2 28 11 SCTF Work Session_NIU Crime Study Slide PresentationDubuque 2010 Quantitative
Research Study
on
Crime & Poverty
Feb. 28, 2011 Presentation by NIU CGS
1
2010 Dubuque Project Study Team
Gregory T. Kuhn, Ph.D., Assistant Director, Project Director
Charles Cappell, Ph.D., NIU Associate Professor, Sociology, Project Lead Statistician
Shannon N. Sohl, CPA, Ph.D. (ABD), Principle Associate
Desheng(Ben) Xu, MS, Senior Research Associate
RimaRoy, MS, Urban Studies & Cartography, Senior Research Associate
LoraynOlson, Ph.D., Director of the Northern Illinois University’s Public Opinion Laboratory
Mindy Schneiderman,Ph.D., Assoc. Director of the Northern Illinois University Public Opinion Laboratory
HtunSoe, BA, MA, Research Associate
Liang Tang, MS, Research Associate
Chia-PaoHsu, Ed.D. (ABD), Research Associate
George Graves, MPA, Police Specialist
David Mitchell,MPA, CGS Doctoral Graduate Assistant
Patrick Donnelly, MA, Research Assistant
Thomas Kazmierczak, MS, Research Assistant
Roger Dahlstrom, MS, AICP,Senior Research Associate
Mary Strub,BA,Editor
JanieceBollie, BA, Research Analyst
Feb. 28 th, 2011 Presentation by NIU CGS
2
Project Background
General Project Background:
InAprilof2010,theCityofDubuqueinitiatedthe2010QuantitativeResearch
StudyonCrimeandPovertyinordertoprovideanobjectiveassessmentof
trendsandperceptionsofcrimeinconjunctionwithpoverty,specifically
povertyrelatedelementspertainingtoSection8housing.
Over350,000recordsfromvariousdepartmentsrelatedtotheStudy’sfocus
havebeenusedinthisanalysis.
ThestudyteamwishestoacknowledgetheassistanceandeffortsofCitystaffin
providingtherawdatatotheresearchersforanalysis.
Feb. 28th, 2011 Presentation by NIU
3
CGS
Project Background
Research Questions posed by Dubuque:
D
oestheperceptionofcriminalactivityanditscausesinDubuquematchwhat
isactuallyhappening,andaretheresignificantrelationshipsbetweenincidents
ofcrimeandSection8Housingprogramparticipantsorlocations?
W
ithincategoriesofcrimewithsignificantincreasesinarrests,whatpolicies
orstrategiescaneffectivelydecreasecrime?
I
ftherearecaseswheretherearecommunityperceptionsofincreased
criminalactivitybutnoevidencetosupporttheperception,whatpoliciesor
strategiescaneffectivelyaddresstheconcerns?
Feb. 28th, 2011 Presentation by NIU
4
CGS
Organization & Approach to the Study
Part I: Literature Review & Section 8 Assisted Housing Program Background
Part II: Community Survey & Perception Analysis
Part III: Quantitative Analysis
Section I -Comparative & Trend Analysis
Section II -Local Data Analysis, Spatial & Statistical Analysis
Section III -Synthesis of Quantitative & Perception Analyses
Analysis Of Arrests, Crime Incidents And Housing
o
Contextual Analysis
o
Assisted/Section 8 Housing Program
•
Demographics
•
Quantitative Analysis
o
Discussion of Findings and Recommended Strategies & Policies
Feb. 28th, 2011 Presentation by NIU
5
CGS
Literature
Review
Perception
Analysis
Quantitative
Analysis
Synthesize: Analyze Information Individually & Collectively to Report Findings and Identify Potential Strategies
Part III
Part I
Part II
Synthesis of
Perception
Comparative &
Local Data
Citizen
Analysis &
Trend Analysis
Analysis
Literature Section 8
Perception
Quantitative
ReviewBackground
Survey &
Analysis
Findings
Statistical Analysis of
Spatial Analysis
Dubuque’s Crime
Feb. 28th, 2011 Presentation by NIU CGS
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Feb. 28th, 2011 Presentation by NIU CGS
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Identified 1,440 records with invalid
•
Received 31.7K records (04 to 09 Arrest
•
SSN or missing SSN.
with Adult Charges).
Identified many duplicated records
•
Identified 2, 178 w/o SSN.
•
(each participant’s last record of
Identified 1,372 w/o consistent NameNo
•
previous year are reentered into
& SSN.
next year).
Removed 1,050 bookkeeping duplicates.
•
Analytical File Record Count =
•
Analytical File Record Count= 30.6K
•
126.5K
Received 209K records.
•
Identified 8K records w/o SSN (excluding
•
Identified 55K records with valid
•
non-person entities, ex. State of IOWA).
action types.
Identified about 700 inconsistent records
•
Removed 22K bookkeeping
•
Removed 214 bookkeeping duplicates.
•
duplicate records.
Analytical File Record Count = 209K
•
Subtotal of 33K records up to this
•
point.
Removed an additional 533
•
records (moved out of section 8
prior to 1/1/2006)
Merged Arrest Records (30.7K) & Incident
•
Removed 98 records with extra
•
Records (209K)
(repeated) move-in or move-outs
Removed approximately 90K Records of
•
dates.
non-charge related parties such as witnesses
Restructured the data to assign
•
& suspects or incidents not associated with
Begin Dates and End Dates.
arrestees, victims and complainants or
Analytical File Record Count =
•
referred involvement.
11.3K
Analytical File Record Count = 148.5K
•
Feb. 28th, 2011 Presentation by NIU CGS
8
CS
ommunity urvey
PA
and erception nalysis
Feb. 28, 2011 Presentation by NIU CGS
Community Survey & Perception Analysis
NIU’sPublicOpinionLaboratory(POL)workedwithCitystaffmembersto
developaquestionnaireandundertookarandomsampleofthecommunityfor
interview.
502Dubuqueresidentsaged18oroldercompletedtherandom-digit-dial
telephonesurvey.
InterviewingwasconductedbeginningJune17.
Theoverallresponserate=43.5%.
Toadjustfortheunder-representationofyoungerrespondents,thedatawere
weightedbyage.
Therespondentswereasked61questionsregardingtheirperceptionofthe
CityofDubuque,theirneighborhoodandtheirpersonalorhousehold
characteristics.
Feb. 28, 2011 Presentation by NIU CGS
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POL Survey Respondent Locations
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Findings of Interest
The majority of the sample (84%) had lived in Dubuque for 11 or more years,
with nearly four out of ten (42%) residing in the city for 35 or more years.
Nearly nine out of ten respondents (86%) characterized Dubuque as an
excellent or good place to live.
Among the respondents who had lived in the City for five or more years, nearly
four out of ten (38%) indicated that Dubuque had become a much better or
somewhat better place to live within the past five years.
Nearly six out of ten respondents (57%) indicated that the City of Dubuque was
doing an excellent or good job in addressing safety.
Two-thirds (68%) said the Dubuque police department was doing an excellent
or good job in addressing crime.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Findings of Interest
Thevastmajority(88%)ofrespondentsreportedthattheirneighborhoodwas
anexcellentorgoodplacetolive.
Asmallminorityofrespondents(2%)saidtheyfeltveryorsomewhatunsafein
theirneighborhoodduringthedaytime,whereas11%indicatedtheir
neighborhoodwasveryorsomewhatunsafeatnight.
