STREET LIGHT PLACEMENT ANALYSIS IN MARION COUNTY, INppidb.iu.edu/Uploads/PublicationFiles/Street...
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AUTHORS Joti Kaur, Program Analyst, & Drew Klacik, Senior Research Analyst Assistance provided by Katherine Bailey, Program Analyst Design by Karla Camacho-Reyes, Research Assistant STREET LIGHT PLACEMENT ANALYSIS IN MARION COUNTY, IN AUGUST 2017
Transcript of STREET LIGHT PLACEMENT ANALYSIS IN MARION COUNTY, INppidb.iu.edu/Uploads/PublicationFiles/Street...
AUTHORSJoti Kaur, Program Analyst, &Drew Klacik, Senior Research AnalystAssistance provided by Katherine Bailey, Program Analyst Design by Karla Camacho-Reyes, Research Assistant
STREET LIGHT PLACEMENT ANALYSIS IN MARION COUNTY, INAUGUST 2017
334 N. Senate Avenue, Suite 300Indianapolis, IN 46204policyinstitute.iu.edu
CONTENTSOVERVIEW
DATA SOURCES & METHODOLOGY
REPORT STRUCTUREMetric Identification Process
SUMMARY FINDINGS Key Informant InterviewsFocus Groups
ANALYSIS & MODELING Interactive Tool GuideOther Considerations & Concerns
APPENDIX I: IPL Service Area Maps
APPENDIX II: Detailed Metrics by Census Tracts in Marion County
APPENDIX III: Review of LiteratureImpact on Crime & SafetyOther Related Placement SurveysPetition ProcessesFuture of Street LightsReferences
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There are approximately 103,000 street lights in the Indianapolis Power and Light Company (IPL) service territory. Approximately 90,000 0f those lights are currently in service. The 13,000 lights that are no longer in service were funded by private households, rather than the public sector. Service for these lights have been discontinued because the customer that requested the lights does not want to continue to pay for the service. Of the 90,000 lights currently in service, about 37,000 are paid for by the municipalities (29,000 paid for by the city of Indianapolis). Neighborhood associations and community organizations pay for about 17,000 lights. Individuals and companies pay for the remaining 36,000 lights.
OVERVIEW
MAP 1.Location of all street light/s in IPL service area
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DATA SOURCES & METHODOLOGYDATA SOURCE YEAR DESCRIPTION
Street lightsIndianapolis
Power & Light Company (IPL)
2016 Point locations of street lights in IPL Service Area
Census tracts, IPL service area, Marion County, Libraries, Religious facilities
IndianaMap 2016 Geographical boundaries and point locations
Violent and property crime incidents
IMPD-UCR 2015 Point location of violent and property crime incidents throughout IMPD service areas. Other jurisdictions within Marion County are excluded, due to no reporting to UCR.
Pedestrian vehicle accidents City of Indianapolis
2015 Point locations of vehicles hitting pedestrians
Population with disabilities US census 2015 Percent population with disabilities calculated by census tracts
Limited vehicular access US census 2015 Percent households with at least 1 vehicle calculated by census tracts
Parks IndyGIS 2017 Shapefile of polygons representing park locations in Indianapolis and Marion County, IN
Commercial Corridors IndyGIS 2016
Bike lanesIndyGIS 2016 Shapefile of line representing streets with bike
lanes designations in Indianapolis and Marion County, IN
Pedestrian Network IndyGIS 2016 Shapefile of line representing the pedestrian network in Indianapolis and Marion County
No sidewalks
IndyGIS 2016 Point file for sidewalk ratings in Indianapolis and Marion County, IN. Ratings conducted the Indianapolis Department of Public Works Extracted the rating with the distinction ‘no sidewalk’ and ‘no concrete sidewalks’ to display data for no sidewalks through this report
Population US census 2015 Population by census tracts in Marion County
Abandoned PropertiesIndyGIS 2016 Shapefile consisting abandoned housing
property as of May 2016 in Indianapolis and Marion County, Indiana
Median household income US census 2015 Reported by Marion County census tracts
Unemployment Rate US census 2015 Percent population 16 & over unemployed by Marion County census tracts
Population below poverty US census 2015 Percent population living below poverty level by Marion County census tracts
Race/ethnicityUS census 2015 Percent non-white, Black or African American,
and Hispanic/Latino population by Marion County census tracts
Illegal trash dumping City of Indianapolis
2016 Point locations of reported illegal trash dumping
Bus stops IndyGo 2016 Point locations of bus stops
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The report structure is based on various methods of identifying key metrics surrounding the placement of street lights in Marion County. The individual key informant interviews helped PPI staff design and inform focus group discussions. The interviewees suggested PPI map various demographics that may influence street light placement. The focus groups debated and came up with many additional key metrics that are critical for more street and area lights.
The report contains the following main sections:• A summary of suggestions provided by key
informants, key metrics determined by focusgroups
• Analysis of the current street and area lights need based on the key metrics
• Other consideration and recommendations formoving forward
Notes:• Although IPL serves areas outside of Marion
County (see Map 1), the needs analysis isonly performed for Marion County due to theavailability of some data only for Marion Countyand other data sets not allowing for cross countycomparisons.
• Census tracts are statistical subdivisions of acounty that are updated prior to each decennialcensus by local participants
• Limited vehicular access refers to householdswith one care or less
METRIC IDENTIFICATION PROCESSPPI interviewed approximated 12 key informants, ranging from City/County Councilors and City employees to neighborhood residents. Additionally, PPI held three public forums to gather community input on how to prioritize street light placement. These forums were held at the Wayne Township Center on March 1st, The John H. Boner Center on the Near-East side on March 2nd, and the Tube Factory near Garfield Park on March 22nd.
Each public meeting started with a brief presentation by PPI researchers that provided examples of metrics and described the meeting agenda. PPI devised a layered data collection process. Prior to conversing with others, individuals filled out a form with 5-7 key metrics of their own choice. Then each table (approximately 5 people per table) formed a consensus of the table’s top five metrics, which were then reported out to the group. Finally, the entire group voted on the five most important metrics. This allowed PPI researchers to identify need across personal, table, and group recommendations. The final part of the meeting allowed participants to bring up other street light related issues they felt were important and they wanted addressed.
REPORT STRUCTURE
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KEY INFORMANT INTERVIEWSCrime deterrence and pedestrian/bike safety were the two unanimous concerns of the key informants. While all informants initially focused on the general notion of crime, those that differentiated types of crime considered crimes against people slightly more important than property. There were also two basic notions regarding street lights and safety. Both focus on pedestrian safety. The first, and most common suggestion, focused on pedestrian safety. The second focused on using street lights to promote walkability/bikeability by improving perceptions of safety and comfort. The most common metric suggested by key informants was pedestrian vehicle accidents. Many of those that focused on pedestrian safety suggested using both the lack of sidewalks and pedestrian vehicle accidents in areas without sidewalks as key elements in determining exact location of lights. The second safety suggestion was to place lights at key
gathering spots including, schools, parks and trails, pocket parks, trail / street intersections, and other social gathering places.
Most of the other key informant suggestions were specific to a particular neighborhood or personal cause. These suggestions ranged from targeting specific unlit areas where people dump trash to a particular park, church, or school. As specific suggestions developed into common themes, these recommended were converted into broad metrics.
FOCUS GROUPSThe following data represents the aggregate votes of the three focus groups. As previously stated, immediately after PPI’s presentation, and prior to engaging in any group discussion, participants were asked to identify their five most important metrics. At the end of each meeting, PPI researchers collected
SUMMARY OF FINDINGS
FIGURE 1.Focus group personal votes
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Green Technology
Crime Rates
Proactive Safety
Placemaking
Economic Development
High Population Density
Promote Walkability
Intersections
Bus Stops
Pedestrian Vehicle Accidents
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individual votes. The following chart represents our effort to categorize and aggregate those responses. It should be noted, during two of the three meetings PPI staff presented information and one participant brought attention to technology, cost, and the environment. This likely influenced the volume of individual votes for Green Technology.
The Group Consensus votes represent the consensus of each table. After spending 30 minutes talking through their metrics, each table then provided PPI and the rest of the room with their table’s consensus top 5. Interestingly enough, Green Technology drops from the highest voted individual category to near the bottom in the group consensus ranking. Proactive safety and reduction of crime ranked 1 and 2. If pedestrian vehicle accidents are added to thesafety category, then the separation between safety and crime, and other metrics become even more pronounced. The walkability category includes those that wanted to support existing sidewalk safety, and those that felt that areas without sidewalks needed the most light to ensure safety of pedestrians. Thus, when combined with pedestrian vehicle accidents,
traffic safety becomes a third major category. Similarly, if we combine economic development and placemaking, and each was directed towards supporting physical investment, the result becomes a fourth major category.
The last half hour of each public meeting allowed the entire group to prioritize the metrics that had been reported from each table. The first step in the process was for each table to report their five prioritized metrics in detail. After all metrics were identified, PPI facilitators combined similar metrics, with permission from the group. Before concluding the meeting, participants voted for their top three metrics.
The first two categories: crime and general proactive safety each had over twice the composite vote score of the remaining categories, and are weighted as such in the final analysis. The collective definition of crime included crimes against people and property, with a strong desire to use lighting to make both people and neighborhoods safer. The participants clearly recommended using crime rates as the primary metric for street light location.
FIGURE 2.Group consensus, focus groups
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Green Technology
Crime Rates
Proactive Safety
Placemaking
Economic Development
High Population Density
Walkability
Bus Stops
Pedestrian Vehicle Accidents
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The second highest ranked category was general or proactive safety. The two most important elements within the safety category were the intention to use street lights to reduce the volume of vehicle/pedestrian or bicyclist accidents, and to use street lights to make areas where citizens gathered safer. Participants especially focused on dusk to dawn pedestrian vehicle accidents, and the majority felt that accidents in areas without sidewalks were a critical priority. Among the public places that participants wished to see proactively protected by light are schools, parks (including pocket parks), trails, neighborhood commercial corridors, family and youth destinations (including playgrounds and sport fields), churches, libraries, and bus stops.
Place-based metrics were the final high priority, and included interventions that focus on specific amenities, populations, or places. In addition, while many of these amenities and places were included in the proactive safety category, new elements emerged, including areas with high concentrations of people with disabilities, and census tracts with limited vehicular access.
