Park SafetyAnalyzing Safety around St. Louis County Public Parks

Based on Crime and Population DensityBy: Bradley Lewis Date: April 25, 2013

Civic Problem

Parks are supposed to be a place to relax and enjoy the outdoors.

However, are the parks we go to safe?

Crimes are occuring in and around St. Louis County public parks.

To make parks safer, we have to know where the crimes are happening, what they are, and how to make the parks safer.

Solution

To create a workflow that pinpoints the parks with the most surrounding crime and the types of crime happening.

My main product will be a spreadsheet that shows the names of the St. Louis County parks with corresponding crime and population data.

This data can then be assessed and analyzed by community planners, law enforcement, and park managers to promote and develop safety measures in and around parks with a high density of crimes.

Workflow and Methodology

Selected only parks with names in St. Louis County (total of 68 records).

Selected layers based on type of crime: Crimes Against Persons, Crimes Against Property, and Quality of Life Crimes, and exported them as feature classes.

Spatially joined the parks and census block data, and then the three crime data features based on a 0.5 mile search radius.

Added fields and calculated based on type of crime divided by population data.

Exported to .dbf file and then saved it as an excel file to edit and clean the data. A model of my workflow created using Model Builder in ArcGIS.

Map Product

The total crime map shows that many of the parks in the northeastern area of the county had more crime occur in or near the park than the central and west central parts of the county.

Park_Name Crimes_Against_Persons Crimes_Against_Property Quality_of_Life_Crimes Total_Population_2010 Total_Crimes Crimes_Against_Persons_Divided_by_Pop Crimes_Against_Property_Divided_By_Pop Quality_of_Life_Divided_by_Pop Total_Crimes_Divided_By_Pop

Greaves 2 32 7 4398 41 0.000454752 0.00727603 0.00159163 0.0093224

Forest_Staley 24 86 24 3185 134 0.007535320 0.02700160 0.00753532 0.0420722

Buder 91 431 378 3593 900 0.025327000 0.11995500 0.10520500 0.2504870

Memorial 352 932 840 4546 2124 0.077430700 0.20501500 0.18477800 0.4672240

Larimore 401 1169 275 5110 1845 0.078473600 0.22876700 0.05381600 0.3610570Love 39 387 292 7590 718 0.005138340 0.05098810 0.03847170 0.0945982

Ohlendorf_West 39 174 86 5779 299 0.006748570 0.03010900 0.01488150 0.0517391

Lone_Elk 1 11 15 85 27 0.011764700 0.12941200 0.17647100 0.3176470Kinloch 74 210 91 1604 375 0.046134700 0.13092300 0.05673320 0.2337910

Robert_Winter 13 43 10 2667 66 0.004874390 0.01612300 0.00374953 0.0247469

Forman 21 106 52 5211 179 0.004029940 0.02034160 0.00997889 0.0343504

Antire 0 8 4 105 12 0.000000000 0.07619050 0.03809520 0.1142860

Ebsworth 0 0 0 1888 0 0.000000000 0.00000000 0.00000000 0.0000000

Wayne_C_Kennedy 31 147 79 5682 257 0.005455830 0.02587120 0.01390360 0.0452306

Bright 74 206 121 5760 401 0.012847200 0.03576390 0.02100690 0.0696181

Queeny 21 151 68 6983 240 0.003007300 0.02162390 0.00973793 0.0343692

Sylvan_Springs 205 676 351 4666 1232 0.043934800 0.14487800 0.07522500 0.2640380

R_9 142 361 237 3213 740 0.044195500 0.11235600 0.07376280 0.2303140

Sioux_Passage 17 62 28 2454 107 0.006927470 0.02526490 0.01140990 0.0436023

Tilles 0 0 0 4190 0 0.000000000 0.00000000 0.00000000 0.0000000

West_Tyson 4 24 16 689 44 0.005805520 0.03483310 0.02322210 0.0638607

Spanish_Lake 573 1619 390 6948 2582 0.082469800 0.23301700 0.05613130 0.3716180Classe 84 202 124 5146 410 0.016323400 0.03925380 0.02409640 0.0796735