Themostcommonsafetyissuesratedasbeinga“majorproblem”bymore
than10%ofrespondentswere:
Drugs(33%)
o
ViolentCrimes(23%)
o
GangActivity(22%)
o
UnsupervisedChildren(18%)
o
PropertyCrimes(17%)
o
DomesticViolence(14%)
o
Vandalism(14%)
o
PublicDrinking(14%)
o
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Findings of Interest
Only19%ofrespondentsfeltveryorsomewhatunsafeindowntownDubuque
duringthedaytime,
However,71%indicatedfeelingveryorsomewhatunsafedowntownatnight.
76%indicatedthatcrimewasamajorormoderateprobleminDubuque.
AmongtherespondentswhohadlivedintheCityforfiveormoreyears,
89%saidthatcrimehadincreasedsignificantlyorsomewhatwithinthepast
fiveyears.
1in10(approx.)-12%,indicatedthatcrimewasamajorormoderateproblem
intheirneighborhood.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Age Category
Thereisapositiveordinalrelationshipbetweenageandrespondentratingsof
howbadtheloiteringproblemisinDubuque.Thelikelihoodthatrespondentswill
rateloiteringasamajorproblemincreaseswithage.
Length of Residence in Dubuque
PeoplewholivedinDubuqueatleast21yearsweresignificantlymorelikely
toratecrimeasamoderatetomajorproblem.Peoplewhohavelivedin
Dubuquelongerarelesslikelytofeelsafedowntownintheday.
PeoplewhohavelivedinDubuquelongerarelesslikelytofeelsafe
downtownatnight.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Length of Residence in Dubuque
Thereisapositiveordinalassociationbetweenlengthofresidenceand
perceivingloiteringandunsupervisedchildrenasmoderatetomajor
problemsinDubuque.
Thereisapositiveordinalassociationbetweenlengthofresidenceand
higherlevelsofconcernaboutbeingphysicallyattacked,being
mugged/robbedandhavingone’shousebrokeninto.
However,notalltherelationshipsbetweenlengthofresidenceandfearof
crime/disorderarelinear.
PeoplewhohavelivedinDubuqueatleast21yearsaremorelikelyto
o
ratedrugsanddisruptiveteensasmoderatetomajorproblems.
PeoplewhohavelivedinDubuquelessthan11years,oratleast35
o
years,aremorelikelytoratenoiseasamoderatetomajorproblem.They
arealsomorelikelytobemoderatelytoextremelyconcernedabout
beingsexuallyassaulted.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Education
Peoplewithhigherlevelsofeducationtendtofeelhigherlevelsofsafetyand
lowerlevelsofconcernaboutcrimeanddisorder
Peoplewithbachelor’sdegreesarelesslikelytoratecrimeasamoderateto
majorprobleminDubuque
Thosewithatleastabachelor’sdegreearemorelikelytofeelsafein
o
downtownDubuqueatnight.
Thosewithatleastabachelor’sdegreehavelowerlevelsofconcernabout
o
beingphysicallyattackedormugged/robbed.Theyarealsolessconcerned
abouthavingtheirhomesandcarsbrokeninto.
Socialdisorderproblems,suchasdrugs,loitering,disruptiveteens,noise
o
(significantnegativeordinalrelationship)andpublicdrinkingarelesslikelyto
beratedasmoderatetomajorproblemsbythosewithatleastabachelor’s
degree.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Gender
WomeninDubuquefeellowerlevelsofsafetyandhigherlevelsofconcern
aboutcrimeanddisorderthanmendo.
Womenaremorelikelytoratecrimeasamoderatetomajorproblemin
o
Dubuque.
WomenarelesslikelytofeelsafeindowntownDubuqueinthedayand
o
atnight.
Womenaremorelikelythanmentoratesocialdisorderproblems,such
o
asdrugsandunsupervisedchildrenasmoderatetomajorproblemsin
Dubuque.
Womenhavehigherlevelsofconcernabouthavingone’scarbrokeninto
o
andbeingsexuallyassaulted.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Previous Physical Attack Victimization
PreviousphysicalattackvictimsarelesslikelytofeelsafeindowntownDubuquein
thedayandatnight.
Socialdisorderproblems,suchasdrugs,publicdrinking,unsupervisedchildrenand
disruptiveteensaremorelikelytoberatedasmoderatetomajorproblemsin
Dubuqueamongpreviousattackvictimsthannon-victims.
Previousvictimshavehigherlevelsofconcernaboutbeingphysicallyattacked,
beingmugged/robbed,andhavingone’scarbrokeninto.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Homeownership
Non-homeowners tend to have more fear and concern about crime and disorder
than homeowners do.
Non-homeowners are more likely to rate disruptive teens, public drinking and
o
noise as moderate to major problems in Dubuque.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Cross-Tab Analysis Significant at the .05 level
Previous Damage Or Vandalism To Property
Residents who have had their property damaged or vandalized in the past year are
more likely than those who have not to rate crime as a moderate to major
problem in Dubuque.
Previous property damage victims are less likely to feel safe downtown at night.
Previous property damage victims are more likely than non-victims to rate public
drinking, loitering, disruptive teens, and unsupervised children as moderate to
major problems in Dubuque.
Previous property damage victims have higher levels of concern about being
physically attacked, being mugged/robbed, having one’s car broken into, and
having one’s home broken into.
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Dubuque Survey Results showing Associations
with Feeling Safe Downtown at Night
Non-
Victimization
homeowner
+
-
Length of
Residence
-
Feel Safe
Low Education
Downtown
Level
@ Night
-
Sex: Female
+
Age
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Dubuque Survey Results showing Associations
with Concerns about Crime
Non-
Victimization
homeowner
+
Length of
+
Residence
+
Risk
Low Education
Assessment:
Level
Concern about
+
Crime
Sex: Female
Age
Feb. 28, 2011 Presentation by NIU CGS
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POL Perception Survey
Dubuque Survey Results showing Associations
with Perceptions of Problems
Non-
Victimization
homeowner
+
+
Length of
+
Residence
+
Low Education
Perceptions
Level
of Problems
+
Sex: Female
+
Age
Feb. 28, 2011 Presentation by NIU CGS
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Fear of Crime Model
Three Models Summarized by Bursik
Perceived Perceived
Crime RateCrime Rate
Fear of Fear of
Transmission
of Crime-
CrimeCrime
Related
Information
Integration Integration
into into
Neighborhood Neighborhood
NetworksNetworks
Source: Figure 4-1: The Basic Indirect Victimization Model (Bursik, 1993, 98)
Feb. 28, 2011 Presentation by NIU CGS
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Fear of Crime Model
Three Models Summarized by Bursik
Perceived Perceived
Perceived Perceived
Crime Crime
Erosion of Erosion of
Social Social
RateRate
OrderOrder
Fear of Fear of
SocialSocial
DisorganizationDisorganization
CrimeCrime
Breakdown Breakdown
Perceived
of Cohesionof Cohesion
Symbols of
Disorder
Source: Figure 4-2: The Basic Disorder Model (Bursik, 1993, 103)
Feb. 28, 2011 Presentation by NIU CGS
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Fear of Crime Model
Three Models Summarized by Bursik
Risk of Risk of
Crime Crime
UnfamiliarityUnfamiliarity
VictimizationVictimization
RelatedRelated
StereotypesStereotypes
Fear of Fear of
Racial/EthnicRacial/Ethnic
CrimeCrime
HeterogeneityHeterogeneity
Concern for
CompetitionCompetition
Perceived Perceived
Property
Economic Economic
Values
ViabilityViability
Source: Figure 4-3: The Basic Heterogeneity Model (Bursik, 1993, 105)
Feb. 28, 2011 Presentation by NIU CGS
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Perceptions / Fears of Crime & the Facts of
the Matter Regarding Dubuque Crime
Fearisanemotionalresponse.