Street lighting is intrinsically a public issue and has been for most of its history, and often, the primary focus has been on crime reduction and improved pedestrian and traffic safety.1 This is consistentwith the key informant interviews and focus group findings that recommend crime as the priority when considering placement of street lights. It was not until the 1970s that street lighting emerged as an area of academic and policy research, to better understand the impact of street lighting and whether historic assumptions were correlated.
A study conducted in various parts of the US and UK found, interestingly, that the reduction in crime associated with new street lights occurred during daytime as well as the night. This study suggested that some of the reduction might have been due to an increase in community pride, or systematic community change that came with the light installation rather than the illumination.2 Anotherinteresting result of the study is that improved street lighting decreased crime in the immediate area as well in its adjacent areas. Furthermore, improvement of area lighting found an increase in pedestrian traffic. This potentially is due to people feeling safer walking in well-lit areas.3
Contradictory to these findings, several cities that have had opposite outcomes with street lighting and some found no real changes. Various studies suggest
that additional street lights may enable criminals dependent upon the area.4 These contradictionsshow that although lighting can have a positive impact on both perception and reality, it works best when part of a well – conceived plan, and it must not be viewed as a cure-all.
Most street light related analytical efforts have retroactively tried to determine why street lights were placed in their current location rather than a proactive guide for potential locations. Researchers in Houston attempted to use historic metrics to gain insight for past street light placement, with the hope to understand how it may affect future placement.5
There was no literature and web based evidence of a proactive metric based model, though some cities may have developed such internally.
Furthermore, literature does not lead to a consensus conclusion regarding street and area light location
ANALYSIS & MODELING
1 http://ns1.keysso.net/community_news/May_2003/improved_lighting_study.pdf
2 See footnote 1
3 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.362.602&rep=rep1&type=pdf
4 https://www.citylab.com/equity/2014/02/street-lights-and-crime-seemingly-endless-debate/8359/
5 https://kinder.rice.edu/uploadedFiles/Kinder_Institute_for_Urban_Research/Programs/Disparity/FINAL_Streetlights_Report.pdf
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and impact. This led PPI researchers to use a public outreach data-driven integrated approach to developing a street light need map. The key informants and focus groups recommended variables (confirmed by literature) that are used in the modeling analysis. The following explains how the analysis was undertaken in a set of fixed tables and maps, with one component being an interactive map that accompanies this report.
Based on the key informant and focus group input, top five metrics were determined. From the identified variables, groups assigned a priority value (1=high priority, 2, or 3) to each. Some groups identified violent crime as high priority and other identified it as mid (2) to low (3) priority. To determine the top
five, variables were weighted based on frequency and rank of priority.
Formula: weighted score for metric X = ((frequency*(3) + frequency *(2) + frequency*1))/3
In this formula, frequency of votes for high priority is multiplied by 3.
METRIC WEIGHTED SCORE
Violent Crime 13.00
Property crime 9.67
Pedestrian Vehicle Accidents 7.00
Population with Disabilities 3.67
Limited Vehicular Access 2.33
MAP 2.Priority ranking for street light/s placement by census tracts in Marion County
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The following emerged as the top five metrics to take into consideration to clearly identify areas with highest perceived need for street lights:
1. Violent crime2. Property crime3. Pedestrian vehicle accidents4. Population with disabilities5. Limited vehicular access
After determining the weighted score for each of the five metrics, each metric is ranked by census tracts (n=224). The census tracts with the highest violent crime per 1,000 people ranks 224. Ranking of each metric is multiplied by the weighted score, and all five metrics are added to create a single value of need, which is then ranked from highest to lowest. Each census tracts is ranked 1 to 224. 1 indicates census tract with the highest perceived need for additional street lights, and rank 224 indicates the lowest need (see Map 2).
Weighted score of all five metrics= ((rank of violent crime per 1,000 people *13) + (rank of property crime per 1,000 people * 9.67) + (rank of pedestrian vehicle accidents * 7) + (rank of population with disabilities * 3.67) + (rank of limited vehicular access*2.33))
Additionally, the weighted score of all five metrics is divided into statistically valid quadrants to create clusters of need known as “zones.” Zone 1 includes census tracts ranking 1 through 56, which is the highest perceived need zone for additional street lights. Zone 2 includes areas ranking 56 through 113, representing moderate need. Zone 3 is composed of ranks from 114 to 168, where additional street light need is mild. Lastly, the remainder of Marion County in part of zone 4, where the perceived need is the lowest.6
The public also heavily emphasized need to invest in lighting to support various amenities when con-sidering street and area light placement. Participants felt that parks need street lights to support evening activity and deter crime activities. Commercial corridors require sufficient lighting due to the high volume of activity of bicyclists and pedestrians to enhance safety. Therefore, those areas can continue being the nodes of entertainment. Participants also felt that libraries and churches should be lit appropriately, so people feel safe at odd hours walking alone to and from their car.
The final recommendation on location of street lights should take into consideration both the need score and the ability to support the amenities suggested by key informants and group participants. The following series of maps can be used in conjunction with need score to determine final placement.
The place/amenity overlays (see Map 3 to 8) include:• Parks• Bike lanes and trails• Commercial corridors• Pedestrian volume/walkability• Absence of sidewalks• Libraries and Religious Facilities
All this overlapping data can be considered visually by using the interactive street light placement analysis prepared by PPI analysts.
INTERACTIVE TOOL GUIDE The interactive map provides a comprehensive platform to assist in determining street light locations. The tool has the ability to zoom into any geographical area, and immediately see the overlap of zones and ranking, current street light locations, other amenities suggested for lighting and areas of risk (no sidewalks). Among the included amenity layers are pedestrian network, bike lanes, and places of worship, schools, parks, libraries, and commercial corridors. This interface allows users to zoom into the area of interest, and evaluate where one ranks as well as underlying amenities influencing the need.
When using the interactive mapping tool:• First, consider rank based on public input. Each
census tract in Marion County is ranked from1 to 224, with 1 representing the areas with thehighest perceived need for additional streetlights according to metrics prioritized by publicinput. These rankings were quartered into zones:Zone 1 consists censuses tracts with the highestperceived need. Zone 4 has the lowest perceivedneed. In order to understand which census tractshave the highest perceived street light need, oneshould focus attention on tracts within zone 1.
• Next, consider the additional public amenitiesthat are identifiable on the map. Public inputsuggests that public amenities, such as parksand sidewalks, should be prioritized with regardto exact location of new street lights. If one isconsidering the placement of street lights withina census tract with high perceived need, one6 See Map 2
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MAP 3.Parks
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MAP 4.Bike lanes and trails
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MAP 5.Commercial Corridors
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MAP 6.Pedestrian network
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MAP 7.No Sidewalks
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MAP 8.Libraries and Religious Facilities
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should locate the public amenities in that census tract to prioritize exact location.
• Finally, consider density (see Appendix II). Acensus tract with low street light density does not necessarily equate a census tract with high perceived need. This means that the metrics the public perceives to be good indicators of ideal street lights placement do not necessarily point us to census tracts with a relatively low density of street lights. The City should consider public demand in placing street lights, but also understand that street lights do not necessarily make an area safer (see Appendix III). The City should view current street light density, and consider weighing the pros and cons between placing street lights in areas with low street light density, and placing street lights in areas with high street light density, but that are prioritized by the public.
There was no public consensus regarding whether to concentrate or widely disperse lights. Table 1 displays the average of all working street lights per sq mile, and street lights per sq mile funded by the City of Indianapolis by zone. Specifically, the street
light conundrum, represented by uncertain research findings, is highlighted in zone 1, which has the highest rate of violent and property crime, pedestrian vehicle accidents, and persons with disabilities. To put the demand in perspective, the average rate of violent crime per 1,000 people in Marion County is 15.5, and property crime rate per 1,000 people is 52.2. Both the violent and property crime rate is lower in Marion County than zone 1. On average, zone 1 census tracts have the highest of amount of pedestrian vehicle accidents per sq mile relative to areas making up zone 2,3, and 4 and unsurprisingly, limited vehicular access, indicating high volume of pedestrians. Yet, the current volume of street lights per sq mile in zone 1 is the highest.
This suggests that the current distribution of street lights seem to match well with current demand. Yet, the same tables illustrate uncertainty regarding the impact of street lights. The areas with the greatest density of street lights also have the highest density of crime. To date, the greater density of lights has not affected the crime rate, which suggested need for additional street light density in zone 1. One possible approach to allocating street lights would be
ZONE MARION COUNTY1 2 3 4
Violent Crime per 1,000 People 35.0 16.1 8.3 2.5 15.5
Property Crime per 1,000 People 91.0 59.9 40.4 17.1 52.2
Pedestrian Vehicle Accidents per sq mile 3.8 1.2 0.6 0.4 1.5
% Population with Disabilities 18.9% 16.2% 12.7% 11.1% 14.7%
% with limited vehicular access 66.1% 59.3% 48.1% 38.3% 53.0%
All working street lights per sq mile (includes service lights)
644.0 373.2 243.6 189.4 363.1
City of Indianapolis funded street lights per sq mile
319.9 151.4 76.6 48.5 149.4
Population per sq mile 4,118 3,526 3,241 2,937 3,456
% Population Below Poverty 38.1% 28.3% 16.4% 11.2% 23.6%
Median Household Income $27,130 $34,858 $48,267 $63,446 $43,366
Unemployment Rate 17.9% 13.1% 9.3% 6.6% 11.7%
Race/ethnicity
% non-white 58.6% 49.7% 43.9% 27.4% 44.9%
% black or African American 42.9% 32.3% 29.4% 15.7% 30.1%
% Hispanic/Latino 11.6% 12.9% 8.7% 5.7% 9.7%
Table 1.Average of Variables by zone
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to establish standardized density goals within each zone. Appendix III displays the current rate of street lights per sq mile, and additional supporting variables for each census tract.
