi, H., Boterman, W.R. & Musterd, S. (2021). Staying in the city or moving to the suburbs? Unravelling the moving behaviour of young families in the four big cities in the Netherlands. Popul Space Place. 27:e Boterman, W. R. (2012). Residential mobility of urban middle classes in the field of parenthood. Environment and Planning A, 44, , Boterman, W. R. & Bridge, G. (2015). Gender, class and space in the field of parenthood: comparing middle-class fractions in Amsterdam and London. Transactions of the Institute of British Geographers, 40 (2),

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40 article Nordic Journal of Urban Studies Fagfellevurdert Research publication RESEARCH PUBLICATION Volume 2, No , p ISSN online: DOI: Swedish conditions? Characteristics of locations the Swedish Police label as vulnerable Manne Gerell Associate Professor, Department of Criminology, Malmö University (Corresponding author) Mia Puur MSc (Master of Science in Criminology), Department of Criminology, Malmö University Nicklas Guldåker Associate Professor, Department of Human Geography, Lund University Abstract Deprived neighborhoods in Sweden in which criminal networks have a negative impact on local residents are labeled as vulnerable neighborhoods by the police. The method used by the police to classify such neighborhoods is largely based on perceptions, which raises issues of subjectivity and potential biases. The present study explores the characteristics of such neighborhoods based on registry data on socio-demographics and crime. The study employs data in the form of a grid of 250 x 250 meter vector grids (N=116,660) with data on population, foreign background, employment, age characteristics, household type, and eight types of crime. Generalized mixed-effects models of vector grids nested in municipalities were fitted to analyze the characteristics of vector grids classified as vulnerable (N=1678). Several variables are significantly associated with a vector grid being classified as vulnerable, with the proportion of the population that is foreign born, and the proportion with foreign-born parents, being the strongest predictors. In addition, we consider whether there are systematic differences between municipalities and develop a model based on regression coefficients to predict whether a vector grid is vulnerable. The model reclassifies 39.8 percent of the vector grids, identifying locations that statistically resemble vulnerable neighborhoods but are not classified as such, and vice versa. Keywords vulnerable neighborhood; Swedish conditions; deprived neighborhood; crime; policing Introduction Over the past few years, there has been political debate in Sweden and the neighboring Nordic countries about the problems that Sweden is experiencing with gang violence and vulnerable neighborhoods (Johansson, 2018). In Norway and Denmark, these problems, which are often associated with migration and/or integration, are labeled svenske tilstander (Swedish conditions), and it is stated in the political debate that these two countries need to avoid ending up in a situation similar to that in Sweden (Lokland & Nilsson, 2018; Johansson, 2018; Lie, 2019). This debate has primarily been of a qualitative nature with a focus Copyright 2022 Author(s). This is an open access article distributed under the terms of the Creative Commons CC BY-NC 4.0 License ( ).

41 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No on vulnerable conditions in these neighborhoods, resembling how a ghetto is a racialized problem area, while skid row is its less racialized and less criminogenic counterpart (Huey & Kemple, 2007; Lynch et al., 2013). Meanwhile, very few quantitative studies have directed a central focus towards the characteristics of these vulnerable neighborhoods (Puur, 2020). A significant question in this context is whether it is possible to statistically determine if these areas differ from their surrounding environments and, if so, how? Vulnerable is the label applied by the Swedish Police Authority to deprived neighborhoods in which criminals have a major impact on the local community (Polisen, 2017; Gerell et al., 2020). There are currently 60 areas that the Swedish Police have identified and labeled in this way, the majority being located in and around major cities (Polisen, 2019). This type of neighborhood tends to be characterized by higher levels of fear among residents than other residential areas and higher levels of crime (Brå, 2018), but the differences are generally not so pronounced (Gerell et al., 2020). For some types of crime, however, there are larger differences, and this coincides with particular types of crime that have received a great deal of media attention since There has been an increase in gun violence in Sweden (Sturup et al., 2019), which is, to a large extent, linked to vulnerable neighborhoods (Brå, 2015; Gerell et al., 2021). Vulnerable neighborhoods in Stockholm and Malmö, for instance, experience around 4 5 times as many shootings per capita as other parts of these cities (Gerell et al., 2020). Detonations of hand grenades follow a similar geographical pattern, although these also take place in city centers (Sturup et al., 2020). Similarly, torched cars have been linked to deprived neighborhoods (Gerell, 2017a; Malmberg et al., 2013), as have open drug markets (Magnusson, 2020; Gerell et al., 2021). It seems clear that there are problems with crime and fear among the residents of vulnerable neighborhoods and that these problems feed into political discourses in the Scandinavian countries on vulnerable neighborhoods, immigration and Swedish conditions. To some extent, prejudice, and even racism, is likely to be a contributing factor to the problems in these neighborhoods. In the US, the policing of drugs is much heavier in black communities than in white ones, and it has been argued that this is driven in part by race and in part by economic interests (Lynch et al., 2013). The policing of minorities has given rise to the Black Lives Matter movement, which highlights how police violence disproportionally affects minority communities (Dillon & Sze, 2016). Similar processes exist in Europe, and in the Nordic countries it has been argued that the policing of minorities, and in particular police stops and controls, can increase feelings of exclusion. Young men with a minority background who are stopped a lot by the police perceive this as a marker of their unbelonging, reinforcing feelings of being excluded from the majority society (Solhjell et al., 2019). Such processes can serve to increase patterns of segregation and exclusion and in part contribute to the problems some neighborhoods suffer. At the same time, however, the method employed by the police to determine which neighborhoods are labeled as vulnerable is largely based on subjective police perceptions (Puur et al., 2019). Whether or not an area is labeled as vulnerable depends on whether the local police perceive there to be criminal networks in the neighborhood who have a large impact on the local community (Polisen, 2017). There may be many biases that affect whether a neighborhood is classified as vulnerable. The amount and quality of police intelligence work may differ across police districts, and problems the police know little about will not be included. Similarly, there are large differences across police districts throughout Sweden in the level and type of problems they experience. This may affect how the police in different areas rate the problems found in a neighborhood that may be assessed as vulnerable. In this paper, we will attempt to quantify the underlying and distinctive characteristics of

