urban studies effects of greening and community urban ...youngstown, ohio like other legacy cities...

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Urban Studies2016, Vol. 53(15) 3279–3295� Urban Studies Journal Limited 2015Reprints and permissions:sagepub.co.uk/journalsPermissions.navDOI: 10.1177/0042098015608058usj.sagepub.com

Effects of greening and communityreuse of vacant lots on crime

Michelle KondoUSDA Forest Service, USA

Bernadette HohlRutgers University, USA

SeungHoon HanUniversity of Pennsylvania and USDA Forest Service, USA

Charles BranasUniversity of Pennsylvania, USA

AbstractThe Youngstown Neighborhood Development Corporation initiated a ‘Lots of Green’ programmeto reuse vacant land in 2010. We performed a difference-in-differences analysis of the effects of thisprogramme on crime in and around newly treated lots, in comparison to crimes in and around ran-domly selected and matched, untreated vacant lot controls. The effects of two types of vacant lottreatments on crime were tested: a cleaning and greening ‘stabilisation’ treatment and a ‘communityreuse’ treatment mostly involving community gardens. The combined effects of both types of vacantlot treatments were also tested. After adjustment for various sociodemographic factors, linear andPoisson regression models demonstrated statistically significant reductions in all crime classes for atleast one lot treatment type. Regression models adjusted for spatial autocorrelation found the mostconsistent significant reductions in burglaries around stabilisation lots, and in assaults around com-munity reuse lots. Spill-over crime reduction effects were found in contiguous areas around newlytreated lots. Significant increases in motor vehicle thefts around both types of lots were also foundafter they had been greened. Community-initiated vacant lot greening may have a greater impact onreducing more serious, violent crimes.

Keywordscommunity gardens, crime, difference-in-differences, greening, urban health

Received December 2014; accepted August 2015 Corresponding author:

Michelle Kondo, USDA Forest Service, Northern Research

Station, 100 North 20th St, Suite 205, Philadelphia, PA

19072, USA.

Email: [email protected]

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A legacy of suburban out-migration, eco-nomic restructuring, and housing foreclosurecrises have had a major negative impact onthe physical and social structures of manyurban areas (Beauregard, 2009; Mallach andBrachman, 2013; Mallach et al., 2010).However, to date, scientific analyses ofdilapidated buildings and vacant land havealmost exclusively focused on large USlegacy cities (Branas et al., 2011; Kondoet al., 2015a), leaving in question the impactof these structures on mid-sized legacy cities.As a mid-sized legacy city with almost one-third of its properties listed as vacant(Mahoning Valley Organizing Collaborative,2010), Youngstown, Ohio represented anexcellent opportunity for study.

Vacant buildings and lots come with sig-nificant economic costs – remediation,demolition, reduced values to surroundingproperties, and reduced tax revenues formunicipalities that might otherwise applysuch revenues to social and welfare pro-grammes for their citizens (Accordino andJohnson, 2000; Han, 2014). There is growingevidence that the presence of vacant build-ings and lots also have negative impacts oncommunity health and safety (Chaix, 2009;Cohen et al., 2003; Hannon and Cuddy,2006; Spelman, 1993; Wei et al., 2005).

In the absence of meaningful federal andstate support, legacy cities have been forcedto initiate new, but unproven, programmesto abate physical signs of disorder in anattempt to revitalise their neighbourhoods,reduce expenses, generate tax revenues, andpotentially improve public safety (Accordinoand Johnson, 2000; Cohen, 2001; Katz andBradley, 2013; Teixeira and Wallace, 2013).These programmes are often enabled by thedevelopment of a variety of policy andadministrative tools and entities, such asvacant-lot nuisance ordinances, buy-back ortake-over programmes, and land banks.

Many cities are incorporating vegetationand natural features into vacant-lot

remediation. Typical remediation strategiesinclude ‘cleaning and greening’ of lots, ortransformation into community gardens,stormwater management sites, or greenstormwater infrastructure. Urban municipa-lities are hopeful that greening vacant lotswill reduce crime and provide other co-bene-fits, including environmental, and some evi-dence is suggesting that this may be true(Kondo et al., 2015b, 2015c). However,there remain opportunities for further rigor-ous evaluation of vacant lot reuse pro-grammes particularly on social outcomes.

This paper describes the first quasi-experimental study using a spatial panel datamodel that considers the impact of commu-nity involvement in vacant lot greening oncrime. The City of Youngstown, in partner-ship with the Youngstown NeighborhoodDevelopment Corporation (YNDC) initiateda ‘Lots of Green’ (LOG) programme torepurpose and reuse vacant land in 2010. TheLOG programme included a cleaning andgreening ‘stabilisation’ treatment conductedby YNDC, and a ‘community reuse’ treat-ment that was initiated and maintained bycommunity groups (and mostly involved com-munity gardens). We used a difference-in-differences analytical approach to test whetherthe YNDC’s LOG programme had an effecton safety in Youngstown over a 25-month-period starting in 2010, after accounting forseveral key sociodemographic factors. Wedefined safety in terms of various crimes andtested for the effects of treated lots on crimeamong all vacant lots as well as separatelyamong the stabilisation and community reusetreatment schemes. Also, given the concernthat the programme could displace crime toother places, we expanded the spatial analysisto trace the geographic dynamics of the effect.

Literature review

There is evidence that vacant lots play a rolein the health and safety of nearby residents.