OTHER CONSIDERATIONS & CONCERNSThe key informants and focus group meeting participants consistently expressed concern over two issues that require us to bring them to the attention of policy makers. Both issues revolve around the realization that not all street lights are the same. The first issue is one of cost, and the relationship between bulb efficiency, design quality, and cost. Opinions on how the cost issue should be resolved varied widely. PPI suggests that based on the passion at public input sessions it be addressed transparently. The Project for Public Spaces and other organizations have written about the relationship between type of light, spacing, and design of pole, and the collective impact of place and safety.7 A few of the most basic rules are height, design, and type of street light can all vary and be adjusted related to different purposes and desired and outcomes. Taller poles can be better for parking lots and city streets. Shorter poles have a larger impact on sidewalks, but must be located closely together to have the same impact on streets.8 Along with this variation in height, brightness and bulb type must be adjusted to optimize lighting for drivers. LED is highly stressed as the most efficient lighting.
The second issue was light pollution, and using intentional design to direct light downward to both maximize its positive impact in the neighborhoods, and minimize its negative impact in the sky above. One source that addresses the issue of light pollution is the Florida Atlantic University (FAU) Astronomical Observatory.9 They indicate good outdoor lighting should do 5 things: optimize visibility at night for what we want lit, minimize energy consumption, minimize impact on the environment and ourselves, minimize glare, and minimize light trespass.10
Street lights are becoming a topic of discussion in a possible modernization of cities across the globe, especially “smart cities.” Street lights are used to collect hyperlocal data by loading hidden sensors. Street lights are beginning to be used to measure air quality, light intensity, sound volume, heat, precipitation, and wind as well as count the people going by with the intention of understanding citiesbetter.9 This technology also has the capacity for collecting more information on energy efficiency, movement detection, and air pollution detection.10
Intelligent LED street lights can broadcast and record sounds as needed.11 Street light poles could potentially be equipped with Wi-Fi routers. Eventually, there will be technology that allows street lights to track the number of people waiting at a bus stop, allowing the City to send buses when the demand is there.12 Thus, it is evident that street lights present the opportunity for much more than lighting. The emergence of new technologies, such as the internet of things, present opportunities to leverage this urban infrastructure for the co-location of next generation telecommunication devices, sensors that provide valuable real-time feedback on environmental conditions, and other emerging technologies that will be integral parts of the urban infrastructure landscape in the years to come.
Currently, residents report lights that are out on their street, without knowing whether they are private security lights or municipality funded street light. This creates frustration towards IPL, which can be avoided by providing an online system where residents can check the status of the light before sending in complaints. The public interface of the interactive tool may provide the most value to public, the City of Indianapolis, and IPL when public can access the locations of lights, in service, out of service, and private security lights that are no longer in service.
7https://www.pps.org/reference/streetlights/
8 http://www.slate.com/blogs/future_tense/2014/06/23/sensors_in chicago_street_lights_will_record_hyperlocal_data.html
9 http://cescos.fau.edu/observatory/lightpol.html
10 http://futurecity.glasgow.gov.uk/intelligent-street-lighting
11 hthttp://www.dailymail.co.uk/news/article-2497624/Las-Vegas-street- lights-record-conversations.html
12 https://www.fastcompany.com/3042152/the-streetlights-of-the-future- may-help-cities-fight-traffic
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APP
END
IX I
MAP 9.Population per sq miles
by census tracts, in PL service area
Data Source:U.S Census, ACS 5-year estimate, 2015
APP
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MAP 10. Pedestrian vehicle
accidentsper sq mile
Data Source:City of Indianapolis, 2015
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APP
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MAP 11. Violent crime incidents
per sq mile
Data Source:IMPD-UCR, 2015
Data Note:Numbers of incidents are normalized by sq mile.
IMPD: Marion County entry reflects part I-violent crime data. Part I-vi-olent crime includes aggravated assault, homicide, (attempted) sexual assault, and (attempted) robbery.
No crime data for excluded areas because reports by other non-IMPD departments are not accounted for in the UCR data.
APP
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IX I
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MAP 12. Property crime
per sq mile
Data Source:IMPD-UCR, 2015
Data Note:Numbers of incidents are normalized by sq mile.
Property crime includes the offenses of burglary, larceny-theft, motor ve-hicle theft, and arson. No crime data for excluded areas because reports by other non-IMPD departments are not accounted for in the UCR data.
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APP
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IX I
MAP 13. Bus stops
Data Source:IndyGo, 2016
APP
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MAP 14. Abandoned Properties
per sq mile
Data Source:IndyGIS, 2016
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APP
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MAP 15. Illegal trash dumping
per sq mile
Data Source:City of Indianapolis, 2016
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APPENDIX IIDetailed Metrics by Census Tracts
in Marion County
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APP
END
IX II
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1.1
212
1.2
142
21.4
%19
576
.6%
213
603.
230
6.4
1,10
7.4
$24,
216
15.9
%43
.0%
89.5
%87
.3%
0.1%
1160
.022
227
5.8
224
19.6
224
4.9%
575
.5%
210
824.
344
8.0
1,83
3.6
$46,
331
5.8%
30.6
%31
.4%
11.1
%6.
4%
1246
.421
174
.218
02.
919
222
.6%
208
80.7
%22
077
0.9
431.
13,
444.
6$1
9,34
230
.1%
62.0
%93
.9%
82.9
%3.
4%
1332
.319
910
2.8
206
4.7
208
19.0
%17
659
.9%
136
808.
632
3.7
2,98
8.3
$25,
956
21.7
%44
.3%
32.5
%11
.9%
18.6
%
1439
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672
.417
712
.222
315
.7%
135
76.4
%21
273
6.3
313.
54,
634.
2$2
1,64
819
.2%
40.1
%84
.6%
76.0
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9%
1539
.020
511
6.1
216
8.0
218
9.8%
3976
.6%
214
990.
853
2.1
4,04
7.8
$28,
594
12.6
%32
.3%
36.2
%20
.2%
6.6%
1650
.121
510
0.9
205
1.1
137
18.2
%16
982
.2%
221
452.
317
0.2
1,44
7.0
$21,
087
27.8
%42
.8%
92.0
%86
.6%
2.2%
1749
.921
475
.918
41.
716
323
.2%
210
63.4
%15
874
4.1
298.
03,
104.
3$2
3,70
825
.7%
37.1
%98
.9%
87.5
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3%
1835
.020
065
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82.
719
124
.2%
212
73.5
%20
572
3.7
357.
94,
999.
9$1
9,61
521
.9%
41.4
%88
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74.0
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7%
1927
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682
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92.
518
322
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204
69.7
%19
090
7.3
537.
95,
562.
9$2
5,34
716
.9%
35.4
%32
.4%
6.1%
24.1
%
2026
.018
082
.019
04.
120
424
.5%
216
58.0
%13
141
1.4
189.
22,
951.
8$3
0,67
413
.3%
51.0
%50
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14.3
%35
.4%
2140
.720
762
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64.
720
625
.5%
219
61.4
%14
445
0.4
260.
92,
173.
0$2
0,98
020
.8%
46.1
%10
0.9%
91.0
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2%
2242
.920
919
1.0
223
0.6
100
19.7
%18
467
.4%
184
247.
883
.91,
251.
6$2
4,78
522
.4%
41.1
%79
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57.4
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2332
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787
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42.
017
221
.3%
194
60.0
%13
776
0.5
457.
56,
492.
4$2
8,32
022
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56.5
%26
.1%
8.6%
16.6
%
2430
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084
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32.
017
318
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165
73.0
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339
8.1
121.
42,
121.
8$1
6,17
826
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55.0
%67
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53.4
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2535
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260
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219
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222
63.2
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763
8.8
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63,
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7$2
3,72
329
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34.9
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94.6
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5%
2627
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467
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32.
618
822
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206
73.0
%20
469
1.4
344.
48,
017.
0$2
2,47
218
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41.5
%48
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27.6
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2731
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410
5.0
208
5.4
212
9.9%
4052
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9870
6.6
443.
62,
674.
3$6
1,42
92.
2%13
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%
2823
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56.
521
418
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777
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63,
939.
0$2
9,58
322
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29.6
%77
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64.0
%4.
6%
26
APP
END
IX II
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cle
accid
ents
Per S
q Mile
% p
opul
atio
n w
ith D
isab
ilitie
s
% w
ith li
mite
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
2941
.720
877
.118
52.
518
513
.3%
101
46.1
%75
612.
529
7.4
5,13
4.5
$24,
444
17.1
%41
.4%
53.6
%26
.7%
23.2
%
3029
.518
871
.317
61.
214
324
.5%
215
64.3
%16
537
1.3
209.
93,
827.
2$2
8,63
920
.1%
35.6
%47
.5%
36.7
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0%
3128
.918
770
.617
33.
119
413
.9%
112
67.6
%18
675
4.9
391.
37,
243.
9$3
4,03
120
.2%
27.3
%31
.1%
15.8
%12
.6%
3230
.119
175
.418
32.
217
614
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116
62.5
%14
871
9.0
353.
02,
922.
9$2
6,66
724
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47.0
%72
.5%
67.0
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8%
3335
.120
169
.016
81.
013
11 7
.5%
158
69.8
%19
164
6.8
249.
72,
295.
1$1
8,90
227
.1%
50.0
%84
.6%
80.2
%2.
4%
3422
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889
.019
65.
220
911
.3%
6564
.3%
164
889.
256
3.5
3,94
8.0
$48,
687
5.0%
25.3
%49
.8%
41.5
%4.
6%
3516
.614
810
4.2
207
1.5
154
24.3
%21
363
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159
321.
378
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471.
5$2
7,66
78.
5%25
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21.8
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2%9.
6%
3616
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484
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28.
421
913
.7%
106
74.1
%20
71,
119.
663
6.8
7,42
5.5
$50,
634
7.6%
26.6
%42
.5%
27.7
%6.
8%
3738
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463
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20.
811
719
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180
78.2
%21
952
1.7
186.
93,
301.
5$2
1,36
115
.9%
39.8
%93
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82.8
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5%
3826
.118
177
.818
61.
716
216
.6%
146
55.0
%11
730
2.1
96.2
2,17
2.5
$29,
756
18.4
%26
.9%
65.0
%56
.2%
0.8%
3921
.616
562
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97.
521
617
.2%
155
67.1
%18
296
2.4
465.
34,
770.
7$3
2,83
514
.2%
24.0
%78
.9%
72.1
%2.
4%
4022
.31 6
764
.115
41.
615
824
.6%
217
65.5
%17
556
6.8
295.
14,
676.