Endicott 79 321 462 3757 862 0.021027400 0.08544050 0.12297000 0.2294380

Lilac_and_Dunn 186 791 166 2353 1143 0.079048000 0.33616700 0.07054820 0.4857630Fort_Belle_Fontaine 77 69 27 1833 173 0.042007600 0.03764320 0.01473000 0.0943808

Bissel 263 1036 280 4797 1579 0.054825900 0.21596800 0.05836980 0.3291640

Greensfelder 47 242 49 1961 338 0.023967400 0.12340600 0.02498730 0.1723610

Creve_Coeur 19 96 27 7942 142 0.002392340 0.01208760 0.00339965 0.0178796

Castle_Point 467 1175 469 3016 2111 0.154841000 0.38958900 0.15550400 0.6999340King 105 230 95 3190 430 0.032915400 0.07210030 0.02978060 0.1347960

Gravios_Creek 40 202 117 3241 359 0.012341900 0.06232640 0.03610000 0.1107680

Bohrer 102 695 399 4627 1196 0.022044500 0.15020500 0.08623300 0.2584830

McDonnell 101 248 106 7238 455 0.013954100 0.03426360 0.01464490 0.0628627

Mathilda_Welmering 181 657 340 5689 1178 0.031815800 0.11548600 0.05976450 0.2070660

Deer_Creek 3 11 6 3571 20 0.000840101 0.00308037 0.00168020 0.0056007

Gramond 93 280 130 5564 503 0.016714600 0.05032350 0.02336450 0.0904026

Bishop_Long_Log_Cabin 12 44 30 3662 86 0.003276900 0.01201530 0.00819224 0.0234844

St_Stanislaus 0 0 0 3616 0 0.000000000 0.00000000 0.00000000 0.0000000

Champ 82 295 61 4828 438 0.016984300 0.06110190 0.01263460 0.0907208

Faust 21 130 178 5328 329 0.003941440 0.02439940 0.03340840 0.0617492

Bon_Oak 412 1249 323 5745 1984 0.071714500 0.21740600 0.05622280 0.3453440Veterans_Memorial 545 2026 304 10307 2875 0.052876700 0.19656500 0.02949450 0.2789370

Affton_Community_Center 88 323 182 6150 593 0.014308900 0.05252030 0.02959350 0.0964228

Ohlendorf 67 347 107 5545 521 0.012083000 0.06257890 0.01929670 0.0939585

Jefferson_Barracks 364 1074 455 6493 1893 0.056060400 0.16540900 0.07007550 0.2915450

Museum_of_Transportation 50 172 132 6978 354 0.007165380 0.02464890 0.01891660 0.0507309

Bee_Tree 21 121 33 4090 175 0.005134470 0.02958440 0.00806846 0.0427873

Clydesdale 115 506 197 6884 818 0.016705400 0.07350380 0.02861710 0.1188260

Packwood 17 115 62 3356 194 0.005065550 0.03426700 0.01847440 0.0578069

Hunters_Ford 1 5 0 186 6 0.005376340 0.02688170 0.00000000 0.0322581

St_Vincent 176 522 388 4713 1086 0.037343500 0.11075700 0.08232550 0.2304260

George_Winter 15 82 87 1437 184 0.010438400 0.05706330 0.06054280 0.1280450

Belleview_Farms_Sherman_Beach 11 66 32 3417 109 0.003219200 0.01931520 0.00936494 0.0318993

Lemay_Ferry 16 106 75 3002 197 0.005329780 0.03530980 0.02498330 0.0656229

Albrecht 27 107 39 5068 173 0.005327550 0.02111290 0.00769534 0.0341358

Black_Forest 110 341 135 2036 586 0.054027500 0.16748500 0.06630650 0.2878190

Lemay 202 672 350 2726 1224 0.074101200 0.24651500 0.12839300 0.4490100Laumeier 0 0 0 1794 0 0.000000000 0.00000000 0.00000000 0.0000000

Cliff_Cave 77 377 220 8933 674 0.008619720 0.04220310 0.02462780 0.0754506

Bella_Fontaine 1729 5335 2608 15622 9672 0.110677000 0.34150600 0.16694400 0.6191270Widman 16 63 29 3366 108 0.004753420 0.01871660 0.00861557 0.0320856