3280 Urban Studies 53(15)



These lots are often sites of illegal dumpingfor materials such as construction debris,chemicals, tyres, furniture, and abandonedvehicles (Beauregard, 2013). Vacant lots areassociated with fear of crime (Garvin et al.,2013; Hur and Nasar, 2014) and crime itself.For example, recent research has demon-strated a significant link between the pres-ence of neighbourhood vacant propertiesand an increased risk of neighbourhoodassaults (Branas et al., 2012; Garvin et al.,2012). Studies have also found associationsbetween presence of vacant properties andphysical health outcomes including rates ofpremature mortality (Cohen et al., 2003),drug-dependence mortality (Hannon andCuddy, 2006), cardiovascular disease(Augustin et al., 2008; Chaix, 2009; DiezRoux et al., 2001), teen births (Wei et al.,2005), and sexually-transmitted disease(Cohen et al., 2000). Vacant properties alsoincrease the risk of fire (Schachterle et al.,2012).

Some vacant lot remediation programmeshave already shown promising results interms of affecting crime (Kondo et al.,2015c). Greening has also been associatedwith an enhanced sense of safety and feelingsof security (Garvin et al., 2012). Residentsliving near greened vacant land perceive it tobe significantly safer than do people livingclose to untreated, often unkempt, vacanturban land (Garvin et al., 2012).

In addition, existing theory from the fieldof environmental criminology, known ascrime opportunity theory (Wilcox et al.,2003), supports this positive association.Crime opportunity theory argues that crimesprimarily occur when appropriate opportu-nities are present for motivated individuals.Manipulating the elements of crime oppor-tunity in urban environments is a strategicand perhaps sustainable and cost-effectiveway to prevent crime (Clarke, 1980). The ele-ments of opportunity in the occurrence ofcrime include a ‘motivated offender’ who

encounters a ‘suitable target’ in the absenceof ‘capable guardians’ (Cohen and Felson,1979). Even in the presence of motivatedoffenders, greening an area could changeroutine activities of those who live in andaround the area, thereby influencing the sup-ply of suitable targets and capable guar-dians. For example, people may walk, run orbike in and around a vacant lot because theyare experiencing less fear after it is greened,which increases the supply of potential wit-nesses and guardians of the spaces in ques-tion. Offenders may want to avoid a newlygreened area because it is no longer a placein which they feel comfortable committingcrimes without detection (Brantingham andBrantingham, 1993).

Broken windows theory provides anotherrationale for how greening may reducecrime. Wilson and Kelling (1982) argue thatrepairing broken windows and other signs ofdisrepair, like abandoned vacant lots, canreduce not only social disorder and publicincivility but also serious crime because theformer leads to the latter. Despite this, thereare a limited number of field trials that sup-port this relationship (Keizer et al., 2008).Though it is now common practice to targetminor offences in order to ultimately reducemajor crimes, broken windows policingdraws criticism for its disproportionate harmto poor and communities of colour and itslack of sufficient empirical evidence(Harcourt and Ludwig, 2006). This suggeststhat there is more work to be done to bettersupport this theory’s translation into policy,and the study of actual changes to neigh-bourhood environments (i.e. seeing whathappens after ‘broken windows’ arerepaired), like vacant-lot greening, is a keyway to accomplish this.

However, with few exceptions, most stud-ies addressing the relationship between greenspace and crime have been cross-sectional,and provide limited evidence of before–aftercausal effects (Lee and Maheswaran, 2011).

Kondo et al. 3281



One exception, a quasi-experimental studyin Philadelphia, found that greening reme-diation of vacant lots was associated withreduced gun assaults and vandalism, as wellas improved health indicators such as lessstress and more exercise (Branas et al.,2011). This study has yet to be replicated inanother urban area and reflects the experi-ence of a large US city; it is unclear whetherits findings are fully applicable to medium-sized legacy cities which are much larger innumber across the US and have somewhatdifferent urban challenges.

Study context

Youngstown, Ohio like other legacy cities inthe United States is dealing with effects fromhistorical deindustrialisation, economicrestructuring and suburbanisation. Around1930 the city grew to accommodate a popu-lation of over 170,000. Since that time thecity’s population has been in constantdecline. Youngstown’s population hasdecreased by an average of 17% in each ofthe last five decades, and 65,405 residentsremained in 2010 (US Census Bureau, 2010).It has been projected that without interven-tion, the city’s population could continue tofall to 54,000 by 2030 (City of Youngstown,2005).

A large proportion of the homes andinfrastructure built to support the popula-tion of 170,000 is now vacant. A 2010Vacant Property Survey conducted by theMahoning Valley Organizing Collaborativefound that 23,831 of 62,569 parcels werevacant. These parcels accounted for over5600 acres or over 31% of the city’s landarea, which is two times the average reportedfor a sample of US cities with populationover 100,000 (Pagano and Bowman, 2000).In addition, 3246 parcels contained vacantstructures, a rate nearly 20 times the averagefound by Pagano and Bowman (2000).Nearly 1500 structures were in foreclosure at

the time, which suggests that there waspotential for an increase in the vacancy inthe near future.

While early efforts to stabilise shrinkingcities solely emphasised economic growth,cities such as Youngstown are now also tak-ing ‘right sizing’ approaches (Rybczynskiand Linneman, 1999), recognising the incon-gruence between existing social and marketdemands and city plans (Rhodes and Russo,2013). Youngstown began a collaborativecitywide effort in 2000 to develop a newcomprehensive plan (called Youngstown2010) that envisions a ‘smaller, greener, clea-ner’ city (City of Youngstown, 2005: 8).