1$2
6,13
614
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42.7
%58
.3%
14.9
%41
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4123
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012
8.0
220
2.5
184
7.8%
1954
.6%
115
273.
970
.13,
657.
9$3
0,35
611
.7%
37.0
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50.5
%35
.3%
4220
.916
074
.818
21.
716
521
.8%
199
53.0
%10
266
4.2
401.
13,
507.
1$3
7,35
313
.1%
25.6
%24
.0%
4.3%
16.0
%
4325
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868
.716
72.
017
016
.0%
140
55.2
%12
046
8.5
211.
84,
333.
9$3
0,46
825
.0%
19.0
%85
.1%
72.8
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0%
4423
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245
.611
13.
920
121
.7%
197
71.8
%19
962
6.4
243.
24,
526.
5$2
1,42
919
.9%
28.8
%98
.1%
94.9
%1.
1%
4512
.512
392
.219
82.
619
020
.7%
190
63.0
%15
488
3.6
524.
96,
095.
7$2
7,61
417
.7%
36.6
%21
.4%
9.3%
9.2%
4621
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464
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64.
720
713
.0%
9571
.3%
196
983.
549
4.1
6,02
3.3
$20,
486
18.2
%44
.3%
71.8
%64
.2%
2.2%
4727
.518
564
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50.
610
322
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205
71.1
%19
547
8.1
247.
02,
490.
9$2
4,31
518
.3%
45.5
%37
.3%
19.0
%14
.9%
4849
.021
310
6.2
210
0.0
118
.7%
173
62.6
%15
169
8.3
409.
83,
135.
1$2
5,02
719
.4%
48.0
%44
.8%
29.5
%7.
1%
4932
.019
692
.319
90.
01
28.7
%22
370
.1%
193
437.
526
6.6
2,51
3.8
$17,
250
26.4
%46
.8%
36.3
%29
.3%
5.8%
5021
.216
197
.320
31.
214
115
.6%
134
50.5
%91
323.
088
.82,
239.
0$2
6,41
815
.3%
30.9
%36
.1%
12.9
%21
.2%
5152
.421
670
.417
20.
01
19.7
%18
378
.0%
218
722.
231
1.7
6,89
1.0
$21,
136
19.7
%26
.6%
54.8
%32
.9%
19.5
%
5232
.219
896
.620
20.
01
21.9
%20
063
.6%
160
363.
018
3.8
1,03
6.5
$25,
691
17.1
%33
.9%
19.5
%8.
9%7.
7%
5319
.615
712
3.4
218
0.5
9420
.1%
186
42.6
%60
228.
237
.545
4.8
$31,
538
12.4
%32
.5%
9.3%
1.1%
6.6%
5415
.113
872
.417
87.
621
712
.8%
9053
.9%
110
1,07
1.3
592.
65,
703.
6$4
0,66
78.
3%20
.6%
41.7
%30
.1%
3.6%
5521
.516
355
.212
91.
415
319
.6%
182
64.7
%16
851
0.5
358.
21,
789.
7$2
7,20
07.
0%25
.5%
83.0
%71
.5%
5.8%
5625
.917
944
.910
93.
920
311
.3%
6470
.0%
192
535.
217
3.2
8,28
6.0
$25,
000
19.4
%52
.3%
83.4
%66
.0%
16.5
%
27
APP
END
IX II
ZO
NE
2
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cle
accid
ents
Per S
q Mile
% p
opul
atio
n w
ith D
isab
ilitie
s
% w
ith li
mite
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
5723
.717
469
.216
90.
485
19.5
%18
155
.8%
125
296.
982
.52,
013.
3$2
5,90
415
.8%
36.3
%42
.0%
11.4
%28
.6%
5820
.515
869
.217
01.
013
217
.2%
156
52.4
%99
254.
981
.72,
018.
6$3
9,69
619
.6%
25.5
%83
.0%
71.4
%9.
5%
5922
.116
644
.710
82.
317
715
.3%
128
77.3
%21
732
9.3
35.7
2,44
1.9
$21,
183
23.0
%54
.3%
77.1
%48
.3%
23.6
%
6016
.314
559
.714
22.
017
113
.8%
111
73.5
%20
628
4.1
26.4
4,67
4.1
$26,
490
12.1
%28
.6%
64.3
%50
.4%
9.1%
6130
.519
238
.186
2.3
180
12.9
%92
66.4
%17
935
6.2
217.
91,
741.
0$2
5,31
323
.3%
44.6
%91
.5%
86.4
%0.
8%
6213
.312
511
2.0
213
1.9
167
10.6
%51
57.4
%12
980
6.4
417.
42,
574.
7$5
0,83
310
.9%
23.1
%30
.0%
22.5
%4.
6%
638.
698
66.2
161
2.6
187
19.8
%18
582
.4%
222
275.
09.
03,
433.
4$2
4,81
06.
1%27
.3%
32.1
%5.
1%8.
5%
6413
.612
981
.118
81.
515
715
.9%
138
48.8
%86
449.
517
4.4
3,65
8.8
$36,
024
10.3
%29
.1%
28.6
%2.
0%21
.7%
6523
.617
382
.319
10.
01
22.0
%20
167
.2%
183
774.
950
1.7
6,57
4.7
$20,
885
18.6
%42
.3%
18.6
%10
.6%
5.7%
6616
.915
048
.512
31.
013
320
.8%
192
74.8
%20
934
9.5
77.8
2,39
4.6
$27,
581
6.5%
29.9
%39
.1%
17.8
%19
.0%
6729
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962
.715
00.
01
23.3
%21
184
.2%
223
795.
929
5.9
4,18
3.5
$16,
089
28.5
%53
.4%
96.3
%91
.6%
4.0%
6824
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590
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70.
01
26.6
%22
148
.0%
8438
1.3
156.
077
8.8
$38,
636
16.0
%23
.7%
29.6
%13
.2%
13.4
%
6931
.919
570
.017
10.
01
16.4
%14
272
.9%
202
448.
722
6.8
4,92
9.0
$40,
079
12.4
%32
.8%
48.9
%35
.9%
11.8
%
7017
.515
268
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60.
811
616
.7%
147
49.6
%88
278.
996
.22,
673.
5$3
9,91
113
.4%
19.0
%45
.2%
28.2
%12
.6%
7130
.519
373
.817
90.
01
15.8
%13
663
.8%
161
654.
533
6.0
5,50
3.3
$34,
623
9.0%
26.7
%27
.5%
17.9
%4.
9%
7213
.312
647
.111
62.
117
518
.7%
174
74.2
%20
834
8.3
40.2
4,22
9.5
$30,
313
14.2
%17
.3%
60.4
%42
.2%
14.9
%
7315
.314
058
.514
01.
916
813
.4%
102
60.4
%14
026
8.9
75.1
2,58
3.5
$30,
755
5.2%
26.9
%37
.3%
10.8
%16
.9%
7415
.414
280
.118
70.
591
18.1
%16
736
.1%
3922
1.4
67.4
1,18
7.9
$42,
061
5.3%
24.5
%11
.3%
5.8%
3.4%
7517
.915
352
.612
60.
912
218
.5%
172
54.2
%11
234
7.2
156.
22,
868.
8$3
4,26
527
.1%
19.9
%80
.6%
65.6
%10
.2%
7616
.514
748
.212
11.
013
015
.9%
137
71.4
%19
742
3.6
70.8
5,66
6.1
$30,
928
17.8
%28
.3%
64.8
%50
.4%
12.0
%
7723
.016
946
.711
50.
710
517
.8%
161
56.1
%12
743
0.3
215.
13,
525.
4$3
0,95
116
.8%
22.6
%80
.3%
50.7
%25
.5%
7814
.713
462
.014
51.
213
917
.1%
153
53.3
%10
735
6.7
88.2
3,44
7.9
$38,
651
5.7%
15.3
%35
.2%
29.3
%5.
6%
7927
.218
353
.812
70.
01
32.4
%22
467
.5%
185
553.
824
4.2
4,56
1.3
$24,
837
16.5
%27
.0%
94.4
%87
.5%
1.1%
8016
.915
161
.314
41.
314
713
.1%
9740
.2%
5036
7.6
184.
42,
162.
8$5
5,67
39.
8%9.
0%18
.5%
12.0
%2.
7%
8112
.012
215
7.6
222
0.4
8212
.3%
8151
.6%
9531
6.1
18.8
1,91
0.3
$53,
003
5.2%
10.1
%30
.0%
12.4
%8.
2%
8224
.817
662
.314
70.
01
18.1
%16
672
.2%
200
491.
424
5.4
1,44
3.3
$18,
708
28.2
%50
.9%
73.9
%67
.7%
4.3%
8319
.515
656
.213
31.
013
612
.8%
9147
.7%
8125
0.9
135.
62,
199.
1$3
0,48
520
.4%
51.9
%67
.5%
41.8
%23
.4%
8416
.714
962
.514
80.
811
88.
4%23
72.3
%20
117
0.7
10.0
3,63
8.9
$31,
648
9.2%
23.3
%39
.5%
19.9
%16
.8%
28
APP
END
IX II
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cle
accid
ents
%
pop
ulat
ion
Per S
q Mile
with
Dis
abili
ties
% w
ith li
mite
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
8521
.416
265
.015
70.
01
21.8
%19
864
.1%
163
915.
753
0.1
6,74
4.8
$27,
500
11.7
%30
.6%
24.9
%11
.6%
9.5%
869.
110
212
1.2
217
0.3
7714
.4%
118
58.1
%13
212
3.4
8.2
934.
6$5
0,62
58.
6%9.
1%39
.7%
31.3
%2.
8%
8711
.111
867
.016
21.
012
517
.7%
160
42.4
%59
106.
024
.81,
944.
3$4
6,79
314
.9%
23.4
%47
.6%
38.2
%5.
5%
8813
.813
145
.611
21.
314
818
.4%
170
53.3
%10
635
6.1
150.
42,
535.
3$2
7,45
619
.4%
35.4
%32
.7%
22.1
%9.
3%
8911
.311
959
.414
10.
588
25.7
%22
061
.2%
142
254.
076
.83,
371.
8$2
6,96
215
.8%
45.1
%26
.8%
10.8
%11
.6%
9025
.217
757
.913
80.
01
14.3
%11
777
.0%
215
353.