Lower_Meramec 22 255 92 4594 369 0.004788860 0.05550720 0.02002610 0.0803222

Suson 71 279 147 5480 497 0.012956200 0.05091240 0.02682480 0.0906934

Butler_Hill 2 13 8 1327 23 0.001507160 0.00979653 0.00602864 0.0173323

Simpson 46 203 52 1070 301 0.042990700 0.18972000 0.04859810 0.2813080

Steamboat_Island 0 5 3 408 8 0.000000000 0.01225490 0.00735294 0.0196078

Unger 13 210 45 1172 268 0.011092200 0.17918100 0.03839590 0.2286690

Excel Spreadsheet: Results

Park_Name C_A_PersonsC_A_Property QOL_CrimesTotal_Pop

TotalCrimes CAPerson_ByPop CAProperty_ByPopQOL_ByPop TotalCrimes_ByPop

Castle_Point 467 1175 469 3016 2111 0.154841000 0.38958900 0.15550400 0.6999340

Lilac_and_Dunn 186 791 166 2353 1143 0.079048000 0.33616700 0.07054820 0.4857630

Lemay 202 672 350 2726 1224 0.074101200 0.24651500 0.12839300 0.4490100

Spanish_Lake 573 1619 390 6948 2582 0.082469800 0.23301700 0.05613130 0.3716180

Bon_Oak 412 1249 323 5745 1984 0.071714500 0.21740600 0.05622280 0.3453440

Larimore 401 1169 275 5110 1845 0.078473600 0.22876700 0.05381600 0.3610570

Bissel 263 1036 280 4797 1579 0.054825900 0.21596800 0.05836980 0.3291640

Lone_Elk 1 11 15 85 27 0.011764700 0.12941200 0.17647100 0.3176470

Queeny 21 151 68 6983 240 0.003007300 0.02162390 0.00973793 0.0343692

Creve_Coeur 19 96 27 7942 142 0.002392340 0.01208760 0.00339965 0.0178796

Faust 21 130 178 5328 329 0.003941440 0.02439940 0.03340840 0.0617492

Excel Spreadsheet: Selected Results

Advice

Consider the type of of crime to examine.

After assessing the data and map products, prioritize which parks to allocate resources to. **Some of my recommendations include Castle Point, Spanish Lake, Bella Fontaine, and Lemay.

Survey and examine these areas around the parks. Look for reasons crime may occur in these parks. Do they have lights? What kind of facilities and programs do they have? Is there a school or police station nearby? Are there cameras? Fill out a detailed “inventory” form when doing this examination. ** St. Louis County has no cameras and no or only one dusk to dawn light in the maintenance buildings in the parks mentioned above.

Based on the inventory form, decide which parks you want to center in on. Focus budgets on adding facilities to bring the community together. Extend and implement community programming and the hours of park operation (with the help of added LED lights) by having professionally supervised recreation programs.

Cameras can be added, but do cost more and need to be monitored by trained professionals and integrated with other policing functions.

Cameras may displace or avert crime from the parks, but police officials can identify the areas susceptible to crime displacement and place officers in these locations. ** In Chicago and Baltimore, for every dollar spent on a camera, it yielded a benefit anywhere from $1.50-$4.30.

References:

Donahue, Ryan. “Pedestrians and Park Planning: How Far Will People Walk?” http://cityparksblog.org/2011/05/13/pedestrians-and-park-planning-how-far-will-people-walk/. Accessed April 23, 2013.

Federal Bureau of Investigation. “Crime in the U.S. 2010: Caution Against Ranking.” http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/caution-against-ranking. Accessed April 22, 2013.

Gerken, Jim. Operations Manager for St. Louis County Public Parks. Lord-Castillo, Brett. GIS Programmer for St. Louis County Emergency Management.

Markman, Joshua, Nancy G. La Vigne, and Samantha S. Lowry. “Evaluating the Use of Public Surveillance Cameras for Crime Control and Prevention. September 2011.http://www.cops.usdoj.gov/Publications/e071112381_EvalPublicSurveillance.pdf. Accessed April 23, 2013.