Fearisbestconceptualizedasthesubjectiveinterpretationofriskof
victimization–translatedintoemotions.
TypicalQuestion:“Howafraidareyouofbecomingavictimofaphysical
assault.[onecouldaddlocation,time,orduringacommonactivity].
Dubuquesurvey:“HowsafedoyoufeelindowntownDubuqueduringthe
day?”
“HowsafedoyoufeelindowntownDubuqueduringthenight?”
“Howsafedoyoufeelinyourneighborhoodduringtheday?”
“Howsafedoyoufeelinyourneighborhoodduringthenight?”
Feb. 28, 2011 Presentation by NIU CGS
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LR
iterature eview
Feb. 28, 2011 Presentation by NIU CGS
Literature Review &
Section 8 Assisted Housing Program Background
Literature Review Subject Areas
Public Housing, Section 8 and Crime
1.The relationship between the mobility of Section 8 housing vouchers and crime
displacement
2.General relationships between Section 8 housing and crime
Crime in General and Crime in Mid-sized Communities
3.Crime and Social Disorganization
4.General causes of crime in mid-sized communities
5.Contributing factors to the perception of increased crime
Effective Strategies Preventing Crime/Addressing Perception of Crime
6.Effective strategies/policies for preventing crime displacement from one community to
another
7.Effective strategies/policies for preventing crime related to Section 8 housing in general
8.General effective strategies/policies for preventing crime in mid-sized communities
9.Effective strategies/policies to alleviate the perception of crime
Feb. 28, 2011 Presentation by NIU CGS
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Literature Review
Summary of Literature Review Results
Public Housing and Crime
Currently there is no consensus about the effect of public housing on crime and
crime diffusion in the literature.
Depends on the concentration, location, and physical design characteristics of a
o
city’s projects.
Section 8 housing projects that are smaller, more dispersed, garden style, have
o
defensible space, and are located in less resource poor neighborhoods, have not
been found to be linked to crime or crime diffusion (Ronceket al 1981; Galsteret
al 2003; Griffiths and Tita2009; Holzmanet al 2001; Berry and Jones 1995;
Galsteret al 2002).
Large, high rise towers that are concentrated in resource poor neighborhoods do
o
affect crime rates, which diffuse outwardly (Davies 2006; Suresh and Vito 2009).
Feb. 28, 2011 Presentation by NIU CGS
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Literature Review
Summary of Literature Review Results
Policies and Strategies of Preventing Crimes
Community policing, problem-oriented policing, and hot spots policingare
effective crime fighting strategies (Sherman 1986; Xuet al 2005; Mazerolleet
al 2000; Braga et al 1999; Braga 2006; Braga and Bond 2008).
Community policing has been found to reduce fear through improving police
o
relations with citizens and reducing social and physical incivilities (Rohand Oliver
2005; Adams et al 2005; Hinkle and Weisburd2008).
Reducing social and physical disorder is the most reoccurring theme in the
o
literature about lessening fear of crime.
Other important disorder and fear reduction strategies include problem oriented
o
policing (Mazerolleet al 2000; Braga et al 1999), hot spots policing (Braga 2006;
Braga and Bond 2008), and broken windows policing (provided it is combined
with community policing).
Feb. 28, 2011 Presentation by NIU CGS
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Literature Review
Summary of Literature Review Results
Mid-sized Communities
Crime correlates in mid-sized cities are similar to those in larger cities. Areas
with high levels of concentrated disadvantages (high levels of poverty,
unemployment, female headed households, and minority concentrations)
tend to also have high violent crime rates (Ackerman and Murray 2004).
Another cause of mid-size city crime is geographic based racial segregation
(Shihadeh2004).
As the number of police per 10,000 people increased, mid-sized city crime
decreased (Goodman, 1997; Goodman and Mann, 2005).
Allowing off-duty officers to drive squad cars provides an added deterrent
effect, thereby lowering crime rates (Goodman and Mann, 2005).
Feb. 28, 2011 Presentation by NIU CGS
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Literature Review
Summary of Literature Review Results
Effective Strategies
ClosedCircuitTelevision(CCTV)camerastobemoderatelyeffectiveatcrimecontrol.
WelshandFarrington(2009)conductedameta-analysisof44studies.TheStudy’s
o
combinedeffectsizerevealedthatCCTVreducedoverallcrimeby16%,whichisa
modestbutstatisticallysignificantdecline.
UsingCCTVinparkinglotsresultedina51%decreaseincrime.TheuseofCCTVin
o
city/towncenters,publictransportationsystems,andinpublichousingareaswas
notfoundtocauseastatisticallysignificantreductionincrime(Welshand
Farrington,2009).
Curfews
Curfewshavelongbeenapopularcrimecontrolmethod.However,thereislittle
o
evidencetosuggestthatcurfewseffectivelyreducejuvenilecrime(Adams,2003).
Thereissomeevidencetosuggestthatdrivingcurfewsdecreasetherateof
o
automobilecrashesbyreducingteenagelicensure,becausedrivingbecomesless
appealingtoteenagers(Adams,2003).
Feb. 28, 2011 Presentation by NIU CGS
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Literature Review
Summary of Literature Review Results
Effective Strategies
No two communities are alike, nor have any of the authors been able to generalize
their Study’s findings.
Each municipality’s existing landscape of crime, demographics and policies must be
taken into consideration, including the legal, fiscal, and technical feasibility as well as
the political acceptance of each strategy and/or combination of strategies.
The study team strongly encourages anyone interested in the topic of crime,
perceptions of crime, contributing factors to crime, crime and public housing or
community policing and policing strategies to read through entire summary of the
literature review section of this report, and the accompanying bibliography.
Feb. 28, 2011 Presentation by NIU CGS
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Literature Review Synthesis
Hypothetical Model of Crime –Neighborhood/Community Level
Defensible SpaceDefensible Space
+
+
+
Collective Efficacy for Collective Efficacy for
Social Networks / Social Networks /
Social ControlSocial Controlv.v.
Social CapitalSocial Capital
Social DisorganizationSocial Disorganization
_
_
_
_
Cumulative Cumulative
+
Crime RateCrime Rate
DisadvantageDisadvantage
+
+
CollectiveCollective
+
Fear of CrimeFear of Crime
+
+
CollectiveCollective
_
_
Crime Rate of Crime Rate of
IncivilitiesIncivilities
_
Spatially Spatially
_
Proximate AreasProximate Areas
Quality of Physical Quality of Physical
EnvironmentEnvironment
Feb. 28, 2011 Presentation by NIU CGS
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(e.g. neg. Broken Windows)(e.g. neg. Broken Windows)
QA
uantitative nalysis
Feb. 28, 2011 Presentation by NIU CGS
Comparative & Trend Analysis
Population estimates derived from U.S. Census Bureau
For cities with University police –University data was combined with city data
(Ames and Iowa City)
Analyzed Dubuque’s UCR property and violent crime rates in the context of nine
other Iowa cities with comparable populations (a traditional cohort of
Dubuque’s): Ames, Cedar Rapids, Council Bluffs, Davenport, Des Moines, Iowa
City, Sioux City, Waterloo, and West Des Moines.
Crime rates for the 6-year period of 2004-2009 received directly from Iowa’s
DPS (Dubuque data directly from the City) were included in the analysis.