612
0.6
1,89
8.1
$30,
232
21.7
%38
.1%
72.6
%59
.2%
8.2%
9111
.612
139
.692
3.9
202
13.5
%10
456
.9%
128
658.
728
2.6
6,20
2.9
$40,
307
9.5%
14.9
%52
.7%
40.6
%2.
4%
9210
.511
474
.718
10.
483
13.8
%11
061
.3%
143
107.
117
.02,
015.
8$3
5,53
03.
9%25
.4%
36.6
%13
.4%
9.4%
9313
.612
848
.012
02.
919
39.
2%32
52.0
%97
205.
124
.63,
971.
9$4
0,32
310
.5%
20.9
%81
.7%
51.1
%25
.9%
949.
810
945
.811
30.
811
924
.9%
218
58.9
%13
412
3.2
34.1
1,76
1.0
$33,
024
18.0
%31
.0%
20.3
%12
.8%
6.3%
959.
310
448
.612
41.
013
419
.1%
177
59.1
%13
537
6.2
234.
43,
529.
0$3
5,12
713
.7%
27.9
%26
.4%
10.4
%15
.3%
9614
.513
348
.312
22.
418
18.
2%21
47.8
%83
270.
245
.05,
884.
7$3
3,82
88.
7%37
.6%
78.9
%19
.7%
54.9
%
9714
.213
239
.088
1.3
151
13.2
%10
066
.3%
178
356.
215
0.7
2,84
5.4
$31,
622
8.4%
22.6
%70
.6%
63.1
%3.
5%
9815
.113
926
.652
3.3
199
11.2
%61
67.8
%18
843
6.9
154.
46,
182.
0$2
6,94
820
.9%
37.9
%84
.0%
54.7
%21
.8%
997.
385
47.3
119
1.2
145
17.7
%15
977
.2%
216
529.
722
7.6
2,54
7.9
$11,
365
8.2%
52.8
%62
.9%
38.5
%5.
4%
100
18.6
154
32.1
681.
616
011
.1%
5960
.0%
138
370.
918
5.1
6,10
5.7
$26,
630
11.3
%40
.7%
83.2
%32
.7%
45.9
%
101
12.9
124
30.1
655.
321
114
.9%
123
42.8
%62
312.
019
8.7
4,95
4.8
$40,
938
9.7%
31.0
%63
.7%
30.6
%29
.5%
102
7.6
8965
.916
00.
711
020
.8%
191
39.6
%47
202.
464
.71,
645.
5$4
7,20
011
.7%
16.7
%4.
1%1.
4%1.
4%
103
9.0
101
43.1
104
2.0
174
14.6
%12
054
.0%
111
792.
647
1.2
6,43
4.5
$44,
238
5.3%
17.6
%21
.3%
14.1
%4.
8%
104
5.8
7087
.519
51.
615
910
.9%
5636
.3%
4112
4.7
37.8
2,29
4.2
$67,
936
4.9%
6.3%
7.6%
1.8%
3.8%
105
20.8
159
63.9
153
0.0
110
.4%
4767
.7%
187
368.
939
.36,
734.
4$2
8,11
914
.4%
35.7
%66
.7%
35.6
%26
.1%
106
15.4
141
56.4
134
0.0
117
.8%
163
65.3
%17
181
9.2
521.
34,
624.
3$3
2,03
118
.5%
28.0
%82
.2%
79.9
%0.
1%
107
9.8
107
36.6
841.
515
517
.8%
162
52.8
%10
048
6.9
250.
76,
054.
0$2
9,81
715
.0%
35.5
%34
.2%
22.2
%10
.7%
108
11.1
117
34.9
772.
518
210
.7%
5362
.9%
153
275.
055
.55,
552.
1$3
6,96
24.
6%28
.5%
67.8
%36
.7%
27.9
%
109
6.7
7647
.311
82.
618
914
.7%
121
45.9
%73
242.
514
3.2
2,09
9.9
$51,
012
6.4%
14.9
%13
.3%
2.4%
3.5%
110
8.9
100
33.4
731.
314
621
.0%
193
53.0
%10
428
1.7
82.6
3,10
8.2
$38,
719
5.7%
18.8
%12
.0%
1.2%
10.0
%
111
15.8
143
58.4
139
0.0
115
.0%
126
55.4
%12
134
0.6
195.
64,
646.
3$2
9,71
418
.3%
21.8
%67
.4%
31.8
%32
.4%
112
14.9
135
40.5
961.
012
98.
7%26
53.4
%10
813
8.0
13.2
3,72
1.9
$36,
972
14.3
%32
.2%
78.0
%55
.9%
17.5
%
113
7.0
7965
.915
90.
912
011
.1%
6054
.5%
114
60.8
1.3
1,60
3.3
$58,
639
4.9%
8.8%
25.3
%9.
4%5.
0%
29
APP
END
IX II
ZO
NE
3
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cleac
ciden
ts
Per S
q Mile
% p
opul
atio
n w
ith D
isab
ilitie
s
% w
ith li
mte
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
114
18.8
155
41.3
102
0.0
117
.0%
152
60.2
%13
940
8.2
178.
04,
262.
8$3
4,57
819
.3%
20.4
%95
.7%
90.2
%0.
5%
115
10.4
113
27.5
571.
012
622
.2%
202
47.6
%80
352.
211
0.3
2,04
0.7
$41,
731
19.6
%14
.0%
83.1
%81
.0%
0.5%
116
26.3
182
32.4
690.
01
14.0
%11
462
.1%
146
523.
910
4.4
6,37
2.9
$21,
957
21.5
%43
.9%
90.1
%64
.6%
21.2
%
117
10.5
115
43.4
106
0.4
8416
.6%
145
42.8
%61
170.
624
.11,
536.
1$4
0,32
410
.1%
18.1
%7.
8%3.
6%3.
4%
118
10.3
112
39.9
940.
589
17.1
%15
451
.2%
9415
1.6
7.4
2,76
7.1
$44,
678
13.6
%12
.2%
66.8
%57
.7%
7.1%
119
7.2
8156
.013
20.
912
111
.3%
6350
.2%
9012
8.0
12.1
2,87
1.7
$46,
793
8.3%
9.1%
59.7
%42
.5%
10.7
%
120
13.4
127
25.4
482.
618
67.
6%17
41.8
%57
84.2
53.6
3,61
2.9
$52,
461
6.6%
16.3
%65
.6%
36.8
%26
.3%
121
11.1
116
50.0
125
0.0
119
.4%
179
46.1
%77
388.
922
3.6
3,88
0.0
$37,
743
14.2
%19
.3%
13.1
%7.
0%3.
7%
122
7.6
8839
.995
1.8
166
11.6
%68
32.3
%27
132.
85.
52,
166.
6$5
9,45
86.
6%4.
8%13
.8%
3.3%
3.7%
123
8.7
9946
.011
40.
596
12.5
%86
41.1
%53
263.
629
.62,
924.
4$4
8,77
912
.4%
24.5
%45
.9%
19.0
%21
.7%
124
7.3
8341
.810
30.
710
712
.5%
8762
.7%
152
204.
445
.83,
745.
4$3
4,27
58.
2%29
.1%
58.9
%37
.6%
16.3
%
125
7.7
9134
.074
1.0
135
15.5
%13
145
.6%
7120
4.2
88.5
2,84
8.3
$38,
352
13.6
%11
.9%
56.4
%46
.9%
4.8%
126
9.8
108
55.9
131
0.0
113
.2%
9969
.4%
189
274.
410
.44,
228.
2$3
5,89
910
.7%
10.4
%58
.9%
43.9
%11
.9%
127
15.0
137
40.9
970.
01
13.1
%96
64.5
%16
643
2.4
113.
64,
376.
1$2
3,19
011
.4%
41.3
%87
.6%
48.9
%34
.0%
128
8.6
9727
.556
1.7
164
10.3
%46
62.3
%14
733
1.7
64.3
5,53
8.6
$30,
801
9.2%
33.4
%34
.5%
4.9%
14.1
%
129
5.1
6071
.117
50.
379
11.8
%71
36.0
%38
123.
348
.71,
273.
1$9
2,43
42.
9%6.
5%5.
4%1.
1%2.
6%
130
16.4
146
34.6
760.
01
14.5
%11
954
.5%
113
556.
827
1.8
4,22
4.6
$38,
591
13.7
%27
.0%
62.3
%57
.8%
1.5%
131
9.1
103
47.1
117
0.0
117
.0%
150
57.7
%13
048
8.3
134.
44,
534.
8$3
3,82
77.
8%22
.6%
24.5
%10
.5%
9.6%
132
7.6
8628
.059
0.6
104
15.5
%13
267
.0%
181
319.
778
.64,
639.
9$3
7,09
25.
7%23
.1%
41.3
%6.
8%3.
4%
133
6.3
7340
.999
1.6
161
6.0%
1052
.9%
101
429.
423
2.6
3,09
8.6
$66,
625
4.6%
11.5
%12
.8%
8.6%
0.0%
134
8.1
9534
.575
0.7
106
16.0
%13
935
.6%
3729
1.7
91.9
3,10
4.2
$50,
318
12.2
%10
.8%
26.1
%11
.0%
11.0
%
135
4.8
5941
.210
10.
593
18.4
%17
155
.2%
118
213.
947
.52,
837.
7$3
3,38
48.
4%29
.3%
35.2
%12
.6%
9.3%
136
9.6
105
39.4
900.
711
110
.1%
4437
.7%
4319
7.1
52.0
2,41
8.4
$55,
538
9.5%
11.3
%19
.5%
13.3
%3.
0%
137
13.7
130
45.3
110
0.0
19.
2%31
58.6
%13
332
1.3
99.8
5,68
3.3
$35,
120
12.9
%23
.4%
89.3
%63
.8%
20.9
%
138
7.2
8238
.787
0.7
109
12.2
%80
46.2
%79
213.
863
.21,
857.
7$3
8,33
321
.2%
21.1
%58
.8%
47.1
%10
.0%
139
4.7
5667
.816
40.
01
18.8
%17
544
.7%
6715
4.2
49.5
3,00
2.5
$41,
915
8.4%
13.1
%17
.5%
0.0%
2.2%
140
14.9
136
35.6
800.