42 42 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER neighborhoods that are labeled as vulnerable without using any police intelligence reports or subjective measurements. Our work is partly based on previous work (Puur, 2020) that used demographic and socio-economic data to analyze the characteristics of such neighborhoods. We use registry data on unemployment, age groups, household composition, foreign background, and police-reported crime to outline the characteristics of vulnerable neighborhoods. Thus, the main aim is to see which types of variables have the strongest impact on whether a location is classified as vulnerable. Secondary aims are to analyze whether there are differences between municipalities, and whether, or how, our statistical analysis identifies places as vulnerable that have not been identified as such by the police. Our findings indicate that neighborhood grids classified as vulnerable are characterized by measurable and systematic differences compared to other grids across Sweden. These quantifiable differences emerge in different ways. For example, vulnerable neighborhood grids have a higher proportion of residents of foreign background, of young residents, larger households, larger populations, and also more gun violence and more (police-reported) narcotics crime. However, the findings also show that vulnerable neighborhood grids are characterized by lower levels of other types of crime, such as bicycle thefts and cases of assaults on public officials. A somewhat surprising result is that the statistical relationship between vulnerability and high levels of unemployment is rather weak. A strong statistical relationship between vulnerable neighborhood grids and the variables focused on foreign background raises several questions. It could be that the type of criminal organizing that lends itself to violence involving weapons, such as shootings, is more common among immigrant groups. A contributing factor could be that some young people, many of whom are immigrants with insecure home conditions, are more exposed to active recruitment into criminal networks than other young people with more stable living conditions (Polisen, 2017). It is also possible that some form of prejudice or similar plays a role, with criminal networks in neighborhoods characterized by having large proportions of residents of foreign background receiving more attention from the police, e.g., in the form of more regular routine checks (Kardell, 2006; Schclarek Mulinari, 2017). The present paper cannot answer such questions, but by analyzing systematic differences between vulnerable and non-vulnerable locations, we provide a starting point for further analysis. Vulnerable neighborhoods, crime and scientific approaches? The phenomenon of vulnerable neighborhoods, including their characteristics and crime, has been indirectly addressed in several scientific approaches. The relationship between deprived neighborhoods and crime may, for example, be explained by social disorganization, which is the theoretical idea that socially and economically disadvantaged neighborhoods are less capable of coordinating themselves to attain common goals and to monitor deviant behaviors (Bursik & Grasmick, 1993; Sampson et al., 1997; Sutherland et al., 2013). Factors such as low socio-economic status, residential instability, population heterogeneity, structural density, family disruption, and urbanization play a crucial part in generating environments that are more favorable to crime, since the ability of residents to practice social control in the neighborhood either fosters criminal behavior or diminishes it (Bruinsma et al., 2013; Bursik & Grasmick, 1993; Sampson, 2006; Sampson & Groves, 1989). Further, Sampson and colleagues (1997) have argued that these neighborhoods often lack collective efficacy, which means that residents show little trust in one another and are unwilling to intervene for the common good of the neighborhood (Sampson, 2006). Both ethnic heterogeneity and residential mobility make social organization among residents difficult, the former because