FBI cautions users when comparing multiple agencies’ UCR data. Various local
policies and procedures could lead to material differences across municipal UCR
reporting.
Feb. 28, 2011 Presentation by NIU CGS
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Comparative Data
Dubuque Crime Patterns v. Major Iowa Cities
Current Population Estimates
(Year 2009)
250,000
198,460
200,000
150,000
128,182
101,306
100,000
82,794
68,903
66,896
59,911
57,222
56,814
56,503
50,000
-
Source: U.S. Census Bureau, Population Division; Release Date June 2010
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Dubuque’scrimeprofileisnotuniquelydifferentfromtheothercities.
Identifiedsomedifferenceswithregardtooffensesclassifiedasviolentcrime,
usingthestandardUCRcodingapproach.
ReportingprotocolswithinthePoliceDepartmentprovidedbytheCityindicate
thatthe“overreporting”ofincidentsasaggravatedcouldbesubstantialandis
likelyimpactingtheanalysisofdata.
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysi
s
Total UCR Crimes includingSimple Assaultsper 10K
(2004 to 2009)
AmesCedar RapidsCouncil BluffsDavenport
1200
1000
Total UCR Crime incl. Sim. Assault per 10000
800
600
400
Des MoinesDubuqueIowa CitySioux City
1200
1000
800
600
400
20042006200820102004200620082010
WaterlooWest Des Moines
1200
1000
800
600
400
20042006200820102004200620082010
YEAR
Total UCR Crimes including Simp. Assault per 10000 2004-2009
Source File: city_crime_rate_long_panel_10_5_2010
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Violent Crimes per 10K
(2004 to 2009)
AmesCedar RapidsCouncil BluffsDavenport
150
Violent Crime Rate per 10K Population
100
50
0
Des MoinesDubuqueIowa CitySioux City
150
100
50
0
4681046810
WaterlooWest Des Moines
150
100
50
0
4681046810
Year
Figure 9: 6-Year Trends of UCR Violent Crimes per 10000 2004-09
Source File: city_crime_rate_long_panel_11_5_2010.dta
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Violent Crimes includingSimple Assaultper 10K
(2004 to 2009)
AmesCedar RapidsCouncil BluffsDavenport
Violent crime rate per 10K incl simple assault 400
300
200
100
0
Des MoinesDubuqueIowa CitySioux City
400
300
200
100
0
4681046810
WaterlooWest Des Moines
400
300
200
100
0
4681046810
Year
Figure 10: Violent Crime Rates per 10000 including Simple Assault, 2004-2009
Source File: city_crime_rate_long_panel_11_5_2010.dta
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Aggravated Assaults per 10K
(2004 to 2009)
6-Year Ave. Agg. Assaults per 10000
84
80
71
66
60
44
41
40
34
24
23
21
20
12
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 1: Average Aggrevated Assaults per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_rate_long_panel_11_5_2010.dta
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Homicides per 10K
(2004 to 2009)
6-Year Ave. Homicides per 10000
.5
.45
.4
.37
.33
.31
.3
.26
.25
.21
.2
.18
.2
.092
.1
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 2: Average Homicides per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Rapes per 10K
(2004 to 2009)
15
15
6-Year Ave. Rapes per 10000
10
6.3
6.1
5.4
5.1
5.1
4.7
4.6
5
3.2
3.1
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 3: Average Rapes per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
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Comparative & Trend Analysis
Robberies per 10K
(2004 to 2009)
25
6-Year Ave. Robberies per 10000
25
20
16
15
14
13
9.4
10
7
5.6
4.4
5
2.8
2.4
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 4: Average Robberies per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
47
Comparative & Trend Analys
is
Simple Assaults per 10K
(2004 to 2009)
6-Year Ave. Simple Assaults per 10000
250
238
200
179
167
151
150
120
118
100
90
77
45
50
30
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 5: Average Simple Assaults per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_rate_long_panel_11_5_2010_dta
Feb. 28, 2011 Presentation by NIU CGS
48
Comparative & Trend Analysis
Aggravated Assaults per 10K
(2004 to 2009)
6-Year Ave. Agg. Assaults per 10000
84
80
71
66
60
44
41
40
34
24
23
21
20
12
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 1: Average Aggrevated Assaults per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_rate_long_panel_11_5_2010.dta
Feb. 28, 2011 Presentation by NIU CGS
49
Comparative & Trend Analysis
Total Assaults per 10K
(2004 to 2009)
6-Year Ave. Tot. Assaults per 10000
308
300
213
208
195
200
186
161
139
114
100
68
42
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 6: Average Total Assaults per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
50
Comparative & Trend Analysi
s
UCR Auto Thefts per 10K
(2004 to 2009)
6-Year Ave. UCR Auto Thefts per 10000
103
100
80
60
50
40
34
27
25
24
17
20
12
12
11
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 13: Average UCR Auto Thefts per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
51
Comparative & Trend Analysis
UCR Burglaries per 10K
(2004 to 2009)
151
6-Year Ave. UCR Burglaries per 10000
150
134
126
108
100
94
91
85
79
54
44
50
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 14: Average UCR Burglaries per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
52
a22
Comparative & Trend Analysis
UCR Property Crimes per 10K
(2004 to 2009)
6-Year Ave. UCR Property Crimes per 10000
830
800
664
604
600
489
475
401
400
381
349
324
280
200
0
AmesCedar RapidsCouncil BluffsDavenportDes MoinesDubuqueIowa CitySioux CityWaterlooWest Des Moines
Figure 16: Average UCR Property Crimes per 10000 2004-2009: 10 Iowa Cities
Source File: city_crime_long_panel_11_5_201.dta
Feb. 28, 2011 Presentation by NIU CGS
53
Slide 53
a22 Need Updates
a119975, 1/13/2011
a23
Comparative & Trend Analysis
Property Crimes per 10K
(2004 to 2009)
AmesCedar RapidsCouncil BluffsDavenport
1000
800
Property Crime Rate per 10K Population
600
400
200
Des MoinesDubuqueIowa CitySioux City
1000
800
600
400
200
20042006200820102004200620082010
WaterlooWest Des Moines
1000
800
600
400
200
20042006200820102004200620082010
YEAR
Graphs by City
Feb. 28, 2011 Presentation by NIU CGS
54
Slide 54
a23 Need Updates
a119975, 1/13/2011
a55
Comparative & Trend Analysis
Violent Crimes
Percent Percent
Families Households
Iowa City & Place CodeMedianGINIBlackWhite
Below Below Inequal
FamilyPer capitaPer capita
Poverty Poverty
Income.IncomeIncome
LevelLevelIndex
Ames (1901855)6.76152922.8458773052.4812160623924
Cedar Rapids (1912000)8.36382112.0319863528.4381449728473
Council Bluffs (1916860)11.36481013.5936652187.399859521965
Davenport (1919000)11.22597015.0712954668.4401235825373
Des Moines (1921000)11.10226014.1134654820.4471460726984
Dubuque (1922395)9.82307512.9257154997.444709524396
Iowa City (1938595)7.89516423.3137772571.5111275726801
Sioux City (1973335)11.61340014.3042553664.4241238722805
Waterloo (1982425)12.26285014.8232249217.4331335924765
W. Des Moines (1983910)4.5828707.3781183800.4481083738818
10 City Average9.49957515.0401361250.4461281026430
Feb. 28, 2011 Presentation by NIU CGS
55
Slide 55
a55 Need Updates
a119975, 1/13/2011
Comparative & Trend Analysis
Poverty Indicators & UCR Violent Crimes
Poverty Indicators and UCR Violent
•
Median
Crime Rates Across 10 Iowa Cities
Household Income
Median Family
--
Household Income
Linear Trend of UCR
Violent Crime Rate
Percent Families
+
Ave 6 Year UCR
Below Poverty Level
The Linear Trend
Violent Crime Rate
Lines for each city
Income Inequality
are not equal. But
(GINI)
none of the Poverty
Indicators could
Black per Capita
explain any of the
variation in the
Income
-
slopes.