01
12.1
%74
55.6
%12
216
0.5
11.1
4,79
6.9
$36,
193
17.8
%23
.7%
83.3
%55
.4%
20.0
%
30
APP
END
IX II
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cleac
ciden
tsPe
r Sq M
ile
% p
opul
atio
n w
ith D
isab
ilitie
s
% w
ith li
mite
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
141
5.4
6554
.212
80.
273
13.7
%10
822
.3%
741
.04.
081
9.4
$70,
742
4.5%
4.9%
31.9
%21
.2%
0.6%
142
10.1
111
44.2
107
0.0
112
.1%
7550
.8%
9258
0.6
187.
84,
295.
4$2
9,63
016
.2%
38.1
%25
.1%
11.8
%10
.9%
143
4.2
5138
.185
1.4
152
6.7%
1263
.9%
162
155.
013
.73,
962.
4$4
8,63
310
.4%
15.8
%57
.4%
40.8
%9.
5%
144
5.7
6927
.254
0.5
9222
.3%
203
44.8
%68
267.
695
.73,
320.
6$3
8,44
910
.3%
20.5
%75
.0%
66.1
%1.
7%
145
7.8
9240
.910
00.
01
15.2
%12
761
.0%
141
144.
923
.53,
269.
0$4
0,42
55.
2%21
.0%
41.5
%34
.8%
5.4%
146
6.9
7871
.017
40.
01
8.6%
2545
.8%
7213
5.6
9.2
1,32
8.1
$50,
260
5.9%
10.8
%49
.4%
36.5
%6.
7%
147
6.0
7239
.591
0.2
7415
.6%
133
33.4
%32
106.
319
.51,
195.
3$5
3,07
28.
0%15
.1%
30.7
%21
.8%
4.1%
148
11.4
120
34.9
790.
01
9.6%
3765
.4%
173
185.
427
.16,
217.
4$3
1,64
29.
8%25
.3%
88.4
%69
.2%
13.8
%
149
3.3
4257
.813
70.
610
29.
5%36
41.5
%55
84.7
34.4
1,83
5.4
$77,
344
7.0%
2.9%
11.3
%4.
5%2.
4%
150
6.5
7555
.513
00.
01
10.8
%55
65.1
%17
012
1.2
43.6
7,15
9.9
$35,
111
4.5%
15.6
%67
.7%
39.9
%21
.5%
151
3.1
3739
.793
1.3
149
11.7
%70
44.0
%65
187.
837
.22,
121.
1$3
8,72
19.
1%10
.0%
37.8
%20
.1%
9.7%
152
8.4
9656
.813
50.
01
9.7%
3838
.5%
4479
.816
.81,
109.
2$5
6,33
93.
8%9.
3%62
.0%
41.9
%13
.6%
153
4.7
5762
.715
10.
01
13.0
%94
50.9
%93
143.
233
.12,
033.
9$6
4,39
86.
1%7.
8%14
.3%
6.7%
1.6%
154
6.8
7743
.310
50.
01
14.8
%12
255
.2%
119
409.
417
2.4
3,43
7.7
$52,
878
3.9%
2.6%
23.2
%7.
0%3.
1%
155
3.5
4634
.978
0.8
115
12.5
%85
53.2
%10
515
4.0
48.2
2,46
5.9
$42,
428
11.7
%11
.4%
35.4
%19
.2%
11.0
%
156
9.7
106
22.7
360.
275
11.6
%67
48.6
%85
121.
810
.42,
663.
0$4
2,78
310
.9%
24.8
%74
.1%
59.3
%11
.7%
157
7.7
9057
.413
60.
01
5.9%
845
.2%
6942
1.6
228.
75,
231.
7$7
0,10
94.
2%10
.1%
3.8%
1.6%
0.9%
158
6.4
7436
.081
0.9
123
4.6%
232
.8%
2950
7.3
327.
14,
321.
7$1
09,3
911.
4%5.
8%6.
7%1.
7%1.
2%
159
7.1
8029
.664
0.5
8712
.2%
7741
.4%
5415
2.6
13.9
2,79
1.2
$56,
651
12.4
%14
.2%
83.0
%57
.9%
13.0
%
160
5.2
6140
.998
0.5
957.
2%15
46.1
%74
235.
111
6.5
2,84
5.7
$57,
755
3.5%
10.8
%31
.7%
18.6
%8.
1%
161
9.9
110
28.1
610.
01
16.8
%14
831
.4%
2412
7.3
6.8
378.
7$5
3,42
98.
5%11
.3%
14.9
%2.
3%7.
9%
162
8.1
9423
.240
0.6
9911
.3%
6631
.0%
2354
.79.
71,
695.
7$6
3,50
45.
3%9.
4%49
.1%
29.6
%10
.5%
163
4.2
5221
.634
5.2
210
6.8%
1333
.3%
3165
2.1
369.
26,
790.
5$7
1,70
02.
1%10
.3%
19.9
%14
.6%
1.8%
164
5.4
6425
.649
0.6
9813
.9%
113
41.8
%58
123.
335
.21,
030.
1$5
6,58
08.
3%11
.2%
49.4
%43
.7%
2.3%
165
4.0
4929
.263
0.8
113
8.9%
2864
.7%
167
180.
023
.41,
872.
6$3
8,89
98.
1%20
.6%
23.0
%16
.0%
1.6%
166
2.3
2633
.172
2.3
179
10.6
%50
27.2
%16
149.
222
.63,
285.
6$5
3,88
97.
5%9.
5%14
.9%
2.6%
8.8%
167
5.2
6212
.121
1.2
138
13.8
%10
938
.8%
4521
9.9
12.7
3,76
5.2
$43,
301
6.3%
12.3
%45
.2%
32.2
%10
.5%
168
7.6
8732
.470
0.0
112
.2%
7846
.1%
7813
3.0
20.8
2,35
5.3
$56,
250
4.9%
6.6%
35.6
%19
.4%
11.8
%
31
APP
END
IX II
ZO
NE
4
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cleac
ciden
ts
Per S
q Mile
% p
opul
atio
n w
ith D
isab
ilitie
s
% w
ith li
mite
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
169
2.0
2423
.239
4.4
205
5.7%
736
.5%
4247
6.2
289.
95,
170.
1$9
7,58
23.
1%5.
3%9.
3%3.
5%2.
4%
170
5.4
6624
.242
0.0
120
.4%
188
54.7
%11
614
1.3
7.2
3,11
8.3
$53,
576
4.8%
3.6%
17.9
%9.
3%5.
4%
171
4.5
5418
.229
1.2
144
8.2%
2241
.5%
5621
2.8
6.1
5,71
2.9
$54,
784
13.4
%7.
0%71
.6%
51.9
%12
.4%
172
7.9
9327
.153
0.0
110
.1%
4255
.9%
126
661.
633
6.9
7,16
9.3
$42,
522
15.6
%18
.9%
55.6
%47
.9%
1.0%
173
3.1
3815
.525
0.7
112
16.6
%14
441
.1%
5228
8.5
50.4
2,87
5.8
$48,
678
7.4%
17.6
%10
.2%
6.3%
0.6%
174
5.9
7139
.289
0.0
14.
2%1
61.4
%14
513
2.7
9.8
5,25
9.1
$47,
278
4.8%
12.3
%40
.7%
16.7
%9.
2%
175
3.1
3910
.117
0.8
114
12.2
%79
63.2
%15
623
1.4
16.6
4,85
5.0
$30,
137
8.0%
34.6
%57
.0%
33.5
%13
.2%
176
2.9
3420
.333
1.0
124
12.6
%88
42.9
%63
172.
049
.42,
643.
7$5
2,86
46.
4%9.
4%5.
8%1.
7%1.
3%
177
4.1
5020
.232
0.3
8012
.8%
8949
.3%
8716
4.1
67.5
1,40
2.8
$54,
750
7.7%
13.8
%55
.5%
39.5
%4.
5%
178
2.8
3227
.958
1.0
128
7.3%
1634
.9%
3510
1.6
24.4
1,74
7.6
$83,
714
2.4%
7.3%
10.3
%6.
6%0.
3%
179
5.5
6728
.160
0.0
114
.0%
115
40.5
%51
665.
339
7.5
5,61
8.7
$48,
355
6.1%
11.8
%38
.7%
32.8
%0.
0%
180
0.0
10.
01
0.0
17.
2%14
23.0
%8
399.
811
.93,
096.
0$8
7,36
46.
0%3.
2%23
.2%
13.6
%4.
1%
181
4.3
5324
.544
0.5
979.
3%33
33.5
%33
164.
736
.52,
864.
3$6
2,45
511
.6%
7.9%
75.6
%57
.4%
12.2
%
182
7.3
8424
.243
0.0
113
.0%
9339
.4%
4619
6.5
30.0
2,86
3.0
$50,
509
7.2%
20.9
%80
.3%
54.7
%16
.9%
183
2.7
3131
.567
0.0
115
.3%
129
66.4
%18
018
3.9
19.0
3,91
8.3
$41,
845
3.6%
12.4
%33
.8%
14.7
%6.
8%
184
5.6
6825
.650
0.0
116
.4%
143
29.7
%21
104.
339
.02,
769.
1$5
4,07
98.
1%7.
2%36
.0%
30.9
%4.
3%
185
0.0
10.
01
0.0
110
.6%
5228
.6%
1839
1.9
2.6
2,36
7.4
$60,
788
10.2
%11
.9%
30.4
%21
.4%
5.0%
186
0.0
111
.820
0.0
18.
1%20
25.6
%13
264.
336
.11,
214.
9$1
16,2
506.
7%3.
9%12
.7%
3.9%
2.2%
187
3.1
4017
.527
0.5
9013
.7%
107
30.8
%22
141.
450
.62,
498.
6$6
3,12
56.
8%11
.7%
10.5
%1.
8%1.
4%
188
4.6
5525
.147
0.0
112
.4%
8463
.1%
155
74.5
14.7
902.
3$3
7,08
011
.6%
20.4
%59
.5%
45.8
%9.
1%
189
0.8
1310
.518
3.3
198
5.4%
631
.5%
2566
5.0
385.
86,
630.
6$9
6,68
310
.0%
18.9
%20
.5%
13.0
%3.
0%
190
5.3
6332
.971
0.0
110
.5%
4839
.6%
4848
9.9
333.
14,
106.
5$6
5,96
24.
1%6.
6%5.