43 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No there might be communication barriers that inhibit efficient social coordination and the latter because social networks are repeatedly disrupted (Sampson et al., 1997). Social structures can also exert a certain pressure on some individuals in society to engage in various types of behavior, including criminal behavior. Merton s theory of strain describes life goals as being culturally defined in a given society and as being shared via common societal values, whereas the opportunities for achieving these goals may vary substantially and may depend on where you come from or where you live (Agnew, 2011). Therefore, living in socially and economically disadvantaged neighborhoods creates a discrepancy between the goals set by society and individuals means or opportunities to achieve them, which can lead to some individuals pursuing alternative and sometimes deviant methods. Strain thus increases the risk of deviant behavior among individuals with limited opportunities. The perception of some members of society as being deviant is also important for explaining criminal behavior (Becker, 1963), with juveniles from socially and economically deprived areas, for example, being at higher risk of being publicly labeled (Farrington, 1977). These are instances where juveniles are labeled as potential criminals, not because of their own deviant behavior but rather because of reputations based on the socio-economic conditions of their families and because they live in socially deprived areas (McAra & McVie, 2012). Equally, juveniles from certain socio-economic groups are more likely to be identified as suspects due to a higher police presence in certain neighborhoods (Sampson, 1986). In Sweden, Wästerfors and Burcar Alm (2020) found that young people from ethnic minorities who live in socially disadvantaged neighborhoods experience police encounters both more often and more negatively. This might be due to more active crime-control strategies in certain neighborhoods. There have been reports of individuals from minority groups being stopped by police several times a month during so-called routine checks (Schclarek Mulinari, 2017). The disproportionate monitoring and surveillance of specific groups and places, actions based on casual suspicion, and unexplained stops tend to weaken the legitimacy of the police. It can also weaken feelings of belonging in society among men of minority backgrounds who bear the brunt of being stopped (Solhjell et al., 2019). Geographical analysis of deprived neighborhoods and crime There have been prior attempts to examine vulnerable neighborhoods using registry data, but with the exception of Puur (2019), this has not been done directly in relation to the police definition of vulnerable neighborhoods. The National Council for Crime Prevention in Sweden used median income, social assistance, and the proportion of the population aged to classify neighborhoods as socially vulnerable. They found that the most vulnerable 10 percent of neighborhoods had more crime and fear of crime but had, in general, not experienced worse trends over time than other urban areas (Brå, 2018). In relation to deprived neighborhoods, structural characteristics are usually measured using units such as census tracts. A key concept that is often employed is that of concentrated disadvantage, which represents a compound measure of demographic or economic factors that together generate a concentration of disadvantage (Sampson et al., 1997; Gerell & Kronkvist, 2017; Jones & Pridemore, 2018). Measures of concentrated disadvantage typically include variables such as unemployment, education and/or social assistance recipiency as indicators of socio-economic status, in addition to sometimes including variables such as race, single-parent households, and similar (Sampson et al., 1997; Gerell & Kronkvist, 2017). Jones and Pridemore (2018) have found that concentrated disadvantage at the neighborhood level (measured using census tracts) contributes to crime on street segments and

44 44 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER ought, therefore, to be considered alongside micro-level crime measures. A similar finding was noted by Gerell (2018), who found that crime at bus stops was influenced both by place-based characteristics, such as whether there was a bar nearby, and by the characteristics of the surrounding neighborhood, such as concentrated disadvantage or collective efficacy. Also, Kim (2018) used street segments to analyze the relationship between structural characteristics and crime using a similar concentrated disadvantage index, which included poverty, single-parent households, average household income and school degree, and which was calculated using census data at the block level and was then subsequently linked to street segments. The Swedish Police specification of vulnerable neighborhoods The first report on vulnerable neighborhoods was presented by the Swedish Police in 2014 and identified 63 such areas in Sweden (Polisen, 2014). 1 The means employed to identify these areas were fairly crude and based on an unstructured method involving interviews with local police officers. This was followed by a second report in 2015, which listed 53 neighborhoods (Polisen, 2015). A third report was released in 2017 in which the methods used to analyze neighborhoods had been given a broader theoretical basis and had become more standardized (Polisen, 2017). The most recent report to date was presented in 2019 and listed 60 vulnerable neighborhoods (Polisen, 2019). The current process employed by the police to determine whether a neighborhood is to be classified as vulnerable begins by posing a few basic questions to the local police districts regarding criminal organization and how criminality affects the local community in their district. For each question, the police districts can choose between four grades of severity in their responses. If all questions are graded with one of the two lowest grades of severity, the process ends. These districts are not considered to contain a vulnerable neighborhood (Gerell et al., 2020). If at least one item is listed as having a more grave situation, the local police district is asked to provide a substantial amount of follow-up information. They are asked to note the location of the problem on a map of the district, with some districts having multiple neighborhoods of this kind. For each neighborhood that they identify, they are asked to complete a survey containing 80 questions on crime, extremism, and criminal organization in the neighborhood (Litbo et al., 2019). The police district is then asked to list the individuals who are causing the problems, e.g., the criminal network of interest, dividing them into four broad categories labeled A D. The A-category comprises those who generate criminal opportunities via connections, companies, or other assets. The B-category are the doers: those who are selling drugs, shooting people and similar. The C-category comprises the youths associated with the network, who are of importance since they generate disorder in the neighborhood and are at risk of being drawn into more severe criminality. Finally, the D-category are the children who are at risk of being exploited. The police rarely have good knowledge of the D-category and obtain a proxy for the size of this group by requesting statistics from Statistics Sweden on the number of children in the neighborhood in each cohort that have failed in school (Gerell et al., 2020). 1. Due to a typo in the report, which stated that there were 55 neighborhoods, references to the number 55 persist in some discussions on the topic, e.g., 55 no-go areas in Sweden in international media.