White per Capita
Income
Feb. 28, 2011 Presentation by NIU CGS
56
local data analysis
Local Data Analysis
Thefollowingslidesdepictinformationobtainedfromfilesreceivedeither
directlyfromtheCityorfromotherrelevantandreliablesources(i.e.,
demographicdataobtainedfromtheU.S.CensusBureau).
Itisimportanttonotethatchartsorgraphsinthefollowingslidesare
exclusiveofjuvenilearrestcounts;withtheexceptionofafewjuvenile
arrests(chargedasadults)thatwereincludedinthefileforwardedby
DubuquetoCGS.
Note-Ajuvenileisdefinedasapersonunder18yearsofage.
Feb. 28, 2011 Presentation by NIU CGS
57
a25
local data analysis
Comparison of Authorized Section 8 Participants to
Section 8 Housing Units
(Years 2006 through June 2010)
Section 8 Participants & Section 8 Housing Units
Monthly Counts for Years 2006 -2010
2750
2010
2007
2008
2009
2006
2550
2350
2150
Section 8 Participants
1950
1750
1550
1350
1150
Section 8Housing Units
950
750
1357911135791113579111357911135
Source: Dubuque's Housing datafile.
CGS File:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\Section 8 Utilization Reconciliation_with Graphs.XLSX
Feb. 28, 2011 Presentation by NIU CGS
58
Slide 58
a25 Need Updates
a119975, 1/13/2011
local data analysis
Authorized Section 8 Participants per Capita –Monthly
Average Authorized Section 8 Participants per Capita vs. Total
Authorized Section 8 Participants per Capita
(Including Turnover)
Section 8 Particpants per Capita:
Monthly Average & Total (Including Turnover)
6.0%
Monthly Avg
Section 8
5.0%
Particpants per
Capita
4.0%
Total Section 8
3.0%
Participants per
Capita
2.0%
1.0%
0.0%
20062007200820094-Year Avg
Monthly Avg Section 8 Particpants per Capita
4.2%4.1%3.9%4.2%4.1%
Total Section 8 Participants per Capita
5.3%5.4%4.9%5.4%5.2%
Source: Dubuque's Housing DataFile.
CGS File:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\Section 8 Utilization Reconciliation_with Graphs.XLSX
Feb. 28, 2011 Presentation by NIU CGS
59
local data analysis
Dubuque’s Arrestee and Incident Trends:
Years 2006 to 2009
Dubuque's Arrestee & Incident Counts
9,000
8,000
# of Unique
7,000
Incidents
# of Total Arrestees
6,000
5,000
# of Unique
Arrestees
4,000
3,000
2,000
2006200720082009
# of Unique Incidents
8,563 8,630 8,701 8,231
# of Total Arrestees
3,962 3,977 4,100 3,883
# of Unique Arrestees
2,804 2,774 2,863 2,718
Source: Dubuque Arrests & Incidentsata file.
CGSFile Name:S:\common\Dubuque\Report -Final\Charts_Tables_Figures\
Final Frequency Stats\INDIVIDUAL_FREQ_TABLE_TOTAL_INCIDENTS_VALIDATION.xlsx
Feb. 28, 2011 Presentation by NIU CGS
60
local data analysis
Race Composition of Dubuque’s Population Compared
to Iowa’s and the Nations Race Compositions
(3-Year Average)
Race Composition of Population:
3-Yr Avg (2006 -2008)
120.0%
13.3%
4.8%
Others
3.6%
100.0%
80.0%
White
60.0%
74.3%
94.6%
92.7%
Black or African
40.0%
American
20.0%
0.0%
12.3%
2.4%
1.7%
DubuqueIowaNation
Source: AmericanCommunity Survey (ACS); 3-Yr Avg for 2006 -2008
CGS File Name: S:\common\Dubuque\Report -Final\Charts and Tables\dbq_arrest_race_census.xlsx
Feb. 28, 2011 Presentation by NIU CGS
61
local data analysis
Adult Arrests by Race as a Percent to Total Arrests
(3-Year Average)
Adult Arrests by Race as a % to Total Arrests:
3-Yr Avg (2006 -2008)
120.0%
2.3%
2.3%
0.8%
Other Arrests as a % to
100.0%
Total Arrests
80.0%
Whites Arrested as % to
Total Arrests
69.8%
60.0%
79.3%
78.5%
40.0%
Blacks or African Am as
a % to Total Arrests
20.0%
27.9%
19.9%
19.3%
0.0%
DubuqueIowaNation
Source: AmericanCommunity Survey (ACS); 3-Yr Avg for 2006 -2008
CGS File Name: S:\common\Dubuque\Report -Final\Charts and Tables\dbq_arrest_race_census.xlsx
Feb. 28, 2011 Presentation by NIU CGS
62
local data analysis
Comparison of Dubuque’s Authorized Section 8 Participants per Capita
to Authorized Section 8 Unique Arrestees as a Percent to
(Total City)
Dubuque’s Unique Arrestees
Comparison of Dubuque's Authorized Section 8 Participants per
Capita (Total City) to Authorized Section 8 Unique Arrestees as a
Percent of Dubuque's Unique Arrestees
8.0%
Sect 8
7.0%
Participants
per Capita
6.0%
(Represented
as percent of
5.0%
total City
4.0%
population)
3.0%
Sect 8 Share
of Dubuque's
2.0%
Unique
Arrestees
1.0%
0.0%
2006200720082009AVG
Sect 8 Participants per Capita
(Represented as percent of total
5.3%5.4%4.9%5.4%5.2%
City population)
Sect 8 Share of Dubuque's Unique
5.8%5.3%5.4%6.8%5.8%
Arrestees
Source: Dubuque'sArrest Data , Housing Data Files and U.S. Census Bureau.
CGS Data File: C:\Documents and Settings\y13gtk1\My Documents\Dubuque 2010 Study\
Dubuque Sec 8 Participants as % of City Population Compared to Unique Arrest.xlsx
Feb. 28, 2011 Presentation by NIU CGS
63
local data analysis
Comparison of Dubuque’s Authorized
Section 8 Unique Arrestees per Capita
(Authorized Section 8 Population)
to non-Section 8 Unique Arrestees per Capita
(non-Section 8 Population)
Comparison of Dubuque's
Unique Authorized Section 8 Arrestees Per Capita (% to Ttl Section 8 Participants) to
Non-Section 8 Unique Arrestees Per Capita (% to Ttl Non-Section 8 Population)
7.0%
6.0%
5.6%
6.0%
5.5%
5.4%
4.8%
5.0%
Unique Authorized Sect 8 Arrestees % to
3.5%
4.0%Sect 8 Population
3.5%
3.4%
3.4%
3.4%
Unique non-Sect 8 Dubuque Arrestees %
3.0%
to non-Sect 8 Dubuque Population
2.0%
1.0%
0.0%
20062007200820094-Year Avg
Source: Dubuque Housingand Arrest Data Files and U.S Census Bureau.