Part of the Youngstown 2010 strategy isadaptive mitigation or reuse of vacant land.LOG, managed by YNDC, is a vacant landreuse programme that emerged as part ofthis strategy. YNDC initiated LOG version1.0 in 2010 in two target areas (Idora andEast Side neighbourhoods; see Figure 1). Inthese areas, YNDC identifies vacant lotsthat are strategic for reuse, indicating forexample that the lot is located in a blightedarea, a prominent or visible area such as aneighbourhood entrance or an intersection,and the lot has property owners that do notobject to the reuse. With funding from com-munity block grants, YNDC applies thesame basic ‘stabilisation treatment’ toselected lots. This treatment involves: (a)removal of debris, hazardous trees, founda-tions or driveways; (b) addition of topsoiland grading; (c) grass seeding and plantingof two trees; (d) addition of a permeablesplit-rail fence around the lot perimeter toprevent future driving or dumping on thelot; and (e) lot maintenance, which includesmowing approximately every two weeks dur-ing spring and fall. At the time of this study,YNDC had applied basic treatment to166 vacant lots.

YNDC also initiated LOG version 2.0, anapplication-based lot reuse programme, in2011 (‘community reuse treatment’). Under




LOG 2.0, Youngstown residents, groups ororganisations apply for support and fundingto reuse a vacant lot (primarily outside ofIdora and East Side; see Figure 1). YNDCawards financial support with communityblock-grant funding to applicants that showa strong community need and benefit fromreuse. Applicants determine the type ofreuse. To date most are community gardens,urban farms or orchards (47), in addition tonative plantings (17), athletic fields (5), orputting greens (8). This study included atotal of 77 lots that received LOG 2.0 treat-ment. Under LOG 2.0 the applicant isresponsible for maintaining the reused lot.

Methods

We conducted a difference-in-differencesanalysis to assess the impact of the LOGland reuse programme on crime outcomesin Youngstown, Ohio over a four-yeartime period. The difference-in-differences

approach can model treatment effect by esti-mating the difference between outcome mea-sures at two (or more) time points for boththe treated and control locations (those notimplementing or participating in the policyor programme) and then comparing the dif-ferences in outcomes between treated andcontrol groups. Using this strategy helps toensure that any unobserved variables thatremain constant over time, and that are cor-related with the selection decision and theoutcome variable, will not bias the estimatedcoefficients (Branas et al., 2011; Buckley andShang, 2003).

Study design

We considered all 244 LOG reused vacantlots as the treatment group and conductedanalysis on total lots, and LOG 1.0 andLOG 2.0 lots separately. A separate set ofvacant lots (with no structures) eligible toserve as controls (‘LOG-eligible’) were

Figure 1. Overview map of treatment and control lots in Youngstown, OH.

Kondo et al. 3283



those that had not been greened but couldhave been during the same time period (N =959). Demolitions had to have occurredprior to July 2010 for vacant lots to be con-sidered LOG-eligible. We used 2008 and2010 vacancy-survey data, collected bythe city, to verify vacancy status for all con-trol lots.

We then used a caliper matching methodwith a 4 to 1 ratio to randomly select andmatch untreated control lots to treatedlots. We matched lots located in the samecity section: North (48 treatment and191 control sites), South (188 treatment and736 control sites), and West (8 treatmentand 32 control sites), in order to controlfor potential differential development pat-terns. We used the following criteria tomatch treated with control lots: (a) lot sizewithin + /2500 square feet; and (b) sur-rounding median household income within+ /2$5000 USD. We also manuallyremoved and replaced any control lot within1/4-mile of its matched treatment lot to avoidcontamination of effect. The maximum dis-tance between a matched treatment and con-trol site was 7.6 miles. While matching usingother sociodemographic indicators wouldimprove the similarity of treatment and con-trol groups, we found that including moreindicators reduced the number of availabletreatment or control sample sizes in a waythat would reduce model robustness. Aftermatching, our analysis included 244 treat-ment lots and 959 control lots (see Figure 1).

Lot-greening completion occurredbetween July 2010 and August 2013. Withineach group of randomly-matched lots(N = 244), we assigned the treatment datefrom the treatment lot to the control lots.The study period is January 2010 toFebruary of 2014, in two-month time inter-vals for a total of 25 two-month timeperiods. The minimum pre-period and post-period was six months, with an average pre-and post-period of 22 months.

Data

The City of Youngstown and the Center forUrban and Regional Studies at YoungstownState University provided LOG-eligible par-cel data. They also provided 2008 and 2010vacancy-survey data.

Youngstown Police Department providedcrime incident data (with date, location andclass) from January 2010 to February 2014.The Center for Urban and Regional Studiesat Youngstown State University georefer-enced these data. We aggregated the eightavailable crime classes into five classes: (1) fel-ony assault (including homicides and aggra-vated assaults); (2) burglary; (3) robbery; (4)theft; and (5) motor vehicle theft. We used akernel density method to estimate the densityof crimes at every location. Crime locationswere translated to a raster-grid format, wherecells were 100 3 100 feet in size, and cell val-ues represented density of crimes per squaremile. Therefore, the dependent variable repre-sented counts, but was continuous. The num-ber of zero values were 1326 (felony assault),71 (burglary), 2862 (robbery), 47 (theft), and820 (motor vehicle theft).