45 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Finally, the district is asked to mark on a map whether there are any locations where drugs are sold openly or where the police have trouble working, for instance, locations where they cannot park a police car without a risk of it being vandalized. The district then completes an area document in which the problems in the neighborhood are summarized in text format. The material is sent to the National Operations Department, where analysts go through the material. In order to be classified as vulnerable, a listed area should be a neighborhood, 2 and this neighborhood should be mostly residential and deprived. At this stage, locations that are not deemed to be neighborhoods, that are not deprived or that are in the city center (and thus not mostly residential) will be discarded from the analysis. For the remaining neighborhoods, analysts try to establish whether the degree of criminal impact on the local community is high enough to warrant the neighborhood being classified as vulnerable. Evidently, this process involves multiple steps in which subjective perceptions can, and will, have an impact on the outcome. Police districts differ in many ways, and the individual police officers tasked with conducting the analysis differ even more. There may be differences in the level of knowledge/intelligence about neighborhoods, in how this is rated, in how it is described, and in how the descriptions are understood and evaluated by the National Operations Department of the Swedish Police (Nationella operativa avdelningen (Noa)). Research design and data Research questions In this section we present our research questions, outline the demographic data and models employed, and describe our deliberations regarding geographical units of analysis and crime data. The research questions we aim to answer in this paper are: 1. What are the characteristics of vector grids in vulnerable neighborhoods? 2. What differences exist between municipalities in how vector grids are classified as vulnerable? 3. What differences can be identified between vector grids classified as vulnerable by the police and a classification based on statistical analysis of demographic and crime-related variables? From a statistical and methodological perspective, the first question will be addressed by applying generalized linear mixed effects models with a logit link. The second question will be investigated by fitting a single-level logit link regression using dummy variables for the municipalities that have a vulnerable neighborhood and by providing examples of neighborhoods that our models suggest could be classified differently from the way they have been classified by the police. In this context, and with regard to the third research question, it should be noted that the police do have a substantial amount of well-developed intelligence 2. Some police districts report entire cities as struggling with criminal networks, and others report small rural locations with very few residents. Both these cases would be excluded in the current context due to their not being of the neighborhood type.

46 46 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER and knowledge regarding how they assess and classify vulnerable areas, and the methods, analysis, and findings from this paper should be regarded as complementary and not as the only way of understanding systematic similarities and differences between vulnerable neighborhoods. Identifying appropriate geographical units of analysis There are several challenges when trying to analyze the characteristics of vulnerable neighborhoods identified from a police perspective. One problem is that the vulnerable areas are hand-drawn on maps by the police. This means that they do not match any administrative or statistical designation of neighborhoods. To solve this problem, and as mentioned above, we have used national grid data for populated areas of Sweden. All densely populated parts of Sweden have been divided into 250 x 250 meter vector grids, and our main analysis focuses on the characteristics of those grids that intersect with a vulnerable neighborhood. In total, the dataset consists of 116,660 vector grids, of which 1,678, or approximately 1.4 percent, present a geographical overlap with vulnerable neighborhoods. On average, these grids have a much higher population density (mean = 380) than the others (mean = 73) and together account for 638,000 of the total sample of 8,863,000 Swedish residents, or 7.2 percent of the population living in densely populated urban areas. Since the data are focused on densely populated urban areas, the dataset does not cover the entire Swedish population, which was million in Data The data used in this analysis are drawn from five sources. As indicated above, these sources comprise: (1) a vectorized grid-net of Sweden with registry data on the population from Statistics Sweden; (2) crime data for eight crime categories provided by the police; (3) municipal level data on population and municipal boundaries provided by the Swedish Election Authority; (4) the areas that the police have labeled as vulnerable (also from the police); and (5) Electoral participation data on the election district level from the Swedish election authority which is used in a supplementary analysis as a test of social capital/collective efficacy. General data management Statistics Sweden deliver data based on a grid net covering Sweden, in which the whole country is divided into three subcategories. Places with no population, or very low population density, such as certain rural areas and areas of forest, are excluded. Grids outside urban areas and with low population density have a size of 1,000 x 1,000 meters. Vector grids with a high population density are registered with a size of 250 x 250 meters. These grids cover the populated parts of towns and cities. Because the focus of this study is on densely populated areas, only the 250 x 250 meter grids have been included in the analysis. Several steps were taken to generate our final dataset. In the first of these, the geography of the four datasets containing the original source data were brought together, which resulted in a combined dataset containing 116,660 geographical grids with data from at least one of the four datasets. This means that we have generated as much geography as possible by allowing every vector grid that is present in at least one of the four datasets to be included. In the second step, we combined these grids with a second layer of geographical data on the location of Swedish municipalities produced by the Swedish Election Authority. Each