CGS File Name:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\S8 to NonS8 Comparisons.xls
Feb. 28, 2011 Presentation by NIU CGS
64
local data analysis
Comparison of Dubuque’s Authorized Section 8 Unique
Victims per Capita to non-Section 8
(Authorized Section 8 Population)
Unique Victims per Capita
(non-Section 8 Population)
Comparison of Dubuque's
Unique Authorized Section 8 Victims Per Capita (% to Ttl Section 8 Participants) to
Non-Section 8 Victims Per Capita (% to Non-Section 8 Population)
6.5%
7.0%
6.3%
5.9%
5.5%
6.0%
5.5%
5.6%
5.6%
5.4%
5.0%
5.0%
5.0%
Unique Authorized Sect 8 Victims % to
4.0%
Sect 8 Population
3.0%
Unique non-Sect 8 Dubuque Victims % to
non-Sect 8 Dubuque Population
2.0%
1.0%
0.0%
20062007200820094-Year Avg
Source: Dubuque Housingand Arrest Data Files and U.S Census Bureau.
CGS File Name:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\S8 to NonS8 Comparisons.xls
Feb. 28, 2011 Presentation by NIU CGS
65
local data analysis
Comparison of Dubuque’s Authorized Section 8 Unique
Complainants per Capita to non-
(Authorized Section 8 Population)
Section 8 Unique Complainants per Capita
(non-Section 8 Population)
Comparison of Dubuque's
Unique Authorized Section 8 Complainants Per Capita (% to Ttl Section 8 Participants) to
Non-Section 8 Complainants Per Capita (% to Non-Section 8 Population)
7.0%
6.4%
6.2%
6.0%
6.2%
6.0%
6.1%
5.9%
5.7%
6.0%
5.6%
5.3%
5.0%
Unique Authorized Sect 8 Complainants %
4.0%
to Sect 8 Population
Unique non-Sect 8 Dubuque Complainants
3.0%
% to non-Sect 8 Dubuque Population
2.0%
1.0%
0.0%
20062007200820094-Year Avg
Source: Dubuque Housingand Arrest Data Files and U.S Census Bureau.
CGS File Name:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\S8 to NonS8 Comparisons.xls
Feb. 28, 2011 Presentation by NIU CGS
66
local data analysis
Dubuque’s Authorized Section 8 Unique Arrestees, Unique Victims & Unique
Complainants: % to Dubuque’s Authorized Section 8 Participants
Dubque's Authorized Section 8 Unique Arrestees, Unique Victims & Unique Complainants:
% to Dubuque's Authorized Section 8 Participants
Sect 8 Unique Arrestees %
to Sect 8 Unique
7.0%
Participants
6.0%
Sect 8 Unique Victims %
to Sect 8 Unique
5.0%
Participants
Sect 8 Unique
4.0%
Complainants % to Sect 8
Unique Participants
3.0%
2.0%
1.0%
0.0%
4-
Year
2006200720082009
Avg
Sect 8 Unique Arrestees % to Sect 8 Unique
5.5%4.8%5.6%6.0%5.4%
Participants
Sect 8 Unique Victims % to Sect 8 Unique
6.5%6.3%5.6%5.0%5.9%
Participants
Sect 8 Unique Complainants % to Sect 8 Unique
5.9%6.0%5.3%5.6%5.7%
Participants
Source: DubuqueArrests, Incidents & Housing Data Files .
CGS File Name:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\S8 to NonS8 Comparisons.xls
Feb. 28, 2011 Presentation by NIU CGS
67
local data analysis
Dubuque’s Authorized Section 8 Total Arrestees, Total Victims & Total
Complainants: % to Dubuque’s Authorized Section 8 Participants
Dubuque's Authorized Section 8 Total Arrestees, Total Victims & Total Complainants:
% to Dubuque's Authorized Section 8 Participants
Sect 8 Total
12.0%
Arrestees % to
Sect 8 Unique
10.0%Participants
Sect 8 Total
8.0%
Victims % to
Sect 8 Unique
6.0%
Participants
Sect 8 Total
4.0%
Complainants %
to Sect 8
2.0%
Unique
Participants
0.0%
20062007200820094-Year Avg
Sect 8 Total Arrestees % to Sect 8 Unique
8.9%7.8%8.3%8.9%8.5%
Participants
Sect 8 Total Victims % to Sect 8 Unique Participants
10.0%9.7%8.0%7.1%8.7%
Sect 8 Total Complainants % to Sect 8 Unique
8.5%9.9%7.5%8.1%8.5%
Participants
Source: DubuqueArrests, Incidents & Housing Data Files .
CGS File Name:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\S8 to NonS8 Comparisons.xls
Feb. 28, 2011 Presentation by NIU CGS
68
local data analysis
Dubuque’s Authorized Section 8 Arrestees as a % of All Arrestees:
Unique Authorized Section 8 Arrestees % to All Unique Arrestees vs.
stees
Total Authorized Section 8 Arrestees % to Total Arre
Dubuque's Authorized Section 8 Arrestees as a % of All Arrestees:
Unique Authorized Section 8 Arrestees vs. Total Authorized Section 8 Arrestees
8.0%
7.1%
6.4%
6.8%
6.7%
7.0%
6.0%
5.7%
5.8%
5.8%
6.0%
5.4%
5.3%
5.0%
Sect 8 Unique Arrestees % to Unique
Arrestees
4.0%
Sect 8 Total Arrestees % to Total Arrestees
3.0%
2.0%
1.0%
0.0%
20062007200820094-Year Avg
Source: Dubuque Housingand Arrest Data Files.
CGS File Name:S:\common\Dubuque\Report -Preliminary\Charts_Tables_Figures\Final Frequency Stats\S8 to NonS8 Comparisons.xls
Feb. 28, 2011 Presentation by NIU CGS
69
Quantitative Analysis
Crime and Section 8 Housing mapping
Overtwodozenmapshavebeengeneratedtogeographicallyplotkey
elementsofthedataandresearchquestions.
Severalofthemapsdepictthedataatboththeindividualandblockgroup
level.
Feb. 28, 2011 Presentation by NIU CGS
70
Crime Incident Mapping:
A spatial analysis of the location of unique
crime incidents from 2006 to 2009
Feb. 28, 2011 Presentation by NIU CGS
71
Arrest Mapping:
The map depicts the home address of the
arrestee (as reported) for the years 2006
to 2009.
Feb. 28, 2011 Presentation by NIU CGS
72
Authorized Section 8 Housing Mapping:
This map shows the distribution of
authorized Section 8 housing for each of
the years 2006 through 2009.