We obtained demographic variables fromthe US Census, American CommunitySurvey (ACS) 2006–2010 and 2009–2013 atthe tract level. We gathered estimates ofpopulation density, income, residential stabi-lity, and unemployment, and interpolatedvalues between ACS data points. Populationdensity represented the number of personsper acre, income represented annual medianhousehold income, residential stability repre-sented percentage of the population living inthe same residence as 1-year ago, and unem-ployment signified percentage of the popula-tion over the age of 16 in the labour forcenot working.

Statistical analyses

We performed all analyses using Stata 13.1(StataCorp LP, College Station, TX) and




ArcMap 10.2 (ESRI Inc., Redlands, CA)software. We first conducted descriptive sta-tistics using cross-tabulations and tests fornormality. We tested for differences inmeans using a Wilcoxian rank-sum method.We then used three separate mixed modelsto test for the effects of vacant-land greeningon crime: (1) random-effects generalisedleast square regression models (‘Model 1’, or‘GLS’); (2) population-averaged Poissonregression models (‘Model 2’, or ‘Poisson’);and (3) random-effects spatial Durbinregression models (‘Model 3’, or ‘spatialmodel’).

First, we estimated random-effects gener-alised least square regression models withheteroscedacity-robust standard errors. Weused a log transformation to address asevere right-skewness of the crime outcomedata. Within each crime class, we assignedminimum values to any zero-crime observa-tions to avoid missing crime counts whenlogged. Each regression model (see Equation1) included a crime outcome Yit (whereobservation (i) = 1,2,3,.,N and time (t) =1,2,3,.,25); a pre-post construction termb1Pit; a treatment-control term b2Rit; adifference-in-differences term b3(Pit 3 Rit)which is the main variable of interest andthus b3 is the estimate reported in the resultssection; a pre-period mean outcome term toadjust for regression to the mean, b4Mi; aseries of four independent demographic cov-ariates, bkXkit (where k = 5,6,7,8; popula-tion density, income, residential stability,and unemployment at the tract level); andan error term uit that consists of a randomindividual-specific effect ai and an idiosyn-cratic error term eit.

1

Yit =b+b1Pit +b2Rit +b3(pit 3 Rit)

+b4Mi +X

kbkXkit + uit(=ai + eit)

ð1Þ

Second, because crime incidents representcount data, we ran population-averaged

Poisson regression models. In these models,we assumed that crime incidents were non-linearly and exponentially associated withcovariates including the treatment. The coef-ficients were estimated by the maximum like-lihood method. To address the risk of over-dispersion in a Poisson model, it is essentialto use the robust standard error (Cameronand Trivedi, 2009; Gould, 2011). Due to thelack of Stata code embracing both randomeffects and robust standard errors for aPoisson model, we applied a population-average estimation, which frequentlyperforms similarly to a random-effects esti-mator (Cameron and Trivedi, 2009). While anegative binomial distribution is consideredappropriate for the over-dispersion of crime,model results from negative binomial modelswere nearly identical to Poisson models withrobust standard error and therefore we onlyinclude results from Poisson models in thispaper. Incidence rate ratio (IRR) is reported,instead of regression coefficients, to reflectrelative difference.

Our third model addressed the issue ofspatial autocorrelation, or the fact that crimeincidents in one area may be strongly associ-ated with those occurring in surroundingneighbourhood areas. After testing for thepresence of spatial autocorrelation of resi-duals in OLS models using Global Moran’sI, we found that outcome incidents are notrandomly distributed (P \ 0.001), but wereclustered (I . 0).

Therefore, our third model was a spatialregression model (called ‘spatial Durbinmodel (SDM)’) that included a spatially-lagged dependent variable and spatially-lagged all explanatory variables as covariates,having a matrix form of Y = rWY +X 9b+WXu+ u (where r was the spatialautoregressive coefficient and u was a K � 1

vector of fixed but unknown parameters).This SDM has strength as a reference spatialmodel (Elhorst and Zigova, 2014; LeSageand Pace, 2009). As a nesting model, SDM

Kondo et al. 3285



can be simplified to a spatial autoregressivemodel (SAR, that has a spatially-laggeddependent variable only) when u= 0, and toa spatial error model (SEM, that has aspatially-lagged error term as u=Wu+ e)when u=�rb. We then investigatedwhether our SDM could be simplified toSAR or SEM by testing the null hypothesesH0 : u= 0 and H0 : u=�rb.

We considered seven types of spatialweight matrices (W) to describe the spatialrelationship between crime incidents. Thefirst spatial weights matrix used inverse dis-tances between the treatment and controlgroup units, which assumed that every unitinfluenced the others regardless of distance,and that closer proximity indicated greaterinfluence. The second and third spatialweight matrices were based on inverse dis-tances but with a cut-off of 1/8 and 1/4 miles,where two observations further apart thanthese distances were assumed to have no spa-tial relationship and thus assigned a zerovalue in the matrix. However, we alsoassigned one to the nearest neighbour of aunit if the unit did not have any neighbourunder the cut-off threshold, to assure everyunit had at least one spatial neighbour. Wechose these distances because they are com-monly used as the sphere of influence withinrestrictive zoning ordinances such as drug-free zones around schools (The SentencingProject, 2015). The fourth, fifth and sixthspatial weight matrices were based on 2-, 4-and 6-mile cut-off thresholds, respectively.We chose these three cut-off thresholdsbased on semivariogram results that depictedthe spatial autocorrelation among the lots.The semivariograms across our 25 timewaves in general had three peaks at thesethree distances, suggesting that there mightbe possible spatial interactions at these dis-tances. We also assigned one to the nearestneighbour of a unit if the unit did not haveany neighbour under those three cut-offs.