47 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No grid thus contains information about the municipality in which its centroid is located. In the third step, we added municipality population data from Statistics Sweden for the year 2018, so each grid contains information on the number of residents in the municipality in which it is located. In the fourth step, we linked data from police records on crime locations to the grids so that each grid contains information about the number of crimes recorded within it. All data management was performed in the statistical software package R (R Core Team, 2020), using RStudio and the dplyr (Wickham et al., 2020) and sf (Pebesma, 2018) packages. Regressions were fitted using the lme4 (Bates et al., 2015) package. Registry grid data and municipal population Four different sets of registry data from Statistics Sweden have been employed to capture foreign background (2018), employment (2017), household structure (2018), and age (2017). The foreign background dataset includes four variables, which capture whether the residents and their parents were born in Sweden or abroad. In the present study, we use the proportion of the population who are foreign born and the proportion with two foreign-born parents. The employment data include the number and proportion of people aged who are in employment. Being employed is defined as having worked at least one hour per week in the measurement month, i.e., November 2017 (Statistics Sweden, 2021). In this paper, we use the proportion of the employed population aged The age-group dataset consists of the number of people in different age groups. In the present study, we use the proportion of the population comprised of children aged 0 15 years and the proportion comprised of youth/young aged The household structure data are divided into single-person households, partners without children, partners with child(ren), single parents with child(ren), and other households. Inclusion in the other households group means that at least one person in the household is neither a partner nor a child. This group includes for instance multi-generation households, friends living together, and similar. In the present study, we have used other households, since Puur (2020) showed that the presence of such households is associated with vulnerable neighborhoods. Crime data The dataset containing crime data from the police consists of reported outdoor offenses from the years and data from the police registry on illegal firearm discharges from the month of November 2016 through The statistics can be influenced by the willingness to report crimes, and in locations where people distrust the police and/or are afraid to contact the police due to a perceived risk of retaliation from criminals, the proportion of offenses that are reported to the police is likely to be lower. This is in turn likely to be associated with the socio-economic status of a location, with disadvantaged areas being characterized by both lower trust in the police and more fear of criminals (Goudrian et al., 2004). In Sweden, the difference between deprived neighborhoods and other places is, however, not particularly pronounced. The levels of trust are somewhat lower in deprived neighborhoods, but trust levels remain at a fairly high level overall (Brå, 2018). A recent report has also shown that, net of individual level controls, there were no reporting differences for robberies or sexual offenses associated with living in a deprived neighborhood, but there was a decline in the rate of reporting for assaults (Brå, 2021).

48 48 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER The illegal firearm discharge registry is a separate record, maintained by Swedish Police since November 2016, that keeps track of gun violence. Since there is no offense code in the official statistics that captures all gun violence, the police deemed it necessary to track this issue separately. The incidents included in this registry may cover a wide range of offense categories, with one incident often comprising more than one offense. Common offenses in the illegal firearm discharge registry include homicide, attempted homicide, aggravated assault, vandalism, and crimes listed under the Weapons Act. The data on offenses reported to the police comprise four types of violent crime: assault, robbery, assaulting a public official, and public endangerment through the use of explosives (this offense code was introduced in 2018). It should be noted that some of the illegal firearm discharges are likely to be included here as well, but since there are very few illegal firearm discharges in comparison to the number of assaults, this is unlikely to have any major impact on our results. To shed more light on the relationships, we also consider four non-violent crimes: bicycle theft, theft from a motor vehicle, vandalism, and narcotics offenses. As can be seen from Table 1, many of the variables are over-dispersed, in particular the crime variables, which are very unevenly distributed across the grids. Table 1 Descriptive statistics for the grid-level variables included in the study. Reported as counts for population and crime, and as proportions for all other variables. Valid observations are based on queen contiguity values as discussed below. Variable Valid observations Vector-grid mean Standard deviation Population Proportion employed Proportion foreign born Proportion with foreign-born parents Proportion other households Proportion aged Proportion aged Shootings Explosions Bicycle theft Narcotics offenses Robberies Vandalism Assault in public environment Assaulting a public official Theft from motor vehicle For analysis, all variables were standardized into z-scores. Election participation data In a supplementary analysis, we also consider the share of the population that participates in national elections as a measurement of social capital or collective efficacy, which has been linked to crime (Sampson et al., 1997) and to vulnerable neighborhoods (Polisen, 2017). While election participation is a more general social capital variable than collective efficacy,