4%1.
9%1.
4%
191
3.5
4536
.182
0.0
113
.2%
9825
.2%
1139
.72.
448
5.6
$78,
750
3.8%
3.2%
14.3
%4.
9%1.
9%
192
3.3
4136
.183
0.0
18.
5%24
53.5
%10
913
9.0
22.7
2,55
3.3
$51,
182
3.5%
9.5%
22.1
%10
.0%
4.3%
193
3.4
4427
.455
0.1
714.
8%3
31.9
%26
79.7
9.2
1,15
0.1
$65,
530
5.6%
9.1%
14.6
%5.
9%2.
6%
194
2.0
2317
.828
0.6
101
8.9%
2955
.7%
123
132.
915
.34,
174.
3$3
9,00
54.
4%20
.7%
39.0
%3.
9%11
.4%
195
2.3
2823
.138
0.4
8610
.5%
4943
.2%
6412
0.3
13.2
3,00
8.4
$57,
350
3.2%
4.2%
9.8%
2.6%
4.0%
196
3.8
4830
.766
0.0
110
.1%
4347
.7%
8217
3.3
12.7
4,14
3.7
$51,
467
5.3%
15.7
%34
.8%
22.2
%5.
8%
32
APP
END
IX II
Rank
Viol
ent C
rim
ePe
r 1,00
0 Peo
plePr
oper
ty C
rim
ePe
r 1,00
0 Peo
ple
Pede
stri
an
vehi
cle
accid
ents
Per S
q Mile
% p
opul
atio
n w
ith D
isab
ilitie
s
% w
ith li
mite
d ve
hicu
lar
acce
ss
All
wor
king
st
reet
lig
hts
per s
q smi
le(in
clude
s se
rvice
light
s)
Stre
et li
ghts
fu
nded
by
the
City
of
Indi
anap
olis
pe
r sq m
i
Popu
latio
n pe
r sq
mile
Med
ian
Hous
ehol
d In
com
e
Un-
empl
oym
ent
rate
%
popu
latio
n be
low
po
vert
y
% n
on-
whi
te
popu
latio
n
% B
lack
or
Afr
ican
Amer
ican
popu
latio
n
%
Hisp
anic/
Latin
o po
pula
tion
197
2.5
2925
.046
0.3
8110
.2%
4528
.8%
1986
.338
.02,
068.
8$9
3,10
04.
6%2.
0%26
.0%
23.0
%2.
5%
198
3.6
4728
.262
0.0
112
.1%
7635
.6%
3617
9.0
60.6
2,46
4.9
$62,
065
4.4%
7.9%
20.8
%8.
4%1.
6%
199
0.0
10.
01
0.0
110
.7%
5439
.8%
4938
6.9
0.0
2,66
3.2
$68,
641
2.6%
3.5%
17.2
%10
.0%
3.5%
200
2.9
3510
.116
0.0
115
.4%
130
62.6
%15
022
9.6
26.3
5,07
6.2
$31,
216
11.6
%28
.6%
23.7
%5.
3%11
.3%
201
4.8
5824
.645
0.0
19.
0%30
36.2
%40
88.1
4.8
1,38
4.0
$59,
444
4.3%
10.2
%54
.5%
30.7
%13
.9%
202
3.3
4323
.941
0.0
113
.6%
105
28.0
%17
197.
445
.61,
962.
1$5
7,55
74.
8%8.
6%12
.6%
0.3%
4.8%
203
1.2
179.
815
0.7
108
11.1
%58
26.1
%15
133.
518
.62,
847.
6$6
6,10
18.
7%7.
5%21
.1%
12.2
%5.
9%
204
0.0
10.
01
0.3
789.
3%35
21.1
%6
306.
512
.01,
909.
1$8
0,23
49.
4%4.
4%35
.8%
24.1
%6.
9%
205
2.9
3322
.435
0.0
113
.5%
103
45.6
%70
186.
564
.02,
940.
7$4
8,43
33.
0%13
.6%
44.0
%30
.7%
7.8%
206
1.0
1614
.724
0.3
7612
.0%
7234
.2%
3440
.19.
884
9.6
$85,
145
1.0%
5.1%
28.2
%22
.3%
2.2%
207
2.3
2713
.423
0.1
7210
.9%
5720
.1%
426
.83.
739
3.5
$70,
938
3.8%
3.9%
8.2%
1.6%
0.3%
208
2.0
2525
.851
0.0
111
.7%
6929
.0%
2010
3.9
7.8
2,41
3.1
$88,
125
1.9%
2.6%
19.7
%10
.1%
2.4%
209
3.1
3622
.937
0.0
112
.1%
7324
.1%
1036
.55.
281
9.9
$62,
105
8.2%
7.5%
23.5
%14
.5%
4.6%
210
2.6
3018
.430
0.0
112
.3%
8225
.5%
1247
.85.
01,
718.
2$5
7,01
67.
7%19
.7%
12.1
%0.
9%8.
0%
211
1.3
198.
113
0.0
114
.9%
125
46.1
%76
243.
644
.82,
572.
3$3
6,17
86.
4%21
.0%
10.4
%2.
1%4.
1%
212
1.6
2219
.131
0.0
110
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33
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I
In 1880, Wabash, Indiana became the first electrically lit city in the world when they lit up the court house grounds (Robert, 1967). Since then, citizens and businesses have embraced the feelings of safety, comfort, and convenience that lighting conveys. Because of perception, the location of street lights matter, as that location determines how safe people feel and how welcoming a place appears. Yet appearance and reality are not always the same. Leading to the question, do street lights measurably increase safety and do they support investment?
Have you ever stopped to wonder about why street lights are where they are? In addition, who gets to decide where new street lights might be located. The following review of prior studies seeks to provide answers to these fundamental questions while informing the development of the Indianapolis street lights placement analysis IU Public Policy Institute has prepared.
IMPACT ON CRIME & SAFETYStreet lighting is noted to be essentially a public issue. The primary focus of street lighting has been on crime reduction and improved pedestrian and traffic safety, but it was not until the 1970s that street lighting matured as an area of academic and policy research (Farrington & Welsh, 2002). Since then, throughout the United States and the United Kingdom research has been conducted to better understand the impact of street lighting and whether historic assumptions were true. One study found that most willingness to pay for lighting was derived from the assumption that street lighting would alleviate safety concerns (Willis, Powe, & Garrod, 2005). This study also analyzed the different concerns between rural and urban areas. For urban areas, the study found that safety and crime were the primary issue of focus. A study from 2002 with data from the US and UK focused on identifying reductions in crime associated with street lighting (Farrington & Welsh). A reduction in crime was found and, interestingly, the reduction in crime associated with new street lights occurred during daytime as well as during the night when the lights would be impactful. This suggested that some of
the reduction may have been due to an increase in community pride or systematic community change that came with light instillation rather than solely the illumination.
Overall, street lighting has been found to be a cost-effective asset that can be a useful part of crime reduction programs (Farrington & Welsh). One study out of the University of Cambridge found that improved street lighting led to a decrease in crime not only in the experimental area but also in adjacent areas (Painter & Farrington, 1999). The study also found an increase in pedestrian traffic in the newly lit experimental area. At the Federal level, the Department of Justice and the Office of Justice programs sponsors websites that describe the positive impact that improved street lighting have on some forms of crimes, such as property crimes or other lower level offenses.
Darkness is less safe for pedestrians and drivers. Twenty-five percent less travel occurs at night compared with daytime yet more than 50 percent of all fatal crashes occur at night (Gibbons, Meyer, Terry, Bhagavathula, Lewis, Flanagan, & Connell, 2015). While effectiveness of lighting can vary greatly across several variables such as roadway surface, lighting design, and type of light (Moreno, Avendaño-Alejo, Saucedo-a, & Bugarin, 2014). The Federal Highway Administration has produced a lighting handbook in collaboration with other large-scale transportation organizations. Although focused largely on highways, the handbook does analyze the purpose for lighting as well as federal guidance regarding roadway lighting (Lutkevich, McLean, & Cheung, 2012). One key statistic from the handbook says nighttime fatal crashes are reduced by up to 60% with the use of roadway lighting.
Finally, while we have generalized about the most common conclusions regarding street light studies, there are still many contradictory studies. One study compares several cities that have had opposite outcomes with lighting or no real changes and suggests that street lights may enable criminals in some ways (Riggs, 2014).These contradictions show
APPENDIX IIIReview of Literature
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that although lighting can have a positive impact on both perception and reality, it works best when part of a well –conceived plan, and it must not be viewed as a cure-all.
Street lights can also support investment and contribute to place making. The Project for Public Spaces (PPS) is an organization dedicated to helping people create and sustain public spaces that build stronger communities (Project for Public Spaces, 2009). PPS has found lighting important, as it can increase safety, help in geographic orientation, as well as highlight the identity or history of an area. PPS also recommends creative ways to use lighting, suggesting there is a correct amount of lights, and how the lights should be spaced. These issues are beyond the scope of the heat map, but are an important part of a well-conceived street light plan (more detail is available at www.pps.org).
OTHER RELATED PLACEMENT SURVEYSCity specific street light data inventories are rare. Many cities boast about the number and type of street lights they had, but rarely provide any useful associated information such as street lights per mile of road, or other means of normalizing volume across places. Even rarer were surveys or decision-making tools that allowed for citizens and municipalities to make highly informed decisions for street light placement.
One study, from Rice University, focused on the Houston, Texas area and tried to retrospectively determine why street lights were placed where they were (O’Connell, 2017). This study attempted to use historic information and variables to better understand the “why” of street light placement and how these variables may affect future placement. The report used data by census block groups to be as accurate as possible. Within these block groups; the number of street lights was divided by the miles of roads within the group.
American Community Survey data was used for social and economic variables of interest. The regression developed could be used to understand if an area had above or below the expected number of lights per mile. It could not be used to determine if the number of lights, high or low, was appropriate, or should be adjusted. Some findings included what races were the most represented in the block groups with the highest rate of lights. Perhaps the most noteworthy finding was that a higher median
income was associated with an increase in street lights when also linked to a higher percentage of households below the poverty threshold. This would indicate that block groups with a higher disparity in household economic outcomes would also be the block groups with the highest rate of street lights (O’Connell).