Finally, the seventh spatial weights matrixwas based on whether units were located inthe same census tract (= 1) or not (= 0).Unlike the others, this matrix was con-structed as a contiguity (0/1) matrix.

In the spatial models, estimating marginaltreatment effects on crime from b coeffi-cients may be misleading because b coeffi-cients represent only the association betweenthe treatment (X) and the crime outcome netof spatial correlation ((In � rW )Y ). Oneapproach that addresses appropriate mar-ginal effect forms in a spatial regression is tointerpret partial derivatives of the impact as‘direct’ and ‘indirect’ effects (LeSage andPace, 2009). We applied the SDM form ofY = rWY +X 9b+WXu+ u. With the formre-written as Y =(In � rW )�1(X 9b+WXu)+ (In � rW )�1u, the partial derivativesof yi with respect to xik for a particularregion (observation) i is denoted in thematrix shown in Equation 2 (Elhorst, 2014;LeSage and Pace, 2009).

dY

dx1k

: : . . .dY

dxNk

� �=

dy1

dX1k

..

.

dyN

dx1k

. . .

. ..

. . .

dy1

dXNk

..

.

dyN

dxNk

0BBB@

1CCCA

=(In � rW )�1 �

bk

..

.

wN1uk

. . .

bk

. . .

w1N uk

..

.

bk

2664

3775

ð2Þ

In this N by N matrix, the diagonal andoff-diagonal components provide the basesby which direct and indirect effects are cal-culated. When r and uk are zeros, whichmeans there is no spatial correlation, theright-hand side matrix has diagonal compo-nents of bk s and off-diagonal componentsof zeros. The matrix corresponds to a non-spatial regression result. However, when r isnot zero, the diagonal components are notbk s and the off-diagonal components are nolonger zeros. Also, when uk is not zero the




off-diagonal components are no longer zeroseven when r is not zero. For the right-handside matrix, the direct effect is defined as theaverage of the diagonal components, andthe indirect effect is the average of the col-umn or row sums of the off-diagonal com-ponents. The direct effect indicates theimpact that a treatment occurring in a par-ticular region (observation) has on its ownregion. The direct effect is not equal to b,but instead includes a feedback effect whichmeasures impacts that pass through neigh-bourhood regions and turn back to the orig-inal regions of treatment (Elhorst, 2014).The indirect effect indicates the impact thata particular region treatment has on regionsexcept for its own. The indirect effect canalso be viewed as a geographic spill-over

effect, which is one of the main interests inspatial analyses.

Results

Table 1 shows the baseline characteristics ofthe treatment and control lots across thewhole city and within each of the threedesignated sections. Greened vacant lots(N = 244) accounted for over 32 acres ofland in the city. Control lots (N = 976) cov-ered over 130 acres of land. The average sizeof treatment and control lots was 5700square feet and 5900 square feet, respec-tively. Statistical comparison of demo-graphic indicators showed that control lotsin at least one geographic area had statisti-cally lower median income, residential

Table 1. Baseline summary statistics for vacant lots, by city section, Youngstown, OH, 2010–2014 (meanand median values are shown).

No. oflots

Totalarea (ft2)

Area (ft2) Populationdensitya

Medianincomeb

Residence\ 1yr (%)c

Unemployed(%)

All sectionsGreened lots 244 1,402,710 5643 3.7 23,825 89.4 19.5

5749 4.1 23,865 89.2 20.5Control lots 976 5,772,842 5870 4.0 22,038** 84.7*** 17.5***

5915 3.9 22,015 84.2 17.4NorthGreened lots 48 294,789 5250 2.2 19,978 90.8 14.0

6141 1.8 21,144 91.1 12.7Control lots 192 1,206,102 5550 2.5 19,592 86.7** 15.9*

6282 2.6 19,973 84.1 17.9SouthGreened lots 188 1,059,155 5643 4.1 24,334 89.1 20.9

5634 4.2 24,412 89.2 21.0Control lots 752 4,369,801 5893 4.4 22,936 84.1*** 18.2***

5811 4.2 22,465 84.2 17.7WestGreened lots 8 48,766 6000 4.4 27,994 86.1 16.8

6096 4.4 27,324 86.4 17.1Control lots 32 196,938 6040 3.9 23,767** 85.1 11.9**

6154 4.0 23,668 85.0 11.4

Notes: * P \ 0.05, **P \ 0.01, ***P \ 0.001, based on Wilcoxon rank-sum test.aPopulation per square mile; bmedian household income; cresidence in current household more than 1 year.

Source: Demographic data are derived and interpolated from the ACS, 2006–2010 and 2009–2013.

Kondo et al. 3287



stability, or percentage unemployed.However, our regression models control fordifferences in these demographic indicators.

In addition, we calculated unadjusted esti-mates of differences in mean crime outcomesbefore and after treatment at control versustreatment lots. Difference-in-differences esti-mates, unadjusted for demographic confoun-ders, lot size, or geographic relationships, areshown in bold in Table 2 for each type of crime.While regression adjusted difference-in-differences estimates may show a different mag-nitude and significance of effect, unadjusted esti-mates do suggest strong reductions in crimes.