49 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No prior studies have used similar data to operationalize collective efficacy (Weisburd et al., 2014). The election data is on electoral district level, and we have assigned each vector grid the value of the electoral district that it overlaps the most with using the st_join function in R. Analysis and research design Since vulnerable neighborhoods by definition have to be large and, on average, include 31 vector grids, we need to model spatial dependencies or clustering of the data in our analysis. We do this by calculating the mean value for each variable in adjacent grids (Queen contiguity), which we call cluster vector grids. We assume that this aggregate value for multiple grids will be more predictive of a location being a vulnerable neighborhood than the individual value in the vector grid itself. This is because clusters of grids are more geographically similar to a vulnerable neighborhood than the value of a single grid. Our assumption was tested by using the different versions of our variables in regression models, and the assumption turned out to be correct (results not shown). We therefore only use the mean value for adjacent vector grids in the paper. Our analysis focuses on whether or not a vector grid is part of a vulnerable neighborhood. Since we expect there may be subjective differences between different police districts or municipalities in how they assess vulnerable neighborhoods, we fit the models as generalized mixed effects models with vector grids nested in municipalities. Before fitting our models, we standardized our continuous variables into z-scores. To consider differences across municipalities, we ran single-level models with dummies added for each municipality that has at least one vulnerable neighborhood using the fast dummies module in R. This generates a dichotomous variable for each municipality that has at least one vulnerable neighborhood. The regressions were fitted using the glmer command in the lme4 library with a logit link, since the outcome variable is dichotomous. We test the associations with our independent variables in three main steps, each of which is divided into smaller parts. In the first step, we perform our main analysis, which corresponds to the first research question: What are the characteristics of vector grids in vulnerable neighborhoods? The models are mixed effects models, which take both the grids and the municipality into account. 3 We first fit a model that only includes population at the municipal geographical level as an independent variable. In the second model we add crime, and in the third model population-based variables. This results in global level associations for whether a grid is considered vulnerable. These models include a random intercept at the municipal level. To further assess our findings, we also provide a fourth model where we add electoral participation as that is an important variable theoretically. This variable is available on a different geographical unit of analysis, electoral districts, and these models are, therefore, fit as three-level models of vector grids nested in districts nested in municipalities. 3. The models were fit using the bobyqa optimizer to reduce problems associated with non-converging models.

50 50 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER In the second step, we compare systematic differences between municipalities in line with the second research question. We fit a single-level model corresponding to model 3 in table 2 but with dummy variables for each municipality with at least one vulnerable neighborhood. This is to see whether there are systematic differences between municipalities in how many vector grids are classified as vulnerable, with the size of the dummy coefficient being indicative of a difference. In the third step, which corresponds to the third research question, we use the same regression model, but without municipality dummies, to obtain predictive values for whether a vector grid meets the model criteria for being vulnerable, and we use weights from this regression to generate a vulnerability index. The index is calculated by multiplying the value of a variable in each vector grid by the regression coefficient for the same variable. We do this by using both only significant coefficients and using all coefficients for the index. Using these indexes, we then produce maps showing the similarities and differences between how our model predicts vulnerability and the way in which the police have classified vector grids as being vulnerable. We do this for three municipalities: the municipality with the most vulnerable neighborhoods and the two that have the highest and lowest municipal dummy variable coefficients from step two outlined above. The latter two can be interpreted as capturing municipalities for which the model identifies a potential bias in the classification, i.e., municipalities that have more or fewer grids than the model would suggest. A high value for the dummy variable means the model is suggesting that the municipality should have a higher number of grids that are classified as vulnerable, and vice versa. Results We began by analyzing bivariate associations between the independent variables and the dependent variable (results not shown). All variables present associations in the expected direction, as discussed below. The crime variables are positively associated with a vector grid being labeled as vulnerable. The strongest association is for shootings, followed by robbery, narcotics and theft from motor vehicles. Among the non-crime variables, higher employment presents a negative association and is thereby linked to a lower probability of an area being classified as vulnerable, while having higher proportions of children and youth present a positive association. However, these associations are weaker than the association with population, which is in turn weaker than the associations with the variables related to foreign background. Main findings Based on these findings, we proceed with our main analysis (Table 2). The generalized linear logistic regressions are fitted using four main models, with the first model only including the municipal population, and with the second adding cluster levels of crime. The third model adds the cluster mean population, in addition to the cluster proportions of foreign born, of foreign-born parents, of employed, of children, of youth and of other households, and the fourth adds electoral participation. Model 1 shows that there is a strong association between municipal size measured by population and the likelihood of a grid being classified as vulnerable. This is unsurprising and, to some extent, simply reflects the need for a large population to generate the type of local criminal networks that are in focus in vulnerable areas. However, this result constitutes

51 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No a useful point of departure for further analysis. As shown in the high ICC-values, most of the variance is situated between municipalities rather than within municipalities. Model 2 shows a strong relationship between levels of assault, shootings, and narcotics crime in clusters of vector grids and being classified as vulnerable. Explosions, robberies, and theft from motor vehicles also exhibit positive, yet weaker, associations. Vandalism is unrelated to the outcome, while assaults against public officials and bicycle theft present strong negative associations with being labeled vulnerable. The strong negative coefficient for assaults on public officials is somewhat unexpected given that attacks against the police in vulnerable neighborhoods have received widespread attention. Model 3 shows that population presents a significant, and fairly strong, association with grids being classified as vulnerable by the police. A one standard deviation increase in population, corresponding to 150 residents per vector grid in the area, has an impact that is approximately twice as large as that of a one standard deviation increase in shootings (corresponding to 0.11 shootings on average or about one shooting for clusters that include the full nine vector grids). Employment is the only population-based variable that is not significant, which is somewhat surprising given that unemployment is one of the variables explicitly discussed by the police in their definition of vulnerable neighborhoods. There are higher values registered for the proportions of foreign born, foreign-born parents, children, youth, and other households in vulnerable grids. The strongest associations are noted for the foreignbackground variables. A one standard deviation increase in the proportion of foreign born (17 percentage points) has an impact that is approximately twice as large as that of a similar increase in population, or more than four times as large as a similar increase in shootings measured in standard deviations. Adding the population-based variables reduces the coefficients for all crime variables except vandalism. Shootings, narcotics, assaulting public officials and bicycle theft are the only crime variables that retain significance. The reduction is quite dramatic for assaults, with a shift from a large and highly significant coefficient to becoming non-significant. Bicycle theft has the largest coefficient among the crime variables, and the coefficient is negative, which means that one of the primary crime-related factors for understanding grids labeled as vulnerable is that they have much lower levels of police-reported bicycle thefts. To sum up, then, the third model shows that vulnerable vector grids tend to be in clusters of vector grids characterized by more shootings and narcotics crime but fewer bicycle thefts and assaults on public officials. The surrounding vector grids also tend to have a larger population, a higher proportion of children and youth, a higher proportion of the other household type and a much higher proportion of residents of foreign background, all expressed in terms of standard deviations.