Other studies related to street lights largely focus on the appropriate light and light poles for the need. New work mainly focuses on the importance of LED lights and the large savings in energy cost after a higher initial investment cost (Kimber, Roberts, Logan, & Lambert, 2015).
PETITION PROCESSESThe petition process to install new lights varies across the nation. In Fort Wayne, Indiana they have several types of petitions (City of Fort Wayne, 2017). Most types are 100% paid for by the City with one option splitting the cost with the real property affected 40%/60%. Petitions require that 60% of the owners of impacted property footage sign the petition before they are considered. The City also has a process to handle cost sharing of lights installed for the benefit of an entire neighborhood. Minneapolis, Minnesota also has a street light location process (Minneapolis Public Works, 2009). Street lighting is considered during street reconstruction processes, but it is also possible to go through a two-phase petition process. This process starts with contacting the City, which then produces a petition. Residents and property owners are then able to collect signatures and submit.
Minneapolis requires 35% of owners affected to have signed the petition in favor of lighting. Phase two starts with the City mailing information to all taxpayers affected with information about the project’s boundaries, estimated total cost, Uniform Street Lighting Assessment Rate, and information about the remainder of the process. This requires 70% of the property owner’s approval before the project will begin14. Minneapolis offers 5 different types of light design while also addressing that performance is impacted by light levels, light uniformity, and glare. Charlotte, North Carolina has much the same petition process although a phone call to their CharMeck Call Center is needed to start the process (Charlotte Department of Transportation). Los Angeles, California carries out a 2-step process much like Minneapolis (City of Los Angeles). One newsworthy case in Los Angeles
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Itook several years to complete and costs residents thousands of dollars to install lights throughout a neighborhood, but this seemed worth it according to the residents who reported break-ins and prostitution when the streets were dark (CBS Los Angeles, 2014).
FUTURE OF STREET LIGHTS Street light design is a field with a surprising amount of depth. Height, design, and type of street light can all vary for different uses and impact impressions and outcomes. Taller poles can be better for parking lots and city streets. Shorter poles have a larger impact on sidewalks, but must be located more closely together to have the same impact on streets. Along with this variation in height, brightness and type must be adjusted to optimize lighting for drivers. LED are highly stressed as the most efficient lighting. Many sites, including Project for Public Spaces, get into other design elements that can be considered such as light spacing and sidewalk placement (Project for Public Spaces).Street lights have also been a topic of discussion in a possible modernization of cities across the globe. Chicago has collected hyperlocal data using street lights to better understand how cities are used (Newman, 2014). Some of this data could include air pollution and number of pedestrians walking by the light. Similar technology has been used in Glasgow, Scotland (Glasgow City Council, 2017). This technology allows for more information on energy efficiency, manual brightening, movement detection, and air pollution detection. According to a Boston Globe article, there are an estimated 26 million street lights in the United States (Smalley, 2012). This creates a large energy and maintenance cost for taxpayers. As mentioned previously, this network has started to swing toward LEDs to help lower these cost and improve flexibility. One such article discusses the intelligence that could be imbedded in street lights (Daily Mail, 2013). Las Vegas is installing intelligent LED lights that can broadcast and record sounds as needed. Other lights have been manufactured that can help alert drivers to open parking space, monitor pollution, and post information for consumers for local retail outlets (Peters, 2015).
Naturally with this much information being recorded there are privacy issues. The largest issue being what data exactly is recorded and who owns the data. Chattanooga, Tennessee has also started
implementing similar technologies (Badger, 2013). Lights have been installed in and around Coolidge Park that are aimed at mitigating gang activity. It also has had the unintended benefit of allowing a frisbee league to operate at 11PM in the park. Some light polls could potentially be equipped with WiFi routers moving forward. General Electric (GE) has also been developing technology that can track the number of people waiting at a bus stop (Peters). This could allow the city to send another bus when the demand was there. Traffic and parking information can be potentially be recorded and sent to a car’s navigation system in real time. Detroit, Michigan is one example of a city that has made this high initial investment for future payoffs (Reindl, 2015). Lights are being installed, repaired, and updated, to drive potential safety benefits and to show the investment the City was making investment in the future of neighborhoods that were previously dark and felt forgotten about. The Department of Energy also has many resources available when it comes to lighting. The Office of Energy Efficiency and Renewable Energy even have a page fully devoted to Solid-State Lighting (LEDs) and their importance.
Light pollution is another topic that should be addressed proactively. One source that addresses the issue of light pollution is the Florida Atlantic University (FAU) Astronomical Observatory. They say good outdoor lighting should do five things: optimize visibility at night for what we want lit, minimize energy consumption, minimize impact on the environment and ourselves, minimize glare, and minimize light trespass. Many of these are direct issues to consider when deciding what type of light and how many lights to have for street lighting. FAU also address many of the economic costs associated with wasteful lighting.
ReferencesBadger, E (2013, March 13th) City Lab: The
Streetlight of the Future Will Do So Much More than Light Your Street. https://www.citylab.com/life/2013/03/streetlight-future-will-do-so-much-more-light-your-street/4958/
City of Fort Wayne (2017). Street Light Engineering. Retrieved from https://www.cityoffortwayne.org/publicworks/traffic-engineering/street-light-engineering.html
CBS Los Angeles (2014, July 9th). Residents Foot Bill
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for Streetlights In Their Neighborhood. Would You? http://losangeles.cbslocal.com/2014/07/09/residents-foot-bill-for-streetlights-in-their-neighborhood-would-you/
Charlotte Department of Transportation: Street Lighting Program http://charlottenc.gov/Transportation/Programs/Pages/StreetLighting.aspx
City of Los Angeles: Petition for Installing a Modern Street Lighting System http://bsl.lacity.org/downloads/Petition%20Modern%20Lighting.pdf
Communities. Iowa Association of Municipal Utilities (IAMU) And Mike Lambert, Brooks Borg Skills Architecture Engineering LLP/KCL Engineering.
Daily Mail (2013, November 10th) What Happens in Vegas DOESN’T stay in Vegas with New Street Lights That Can Record Your Conversations. http://www.dailymail.co.uk/news/article-2497624/Las-Vegas-street-lights-record-conversations.html
Farrington, D. P., & Welsh, B. C. (2002). Effects of improved street lighting on crime: a systematic review. London: Home Office. Silverberg, Robert (1967). Light for the World: Edison and the Power Industry. Princeton, N.J.: D. Van Nostrand
Florida Atlantic University. Department of Physics. Light Pollution Costs Money, Wasters Energy and Resources. http://cescos.fau.edu/observatory/lightpol-econ.html#One-Upmanship
Florida Atlantic University. Department of Physics. The Problems of Light Pollution – Overview. http://cescos.fau.edu/observatory/lightpol.html
Gibbons, R. B., Meyer, J., Terry, T., Bhagavathula, R., Lewis, A., Flanagan, M., & Connell, C. (2015). Evaluation of the Impact of Spectral Power Distribution on Driver Performance (No. FHWA-HRT-15-047).
Glasgow City Council. (2017). Intelligent Street Lighting. http://futurecity.glasgow.gov.uk/intelligent-street-lighting/
Kimber, A., Roberts, J., Logan, J., & Lambert, M. (2015). LED Street Lighting: A Handbook for Small
Lutkevich, P., McLean, D., & Cheung, J. (2012). FHWA lighting handbook. Parsons Brinckerhoff.
Minneapolis Public Works (2009). Minneapolis Street Lighting Policy http://www.ci.minneapolis.mn.us/www/groups/public/@publicworks/documents/webcontent/convert_280924.pdf
Moreno, I., Avendaño-Alejo, M., Saucedo-a, T., & Bugarin, A. (2014). Modeling LED street lighting. Applied optics, 53(20), 4420-4430.
National Institute of Justice (n.d.). Improved Street Lighting. Retrieved from https://www.crimesolutions.gov/PracticeDetails.aspx?ID=38
Newman, L. (2014, June 23rd) Slate.com: Chicago’s Street Lights will Collect Data on Weather and How Many People Walk By. http://www.slate.com/blogs/future_tense/2014/06/23/sensors_in_chicago_street_lights_will_record_hyperlocal_data.html
O’Connell, H. A. (2017). Streetlights in the City: Understanding the Distribution of Houston Street Lights. Retrieved fromhttps://kinder.rice.edu/uploadedFiles/Kinder_Institute_for_Urban_Research/Programs/Disparity/FINAL_Streetlights_Report.pdf Painter, K., & Farrington, D. P. (1999). Street lighting and crime: diffusion of benefits in the Stoke-on-Trent project. Surveillance of public space: CCTV, street lighting and crime prevention, 77-122.
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IOffice of Energy Efficiency and Renewable Energy. Solid State Lighting https://energy.gov/eere/ssl/solid-
state-lighting
Peters, A. (2015, February 11th). The Streetlights of the Future May Help Cities Fight Traffic (https://www.fastcompany.com/3042152/the-streetlights-of-the-future-may-help-cities-fight-traffic)
Project for Public Spaces (2009). Lighting Use & Design. Retrieved from https://www.pps.org/reference/streetlights/
Reindl, JC. (2015 November 11th) Detroit Free Press: Detroit Rising: And then there were Streetlights. http://www.freep.com/story/news/local/michigan/detroit/2015/11/12/detroit-street-lighting-project-update/31850609/
Riggs, M. ( 2014). Street Lights and Crime: A Seemingly Endless Debate. https://www.citylab.com/equity/2014/02/street-lights-and-crime-seemingly-endless-debate/8359/
Smalley, E. (2012, August 02nd). Boston globe: Streetlights: Changing our night sky, one lamppost at a time https://www.bostonglobe.com/opinion/2012/08/02/podiumstreetlight/9qVaAubIxU0j27bcavREaK/story.html
Willis, K. G., Powe, N. A., & Garrod, G. D. (2005). Estimating the value of improved street lighting: A factor analytical discrete choice approach. Urban Studies, 42(12), 2289-2303.)
The IU Public Policy Institute is a collaborative, multidisciplinary research institute within the Indiana University School of Public and Environmental Affairs. PPI serves as an umbrella organization for research centers affiliated with SPEA, including the Center for Urban Policy and the Environment and the Center for Criminal Justice Research. PPI also supports the Office of International Community Development and the Indiana Advisory Commission
on Intergovernmental Relations (IACIR).