We report results from three regressionmodels. Model 1 (‘GLS’) represents random-effects generalised least square regressionmodels, and Model 2 (‘Poisson’) representspopulation-averaged Poisson regression mod-els. The Wald and the likelihood ratio (LR)test results led to the conclusion that our

SDM could not be simplified to a spatialautoregressive model (SAR) or an SEM,because both null hypotheses of H0 : u= 0

and H0 : u= �rb were consistently rejectedacross all types of crime outcomes. Therefore,we use SDM as Model 3 that accounts forspatial autocorrelations of crime occurrencesbetween lots. Also, for the spatial weightsmatrix selection, we report results from themodels with the 1/8- and 1/4-mile cut-offinverse distance spatial weight matricesbecause these spatial weight matrices had thelowest AIC and BIC scores compared toother spatial weight matrices (see Table 3).

Table 4 reports difference-in-differences(DD) coefficient estimates from each model,run on all treatment lots (both LOG 1.0 sta-bilisation and LOG 2.0 community reusetreatment). Column 1 reports the Model 1GLS regression DD estimates. Model resultsshow statistically significant (P \ 0.001)

Table 2. Unadjusted difference-in-differences estimates for crime outcomes (units are crimes per squaremile).

Pre-period mean Post-period mean Mean difference

Felony assaultsControl sites 1.86 2.63 0.77***Treated sites 1.71 2.13 0.42***Difference-in-differences 0.15*** 0.50 20.35***BurglariesControl sites 12.07 12.86 0.79***Treated sites 15.43 13.78 21.65***Difference-in-differences 23.36*** 20.92 22.44***RobberiesControl sites 1.18 1.68 0.50***Treated sites 1.01 1.22 0.21*Difference-in-differences 0.17 0.46*** 20.29***TheftsControl sites 9.64 10.29 0.64***Treated sites 10.49 10.65 0.16Difference-in-differences 20.85*** 20.36*** 20.48***Motor vehicle theftsControl sites 2.48 2.58 0.10***Treated sites 2.25 1.97 20.28Difference-in-differences 0.23*** 0.61 20.38**

Notes: *P \ 0.05, **P \ 0.01, ***P \ 0.001, based on two-sample Wilcoxon rank-sum (Mann-Whitney) test.

Sample sizes: pre-period (treatment: 12,928/control: 11,472); post-period (treatment: 3232; control: 2868).




reductions in all crime outcomes at greenedlots in comparison to untreated lots. Withthe outcome variable logged, these b esti-mates can be interpreted as percentagechanges at greened lots after treatment: 85%reduction in felony assaults, 24% reduction

in burglaries, 69% reduction in robberies,and 7% reduction in thefts, and 53%increase in motor vehicle thefts holding allother values constant.

Column 2 reports Poisson regressionIRRs. Like the GLS regression results, all of

Table 3. Comparisons of alternative spatial weight specifications with spatial lag model.

Crime Spatialweightsmatrix

1 2 3 4 5 6 7

Nocut-off

1/8 milecut-off

1/4 milecut-off

2 milecut-off

4 milecut-off

6 milecut-off

Sametracts

Felony assault AIC 115,953 71,267 71,343 10,7735 115,302 115,949 120,376BIC 116,119 71,433 71,509 107,902 115,469 116,115 120,542

Burglary AIC 65,822 16,433 17,889 53,648 62,200 65,779 66,393BIC 65,988 16,599 18,055 53,814 62,366 65,946 66,559

Robbery AIC 121,202 57,970 60,336 106,464 112,023 112,054 115,988BIC 121,368 58,137 60,502 106,630 112,189 112,220 116,154

Theft AIC 36,515 220,897 220,738 30,340 35,589 36,438 41,222BIC 36,681 220,731 220,572 30,506 35,756 36,604 41,388

Motor vehicletheft

AIC 126,086 76,849 73,657 112,654 121,692 126,054 121,013BIC 126,252 77,016 73,823 112,820 121,858 126,221 121,179

Table 4. Difference-in-differences estimates of the effect of vacant-lot greening (all treatments) on crime.

Outcomevariable

Model 1: Model 2: Model 3: Spatial Durbin model

GLSregression

Poissonregression

1/8 mile distance threshold 1/4 mile distance threshold

Coefficient IRR Directeffect

Indirecteffect

Directeffect

Indirecteffect

Felony assault 20.85*** 1.00 20.10** 21.04** 20.06 22.04*(0.08) (0.02) (0.04) (0.32) (0.04) (0.92)

Burglary 20.24*** 0.80*** 20.06*** 20.34*** 20.04*** 20.48*(0.03) (0.02) (0.02) (0.09) (0.01) (0.24)

Robbery 20.69*** 0.93** 20.08* 20.65 20.08* 21.31(0.09) (0.03) (0.04) (0.37) (0.04) (0.99)

Theft 20.07*** 0.96** 20.01 20.08 20.01 0.14(0.02) (0.01) (0.01) (0.09) (0.01) (0.26)

Motor vehicletheft

0.53*** 0.90* 0.10** 1.40*** 0.06* 2.66**(0.15) (0.04) (0.03) (0.34) (0.03) (0.91)

Notes: *P \ 0.05, **P \ 0.01, ***P \ 0.001, based on z-score.