Feb. 28, 2011 Presentation by NIU CGS
73
Associations of S8 Location Status:
Incidents, Victims, Arrestees
Non S8
S8
Victim(s)
S8
Arrestee(s)
S8
Arrestee(s)
Arrestee(s)
S8
Incident Event
S8
Location
Section 8 and
Incident Event
Location
Crime Clusters
Section 8 and
Non S8
Incident Event
Crime Clusters
Location
Non S8
Incident Event
Non S8
Location
Arrestee(s)
S8
S8 Section 8 and
Victim(s)
Non S8
Victim(s)
Victim(s)
Crime Clusters
Feb. 28, 2011 Presentation by NIU CGS
74
Associations of S8 Location Status:
Incidents, Victims, Arrestees
Arrestee(s)
P
ersonal Addresses &
Event Locations
C
rime Types
Crime
T
ime of Event
Incident
Victim(s)
Complainant(s)
Feb. 28, 2011 Presentation by NIU CGS
75
Associations of S8 Location Status:
Incidents, Victims, Arrestees
Authorized S8, Adjusted S8 Participants &
Adjusted Dubuque City Population Victimization Rates
14.16
14.13
S8 named
13.02
matched only
12.59
11.19
9.97
9.73
Adjusted S8
8.69
8.61
8.188.18
8.15
8participants at
7.64
7.05
risk
Adjusted
Dubuque City
population at
risk2
2006200720082009Total
From Table 19--Victimization Rates (Persons & Businesses), Including Multiple Victims Per Incident
Note: victimization rates presented here are including multiple victims (persons & businesses) per incident
Feb. 28, 2011 Presentation by NIU CGS
76
Associations of S8 Location Status:
Incidents, Victims, Arrestees
Authorized S8, Adjusted S8 Participants &
Adjusted Dubuque City Population Arrest Rates
13.64
S8 named
12.08
matched only
11.78
11.61
11.27
8.89
8.87
Adjusted S8
8.47
8.32
residents at risk
7.77
5.05
4.89
4.86
4.83
4.66
Adjusted Dubuque
City Population
2006200720082009Total
From Table 20: Arrest Rates, Including Multiple Arrests Per Incident
Note: arrest rates presented here are including multiple arrests per incident
Feb. 28, 2011 Presentation by NIU CGS
77
Associations of S8 Location Status:
Incidents, Victims, Arrestees
Total Unique Crime Incidents 2006-2009
by Incident Location
26.35
19.99
Dubuque city S8
15.27
13.45
Dubuque City
11.96
11.71
Non S8
10.56
10.56
8.94
7.95
7.46
7.04
6.71
6.25
5.71
5.11
4.85
4.56
4.42
4.29
3.59
1.95
0.91
0.42
From Table 16: Total Unique Crime Incidents 2006-2009 by Incident Location
Note: Aggravated assault is classified in category 1: UCR violent; simple assault is classified in category 3: non-UCR violent. A hierarchy rule was used to classify
incidents with multiple charges to the higher ranking crime using a CGS ranking score for 40 categories of crime that were further collapsed to these 12 categories.
Feb. 28, 2011 Presentation by NIU CGS
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Associations of S8 Location Status:
Incidents, Victims, Arrestees
Summary of Rate comparisons
Thedistributionsof12typesofcrimebyS8locationandnon-S8locationshow
thatviolentcrimesmakeupnearlytwiceashighapercentageofcrimes
occurringatS8addressesthanthesharereportedwithnon-S8addresses.
TheonlyothertypeofcrimethatoccursatamuchhigherproportionatS8
addressesthannon-S8addressesinvolveslocalordinancecharges;theS8rate
isoverfivetimeslargerthanthenon-S8rate.
Propertycrimes,DUIs,drugandalcoholrelatedcrimes,andcivildisorders
makeupalowerproportionofthecrimesatS8locationsthantheydoatnon-
S8locations.
ThetypesofcrimecommittedatS8locationsdon’tchangemuchoverthe
period2006-2009withonenotableexception;thereisalargeincreaseinshare
ofcrimesclassifiedasviolationsoflocalordinancesthatoccurredin2008and
2009.
Feb. 28, 2011 Presentation by NIU CGS
79
Associations of S8 Location Status:
Incidents, Victims, Arrestees
LoglinearAnalysis of Dubuque’s Crime and Arrest Data
The loglinearapproach is a statistical tool used to identify
multi-way associations between three or more variables.
In loglinearanalysis, there are no dependent or independent
variables.
The test simply looks for interactions or associations that
might exist between the selected variables (for a discussion of
LoglinearAnalysis see Knokeand Burke’s LoglinearModels,
1980).
Feb. 28, 2011 Presentation by NIU CGS
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Associations of S8 Location Status:
Incidents, Victims, Arrestees
LoglinearAnalysis of Dubuque’s Crime and Arrest Data
Inthisanalysis,studyingtheassociationsofS8address
statuses(S8or~S8)ofincidents,victims,andarresteeswill
informuswhethercrimeisdiffusedacrossS8addressesor
circumscribedbyS8addresses.
noassociations-addressstatusrandomlydistributed;
negativeassociations-crimediffusesinsomewayacross
S8status;
positiveassociation-crimeiscircumscribedbyS8status.
Feb. 28, 2011 Presentation by NIU CGS
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Associations of S8 Location Status:
Incidents, Victims, Arrestees
Random
Associations among S8 Locations:
Victim, Arrestee, and Incident Addresses
S8
Location
of Crime
Incident
S8 S8
Location
Location
of
of Victim
Arrestee
Picture the above circles as reflecting the size of the probability or proportions of each aspect of the crimes recorded
in Dubuque implicating S8 residents. If totally random, the proportions will not be linked. For example, focusing on the
Victim / Arrestee association, the absence of a statistical association indicates that the chances we observe a victim in
S8 housing is unaffected by whether the arrestee (perpetrator) is from a S8 residence, and vice versa
.
Feb. 28, 2011 Presentation by NIU CGS
82
Associations of S8 Location Status:
Incidents, Victims, Arrestees
Statistical
Associations found among S8 Locations:
Victim, Arrestee, and Incident Addresses
S8
Location
+
of Crime
+
Incident
S8 S8
Location
Location
of
+
of Victim
Arrestee
Results showed that positive associations were present between all of the two-way associations. This indicates, for
example, that the probabilities of observing both a S8 victim and a S8 arrestee from a S8 address were higher than
expected if location was random.
S8 victimization rates, arrest rates, and location rates circumscribe crime to S8 locations more than diffuse them to
non-S9 addresses.
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Associations of S8 Location Status:
Incidents, Victims, Arrestees
Summary of Rate comparisons
WhentheS8“persons”(whogaveS8addressesinthecrimeincidentfilebut
werenotmatchedtotheAuthorizedSection8Housingparticipants’fileby
personalID)areaddedtogetherwiththeofficial/authorizedS8residents,the
ratesofvictimizationareoverall1.60timesgreaterforS8residentsthannon-
S8residentslivinginDubuque;withthecombinedaddresses,thearrestrates
forS8residentsare,overall,2.49timesashighastheratesfornon-S8
DubuqueCityresidents.
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LoglinearFindings
Summary of Associations (loglinear)
Results show statistically significant, strong, and positive two-way associations
between all three S8 categories indicating an event location, victim, and
arrestee address.
The probability that a victim with a S8 address was victimized at a S8
location is greater than expected on the basis of randomness;
The probability that a victim with a S8 address was victimized by an
arrestee with a S8 address is greater than expected on the basis of
randomness;
The probability that an arrestee had a S8 address when the crime
occurred at an S8 address is greater than expected on the basis of
randomness.
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Spatial Autocorrelation Analysis overview
The purposes of this analysis were to depict the extent of spatial
autocorrelation among crimes at the block level and to display the locations of
areas with high levels of spatial correlation within the City of Dubuque.
Selected two years of data for comparative purposes in the spatial correlation
analysis.
The two years selected were 2006 and 2008 because of the completeness and
quality of the incident data for these years.
The results presented should be compared to other results with different
specifications. The areal correlation procedure used calls for specification of
how neighbors of any specific block are defined. One can choose to only
designate blocks that are geographically contingent with a block as its
neighbors, or some other rule.
Explanation of crime patterns across geographical areas should also take into
account the social and economic characteristics of those areas along with any
spatial dependence before firm conclusions are drawn.