52 52 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER Table 2 Generalized linear mixed effects model with logit link on whether a vector grid is in a vulnerable neighborhood. All vector-grid variables use the mean for cluster vector grids. All variables are standardized, and random intercepts at the municipal level are included in all models. * p <.05 ** p <.01 *** p <.001 Variable Model 1 Model 2 Model 3 Model 4 Coeff (p) Coeff (p) Coeff (p) Coeff (p) Shootings.476 ***.162 ***.194 *** Explosions.085 *** Assault.904 *** Assaulting a public official *** *** Robbery.083 * *** Theft from motor vehicle.093 *** *** Bicycle theft *** *** *** Vandalism Narcotics.420 ***.223 ***.452 ** Population.438 *** *** Proportion Employed Proportion Foreign born.748 *** *** Proportion foreign-born parents.572 ***.661 *** Proportion aged **.292 * Proportion aged *.252 * Proportion other households.277 ***.558 *** Electoral participation -355 *** Municipal-level variable Municipal population 3.83 *** 3.51 *** 2.68 ** ICC Intercept AIC N In a final model we assess whether a measurement of social capital the share of residents who voted in the last election (2018) impacts on our understanding of the characteristics of vector grids in vulnerable neighborhoods. In Table 2, model 4, we present the results from this three-level model of vector grids nested in electoral districts nested in municipalities. There are several substantial differences in this model, with perhaps the most prominent change being that the municipality population is rendered non-significant, while the electoral participation variable is highly significant instead. In addition, assaulting a public official becomes non-significant, while robbery becomes significant and positive, and theft from motor vehicle becomes significant and negative. The coefficients for several other variables change, too, but not significance or the sign. This highlights the importance of considering social capital, collective efficacy, and similar variables as important measures in understanding vulnerable neighborhoods.

53 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No Systematic differences between municipalities The above analysis has shown how we can understand (clusters of) vector grids that are labeled as vulnerable, but we have yet not touched on possible differences between municipalities. However, our random intercept models registered large differences in the local intercepts, and we now proceed to test systematic differences between municipalities, in line with our second research question. We do this by fitting our models without a random intercept, i.e., as single-level models, in 27 separate regressions that include a dummy for each municipality that has a vulnerable neighborhood (Table 3). 4 As expected, we find that the coefficients tend to be positive, which is natural since we only include dummies for neighborhoods that have vulnerable neighborhoods, while there will also be similar locations elsewhere. Huddinge is the municipality with the lowest coefficient, followed by Landskrona, whereas Örebro and Jönköping have the highest coefficients. We interpret this as showing that our model is suggestive of there being more/larger areas that would be classified as vulnerable in Huddinge and Landskrona, and fewer/smaller areas in Örebro and Jönköping. We also fitted this as one single regression with all dummies included, yielding similar differences but different coefficients (results not shown). Table 3 27 municipality dummy regressions, fitted as single-level models with the same variables as Model 3 in Table 2 (with the exception of the random intercept), with a separate regression for each municipality with a vulnerable neighborhood adding its dummy variable. The municipality of Ale is in italics to highlight the fact that this municipality does not have a vulnerable neighborhood. * p <.05 *** p <.001 Municipality (ID) Municipal dummy coefficient County Population in municipality 2018 Järfälla (123).384 Stockholm Huddinge (126) Stockholm Botkyrka (127).398 Stockholm Haninge (136).805 ** Stockholm Upplands-Bro (139).335 Stockholm Sollentuna (163).730 * Stockholm Stockholm (180).483 Stockholm Södertälje (181).879 ** Stockholm Sundbyberg (183).504 Stockholm Uppsala (380).651 * Uppsala Eskilstuna (484).394 Södermanland Linköping (580).513 Östergötland Norrköping (581).712 * Östergötland Jönköping (680).944 *** Jönköping Växjö (780).862 ** Kronoberg There are 26 municipalities with a vulnerable neighborhood, but in one case this neighborhood is right on the border with another municipality, with some of the vector grids being assigned to the other municipality. We include this other municipality (Ale) for reasons of transparency.