N = 30,075 (= 1203 groups * 25 times).

Robust standard errors are shown for GLS, Poisson and SDM regression.

Kondo et al. 3289



the Poisson model results were statisticallysignificant (P \ 0.01) and negative (exceptfor no effect on felony assaults), which sug-gests that greening vacant lots may berelated with reductions of nearly all types ofcrime incidents that are considered in thisstudy.

However, the GLS and Poisson regressionmodels were not adjusted for spatial auto-correlation present in the data. Estimatesfrom the spatial regression analysis using theSDM (Columns 3 to 6) contrast with the twoprevious models in terms of significance andmagnitude of effect. Models using both the1/8- and 1/4-mile distance thresholds showfewer and less statistically significant reduc-tions in crimes. For each distance threshold,direct and indirect effects are presented. Atthe 1/8-mile distance threshold, felonyassaults and burglaries have statistically sig-nificant and negative direct and indirecteffects. This means that greening vacant lotsis associated with reduced crime not only atthe treatment locations but also in the neigh-bouring observation locations that were

located within a 1/8-mile radius of the treat-ment locations. There were no statisticallysignificant indirect effects associated withrobberies. In addition, these models showedstatistically significant positive associationwith motor vehicle theft (direct and indirect).At the 1/4-mile distance threshold, only bur-glaries and robberies showed statistically sig-nificant negative direct effects. Felonyassaults and burglaries had significant nega-tive indirect effects. Again, these modelsshowed statistically significant positive asso-ciation with motor vehicle thefts (direct andindirect).

Sub-population analysis results for thestabilisation treatment lots are shown inTable 5. Traditional stabilisation treatmentresulted in statistically significant reductionsaccording to GLS and Poisson models in fel-ony assaults, burglaries, robberies, and thefts(P \ 0.01). The GLS model again showed asignificant increase in motor vehicle thefts.The SDMs, adjusted for spatial autocorrela-tion at the 1/8- and 1/4-mile thresholds,found the most statistically significant

Table 5. Difference-in-differences estimates of the effect of LOG1.0 stabilisation treatment on crime.

Outcomevariable


GLSregression

Poissonregression



Indirecteffect

Directeffect

Indirecteffect

Felony assault 20.80*** 1.00 20.11* 20.64 20.04 20.69(0.10) (0.02) (0.05) (0.33) (0.05) (0.81)

Burglary 20.26*** 0.79*** 20.09*** 20.36*** 20.06*** 20.50**(0.03) (0.02) (0.02) (0.08) (0.01) (0.18)

Robbery 20.93*** 0.89*** 20.09* 20.65 20.10* 21.18(0.09) (0.03) (0.05) (0.36) (0.05) (0.81)

Theft 20.11*** 0.95** 20.03* 20.16 20.02 20.11(0.02) (0.02) (0.01) (0.12) (0.01) (0.28)

Motor vehicletheft

0.33 0.87* 0.09 1.02* 0.07 1.61(0.17) (0.05) (0.05) (0.41) (0.05) (0.93)


N = 20,450 (= 818 groups * 25 times).





reductions in burglaries in nearby and neigh-bouring regions (P \ 0.01). Assaults, rob-beries, and thefts were also significantlyreduced in nearby areas (P \ 0.05). SDM atthe 1/8-mile distance threshold also found asignificant indirect increase in motor vehiclethefts.

Table 6 shows that all models found morecrime effects for the LOG 2.0 communityreuse treatment compared to the stabilisa-tion treatment. Community reuse treatmentresulted in statistically significant reductionsaccording to GLS models in felony assaultsand burglaries (P \ 0.01). Poisson modelsfound statistically significant reductions inburglaries (P \ 0.001). The largest signifi-cant magnitude of change was seen with fel-ony assaults according to GLS models, andwith burglaries according to Poisson models.SDM at the 1/8-mile threshold found felonyassaults were statistically significantlyreduced at both treatment locations andtheir neighbouring regions. Direct negativeeffect was seen with robberies. At the 1/4-

mile threshold, felony assaults were statisti-cally significantly reduced at treatment loca-tions. At both distance thresholds, motorvehicle thefts had statistically significant andpositive direct and indirect effects.

Discussion

Cities are embarking on innovative, and asyet unproven, programmes to stabilise neigh-bourhoods and reduce blight and associatedcrime. Youngstown, OH approached vacantlot reuse using a spatially-targeted approach;concentrating efforts in two neighbourhoodsin hopes of maximising potential for stabili-sation in those areas. Non-spatial regressionmodels in our DD analyses found significantand widespread reductions in all types ofcrime outcomes except motor vehicle thefts.Spatial models also found similar crimereductions, except that motor vehicle theftsand theft were not significantly reduced.Overall, based on spatial and non-spatialmodel results, the vacant-lot greening

Table 6. Difference-in-differences estimates of the effect of LOG 2.0 community reuse treatment oncrime.