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Spatial Correlation of 2008 UCR Violent Crimes
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Spatial Correlation of 2006 UCR Violent Crimes
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Spatial Correlation of 2008 UCR Property Crimes
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Spatial Correlation of 2006 UCR Property Crimes
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Spatial Autocorrelation Findings
Thelevelofspatialcorrelationofblocks’crimeratesoverallislowto
•
moderateinDubuque.
ThecrimesexhibitingthehigherlevelsofspatialdependencywereUCR
•
Violent,CivilDisorder,andDrugs&Alcohol(2008).
UCRViolentandNon-UCRViolentcrimesappearedtodiffuse,especiallyin
•
downtownblocks:someblockswithloworaverageratesofviolentcrime
in2006thatneighboredblockswithhighratesbecameblockswithhigher
thanaverageratesin2008.
UCRPropertycrimes,inthedowntownarea,exhibitedtheopposite
•
tendency,tobecomemoreaveragewhenneighboringblockswithhigh
ratesin2006.
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Hot Spot Analysis Overview
Hotspot analysis is a useful, summary descriptive analysis to increase
understanding of the patterns of concentrated distributions of crime in spatial
areas.
Explanation of crime patterns across geographical areas should also take into
account the social and economic characteristics of those areas along with any
spatial dependence before firm conclusions are drawn.
The results reported here are conditional on the specifications the statistician
imposes on the analytical tools to identify Hotspots and are dependent upon
the algorithm choices made by the analysts.
Only after running several models using different specifications can robust
findings emerge. Readers should treat these results as exploratory only, and
are conditional on how the analysis was conducted.
The units of analysis were the unique crime incidents occurring in Dubuque
between 2006 and 2009.
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Hot Spot Analysis Findings
Themapsrevealthatgiventheparametersettingsweimposedonthe
hotspotanalysis,thereweremanymoreSection8hotspotsthancrime
hotspots.
Itisalsoclearthatmanysection8hotspotsdonotoverlap,norarethey
contingentwithcrimehotspots.
ThepresenceofaSection8hotspotinalocationisnotanecessarynor
sufficientconditionforthegenerationofacrimehotspot.Thatistosay,
crimehotspotsemergeinotherlocationsthanareaswithconcentrated
Section8housing.
Moreexhaustiveanalysisisrecommendedinvolvingseveraldifferent
specificationsoftheclusteringparametersbeforeresultsaredeemed
robustanddefinitive.
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Perception and Crime Analysis
Perception:
Crime in Dubuque is worse than other large cities in Iowa.
Finding:
As noted in the comparative analysis, the inspection of comparative UCR
crime rates during 2004 to 2009 for the ten comparably sized Iowa cities
reveals that, overall; Dubuque’s crime profile is not uniquely different
from the other cities. However, the analysis did point to some differences
with regard to offenses classified as violent crime, using the standard UCR
coding approach.
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Perception and Crime Analysis
Perception:
ThereisahigherrateofcrimeinthecenteroftheCitybutmost
neighborhoodsaresafe.
Finding:
Hotspotsaremorelikelytooccurdowntown,exceptforUCRproperty
(mostlylarceny).Thus,perceptionisaccurate.
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Perception and Crime Analysis
Perception:
.
Section8couldbethecauseofcrime
Finding:
Crimeismulti-causalandpovertydoesplayabigrole.Theassociation
betweenpovertyandcrimeshowsinthecitylevelcomparativeanalysis
andinthemoderateoverlapofsection8(thepoorestofthepoornetof
thehomeless)hotspotsandcrimehotspots.Butnocausalassertionscan
bemade-manySection8concentratedareasdonothaveconcentrated
crimehotspots.
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Perception and Crime Analysis
Perception:
Crime is expanding or diffusing from Section 8 to other areas.
Finding:
The loglinearanalysis shows that the probabilistic tendency is for crime to
remain local. Section 8 victims are more likely to be victimized at section 8
locations. Crimes with section 8 victims are more likely to also see section
8 arrestees. Crimes committed at section 8 locations are more likely to
have section 8 arrestees.
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Perception and Crime Analysis Summary
Can we trust our perceptions and opinions?
"There is no harder scientific fact in the world than the
fact that belief can be produced in practically unlimited
quantity and intensity,
Without observation and reasoning.
And even in defiance of both ...
By the simple desire to believe.“
George Bernard Shaw, The Doctor's Dilemma.
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Perception and Crime Analysis Summary
R
esearch-Confirmed Psychological Biases in Information Processing
C
onfirmation Bias -prefer evidence that is consistent with our
predisposed outlook.
I
nattentionalBlindness -process only information we deem important &
overlook other information
N
arrative (Patternicity) Fallacy -occurs when we construct narrative
explanations to give coherence to events that are not related
P
re-mature Closure Error -ceasing an inquiry, or closing one's mind to
evidence because one thinks the matter settled before it is.
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Perception and Crime Analysis Summary
P
latonicity-tendency to believe our models of reality are reality.
S
election Bias -evidence selected using non-random methods, likely to
make the evidence unrepresentative
S
n
mall Problem (Overgeneralization Error) -using few pieces of
information to reach broad conclusions
P
riming Effect -Disproportionately perceiving that evidence that one has
been 'primed' to witness.
P
rimacy Effect -Disproportionately perceiving and giving greater weight to
the first observations made ('first impressions) over those subsequently
obtained.
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RS
eport ummary
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Report Summary
“Dubuquers”takeprideintheircommunity,as86%ofresidentsratethe
communityasan“excellent”or“good”placetolive(NIU2010Comm.
Survey).
Thishighlevelofcitizensatisfactionisareflectionofthequalityoflife
“Dubuquers”associatewiththeircommunity,theirdailyactivities,andthe
expectationthatlocalleadersprovideasafe,prosperousandprogressive
community.
Thestudyalsofounddifferingviewsamongrespondents’fearofcrime,asa
greaterfearwasgenerallyassociatedstatisticallywiththosewho1)had
livedinDubuquelonger,2)werepriorcrimevictims,3)wereless-educated,
4)werewomen,and5)wereolderresidents.
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Report Summary
Dubuque’s rank among the ten cities in these areas:
7inpropertycrime
th
5inUCRviolentcrime(includessimpleassault,3withoutsimpleassaults
thrd
included)
6infamilypoverty,7inhouseholdpoverty,nearincomeinequality
thth
average
5inmedianhouseholdincome
th
Isstaffedwith1.7swornofficersper1,000residents,comparedtoa1.6
average
Isstaffedwith1.8lawenforcementemployeesper1,000residents,
comparedtoa2.0average
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Dubuque’s Efforts
The City of Dubuque has responded to crime concerns by:
1) creating the Safe Community Task Force, tasked with making
recommendations to the City in order to increase public safety;
2) establishing several programs to improve the Washington
neighborhood;
3) focusing police efforts upon troubled downtown areas;
4) creating a number of programs to assist the unemployed and
impoverished in regaining solid financial footing; and
5) tightening Section 8 housing eligibility requirements and aggressively
enforcing tenant responsibilities.
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Recommendations
Nosingularrecommendationavailabletodirectacommunityonhowto
fightcrime—everystrategymustbetailoredtotheneeds,context,values
andassetsoftheindividualcommunity.
Recommendations:
U
•
tilizethisstudyasaspringboardforobjectivedialog
I
•
nvestin,partnerwith,andempowerat-riskneighborhoods
A
•
ddressdowntowncrimehotspots
D
•
isperseSection8housingunitlocations
A
•
ddresspovertywhereveritoccurs
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Feedback & Discussion
“
Great communities come
”
with great expectations
…increase understanding…
…toward reasoned solutions…
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