54 54 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER Municipality (ID) Municipal dummy coefficient County Population in municipality 2018 Malmö (1280).693 * Skåne Landskrona (1282) Skåne Helsingborg (1283).118 Skåne Kristianstad (1290).560 Skåne Halmstad (1380).440 Halland Ale (1440).414 Västra Götaland Göteborg (1480).633 * Västra Götaland Trollhättan (1488).527 Västra Götaland Borås (1490).438 Västra Götaland Örebro (1880) 1.02 *** Örebro Västerås (1980).205 Västmanland Borlänge (2081).631 * Dalarna Predicting vulnerable neighborhoods The final step of the analysis involves using the results from our regressions to consider places that the model believes should be classified as vulnerable but which are not, or vice versa. This corresponds to our third research question. We will not be exploring this in detail in this paper, but, for exploratory purposes, we will calculate predictions and show predictive maps of the two municipalities whose dummies produced the highest and lowest coefficients: Huddinge (lowest) and Örebro (highest), in addition to the municipality with the largest number of vulnerable neighborhoods (Gothenburg). The random intercept model provides a good estimate of how to understand which vector grids are likely to be designated as vulnerable, but, for predictive purposes, the random intercepts make it more difficult to estimate global predictive values. We therefore fit singlelevel models and use the coefficients from the regression as weights to calculate a predictive index for whether or not a vector grid should be considered vulnerable. The single-level regression yields similar results, although some coefficients change. The most substantial changes are that the coefficient for the municipal population variable is much lower and that a significant association is registered between robbery and being vulnerable. We use the coefficients to calculate a prediction index, in which each grid is assigned the value of each significant coefficient times the value of the variable. We then test which threshold value on this index is needed to produce the same number of vector grids as the number classified as vulnerable by the police (1,678), and we use this value (5.835) as a threshold for predicting which of the vector grids should be classified as vulnerable. This results in 60.9 percent of the vector grids classified by the police as vulnerable being classified as vulnerable by our model, while the remaining 39.1 percent of vector grids are substituted by different vector grids. We have also repeated this process using only the significant variables in the model. This yields very similar results but with slightly more vector grids being replaced (39.8 percent). Figure 1 shows the western section of Huddinge, which roughly corresponds to half of the municipality. 5 There is a neighborhood classified as vulnerable by the police in the west- 5. The rest of the municipality contains no grids that are predicted to be vulnerable.

55 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No ernmost part (Vårby), which mostly consist of vector grids identified as vulnerable by the model, but which has a section in the middle that is classified as non-vulnerable presented as translucent cells in the figure. The model identifies a few vector grids to the east of this neighborhood as vulnerable in addition to a few grids scattered in other locations. Importantly, though, the model also identifies a large cluster of vulnerable vector grids in the south-eastern section of the map (red cells). This corresponds to the Flemingsberg area. If we were to construct vulnerable neighborhoods based on the model, this part of Huddinge would emerge as a new vulnerable neighborhood. For Örebro Municipality, the model very accurately predicts the neighborhood classified as vulnerable by the police in the northern part of the municipality (Vivalla). As regards the other vulnerable neighborhood (Oxhagen/Varberga) in the municipality, however, the model shows this neighborhood mostly to consist of vector grids that are not vulnerable, with only six of 21 vector grids being classified as such. The model thus suggests either that

56 56 MANNE GERELL, MIA PUUR AND NICKLAS GULDÅKER the size of the southern vulnerable neighborhood should be reduced or that it should be declassified as vulnerable based on the model predictions using the variables included in this analysis. The two examples described above involve medium-sized cities. In our final example, we present Gothenburg, the second largest city in Sweden. Gothenburg has nine vulnerable neighborhoods, which is the highest number for any city in the country. As can be seen from Figure 3, our model suggests that several areas in north-eastern Gothenburg that the police have not classified as vulnerable have characteristics that are similar to those of vulnerable neighborhoods. There are already six vulnerable neighborhoods in the north-eastern section of the city, but the model classifies large parts of neighboring areas as also being vulnerable. In addition, the model suggests that the Backa neighborhood (in the center of the map) should be somewhat smaller. Interestingly, the model also identifies an area in the city center as being vulnerable. This appears to be driven by a very high number of narcotics offenses,

57 NORDIC JOURNAL OF URBAN STUDIES VOLUME 2 No robberies and a high proportion of youth. Since this is a very different type of location, it highlights a need to adjust this type of model to account for city center effects in future studies. Discussion This study has shown that the strongest predictors of a vector grid being labeled as vulnerable are the foreign-background variables and the size of the municipality in which the grid is located. The prominence of these variables is not surprising, but the fact that their coefficients are so much larger than many of the other variables is surprising and will be discussed further below. Of the crime variables, being vulnerable is positively related to shootings, narcotics crime, and, in some models, robberies It is strongly negatively associated with policereported bicycle thefts and, in most models, with assaulting a public official. It is already well established that gang violence, including shootings and explosions, is strongly linked to these neighborhoods (Gerell et al., 2021; Sturup et al., 2020). Similarly,