Outcomevariable


GLSregression

Poissonregression



Indirecteffect

Directeffect

Indirecteffect

Felony assault 20.93*** 1.01 20.27*** 20.70** 20.18** 20.88(0.16) (0.03) (0.07) (0.24) (0.06) (0.48)

Burglary 20.19** 0.83*** 20.02 20.12 20.01 20.14(0.06) (0.04) (0.04) (0.10) (0.04) (0.19)

Robbery 20.22 0.99 20.18* 20.45 20.11 20.24(0.19) (0.06) (0.07) (0.26) (0.06) (0.47)

Theft 0.00 0.97 20.00 20.04 0.00 20.03(0.04) (0.02) (0.02) (0.06) (0.02) (0.13)

Motor vehicle theft 0.79** 0.94 0.32*** 0.87** 0.21** 1.36**(0.27) (0.07) (0.09) (0.27) (0.07) (0.42)


N = 9625 (= 385 groups * 25 times).


Kondo et al. 3291



programme resulted in statistically signifi-cant reductions in felony assaults, burglaries,and robberies. The lot stabilisation treat-ment was associated most consistently andsignificantly with reduction in burglaries,while the community reuse treatmentshowed more consistent and significantreduction in violent crimes.

Comparisons of results from three regres-sion models – the GLS, Poisson and SDMs– indicate that non-spatial linear regressionresults may be subject to bias because coeffi-cients are different in magnitude and signifi-cance than spatial regression results. Also, itis worth noting that the indirect effects areconsistently negative (except for motor vehi-cle thefts), implying that neighbouringregions are not experiencing increases incrime after treatment, but rather are seeingdecreases in crime. One limitation to thisapproach is that indirect effects do notnecessarily indicate displacement of crimesto exact spatial definitions (such as 1/2- or1-mile displacement), but instead indicate adifference between effects at the exact treat-ment site versus neighbouring areas.Nevertheless, these indirect effect measuresfind no evidence that a displacement ofcrimes had occurred but, rather, that theremay have been a beneficial spill-over (diffu-sion) effect in crime reduction.

We also found a significant increase inmotor vehicle thefts at treatment lots com-pared to at control lots. One possible expla-nation for this is that residents felt morecomfortable parking cars near lots after thelots were greened, thus creating a larger sup-ply of targets. Another possibility is thatnewly greened lots may have changed theeconomic structure of the surrounding neigh-bourhood such that more residents were sud-denly able to afford personal automobiles,although this explanation is less likely giventhe short timeframe of the overall study.

According to multiple regression modelresults, vacant-lot greening was associated

most significantly with reductions in burgla-ries. One possible explanation for this find-ing is that greening vacant lots mayinfluence certain types of crime, and specifi-cally crimes that involve advanced planningcompared to other types of crimes. Greeningvacant lots may lead to significant modifica-tion of crime plans by changing cognitivemaps of crime environments and distortingthe ‘awareness space’ that would-be crim-inals may form (Brantingham andBrantingham, 1993), leading to a reductionin crime opportunities.

Certain limitations in our study are worthmentioning. The non-random assignment oftreatment and control units in a quasi-experimental study like this poses a potentiallimitation. However, in the absence of actualrandom assignment, we handled this limita-tion through statistical adjustment. Whileour models controlled for background socio-economic conditions at each site that mightrelate to the treatment itself and influencecrime levels, an additional limitation wasthat our models did not take into accountother potential external influences on crimeoccurrences, such as geographical variationin policing practices that may have changedover time. It is possible that within each citysection compliance with the ordinance maychange policing patterns, so the effects weobserved may be due to a shift in policingand not the change in vacant lots. Despitethis, the DD approach we employed wasable to adequately isolate and detect theeffects of the intervention on crime at treat-ment sites compared to control sites (Meyer,1995).

The findings here provide further evi-dence that greening of vacant lots can reduceviolent crimes (Branas et al., 2011; Garvinet al., 2012), although they are novel in thatwhat was only known to be an effect formajor cities may now also potentially beapplicable to medium-sized cities. TheYoungstown LOG programme, and




especially the community reuse programme,was associated with significant reductions infelony assaults (including aggravatedassaults and homicides) in regression modelsthat accounted for spatial clustering of thegreened lots. While previous studies foundthis type of effect with a traditional ‘cleanand green’ stabilisation treatment, this studyfound the effect to be most pronounced withcommunity-initiated and maintained lotreuse. Perhaps it was the case that newlytreated lots become a rallying point for com-munity members’ interest and energy,strengthening social ties and pride (Sampsonet al., 1997), and ultimately reducing vio-lence and crime.

Nevertheless, as a quasi-experimentalstudy that applied a rigorous spatial analysisof panel data to detect the effect of vacantlots greening, this research contributes to theidea that the form of, and processes behindlandscapes can influence the extent of healthor safety effects (Cameron, 2014).Differences in the number of significantchanges in crime outcomes between the sta-bilisation treatment and the communityreuse treatment suggest that community-initiated vacant-lot greening in medium-sized cities like Youngstown can potentiallyhave a greater impact on reducing seriousviolent crimes. Community reuse lots aremost typically transformed in to communitygardens or orchards, which are uses thatinvite and require residents’ time and care.This type of greening perhaps provides moreopportunity for developing place attachmentor identity (Altman and Low, 1992;Twigger-Ross and Uzzell, 1996) and, assuch, could have a more pronounced effecton violent crimes as opposed to non-violentproperty crimes. More studies exploring therelationship between crimes and greeningform, social interactions and relationshipssupported by different greening forms arewarranted.

Funding

This research was supported by grants from theUSDA Forest Service, Northern ResearchStation and the National Institutes of Health(10.13039/100000002 R01 AA020331).

Note

1. We considered time fixed effects in the model,but ultimately removed them because theresults were not different without them.

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