The National Recreation and Park Association. “Creating Safe Park Environments to Enhance Community Wellness.” http://www.nrpa.org/uploadedFiles/nrpaorg/Grants_and_Partners/Recreation_and_Health/Resources/Issue_Briefs/Park-Safety.pdf. Accessed April 23, 2013.

Park SafetyAnalyzing Safety around St. Louis County Public Parks

Based on Crime and Population DensityBy: Bradley Lewis Date: April 25, 2013

References: Donahue, Ryan. “Pedestrians and Park Planning: How Far Will People Walk?”

http://cityparksblog.org/2011/05/13/pedestrians-and-park-planning-how-far-will-people-walk/. Accessed April 23, 2013.

Federal Bureau of Investigation. “Crime in the U.S. 2010: Caution Against Ranking.” http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/caution-against-ranking. Accessed April 22, 2013.

Gerken, Jim. Operations Manager for St. Louis County Public Parks. Lord-Castillo, Brett. GIS Programmer for St. Louis County Emergency Management.

Markman, Joshua, Nancy G. La Vigne, and Samantha S. Lowry. “Evaluating the Use of Public Surveillance Cameras for Crime Control and Prevention. September 2011.http://www.cops.usdoj.gov/Publications/e071112381_EvalPublicSurveillance.pdf. Accessed April 23, 2013.

The National Recreation and Park Association. “Creating Safe Park Environments to Enhance Community Wellness.” http://www.nrpa.org/uploadedFiles/nrpaorg/Grants_and_Partners/Recreation_and_Health/Resources/Issue_Briefs/Park-Safety.pdf. Accessed April 23, 2013.

Civic Problem Parks are supposed to be a place to relax

and enjoy the outdoors. However, are the parks we go to safe? Crimes are occuring in and around St. Louis County public parks. To make parks safer, we have to know where the crimes are happening, what they are, and how to make the parks safer.Solution

I want to create a workflow that pinpoints the parks with the most surrounding crime and specific type of crime. My main product will be a spreadsheet that shows the names of the St. Louis County parks with corresponding crime and population data.

This data can then be assessed and analyzed by community planners, law enforcement, and park managers to promote and develop safety measures in and around parks with a high density of crimes.

Results and Analysis The total crime map shows that many of the parks in

the northeastern area of the county had more crime occur in or near the park than the central and west central parts of the county.

Advice

Consider the type of of crime to examine.

After assessing the data and map products, prioritize which parks to allocate resources to. (Some may include Castle Point, Spanish Lake, Bella Fontaine, and Lemay.)

Survey and examine these areas around the parks. Look for reasons crime may occur in these parks. Do they have lights? What kind of facilities and programs do they have? Is there a school or police station nearby? Are there cameras? Fill out a detailed “inventory” form when doing this examination. (St. Louis County has no cameras and no or only one dusk to dawn light in the parks in the spreadsheet below.)

Based on the inventory form and which parks you want to center in on, focus budgets on adding facilities to bring the community together. Extend and implement community programming and the hours of park operation (with current or added LED lights) by having professionally supervised recreation programs.

Cameras can be added, but do cost more and need to be monitored by trained professionals and integrated with other policing functions.

Cameras may displace or avert crime from the parks, but police officials can identify the areas susceptible to crime displacement and place officers in these locations.

Workflow and Methodology

Selected only parks with names in St. Louis County (total of 68 records).

Selected layers based on type of crime: Crimes Against Persons, Crimes Against Property, and Quality of Life Crimes, and exported them as feature classes.

Spatially joined the parks and census block data, and then the three crime data features based on a 0.5 mile search radius.

Added fields and calculated based on type of crime divided by population data.

Exported to .dbf file and then saved it as an excel file to edit and clean the data.

Park_Name

Castle_Point Lemay

Spanish_Lake

Bella_Fontaine

C_A_Persons 467 202 573 1729C_A_Property 1175 672 1619 5335QOL_Crimes 469 350 390 2608Total_Pop 3016 2726 6948 15622TotalCrimes 2111 1224 2582 9672CAPerson_ByPop 0.155 0.074 0.082 0.111CAProperty_ByPop 0.390 0.247 0.230 0.342QOL_ByPop 0.156 0.128 0.056 0.167TotalCrimes_ByPop 0.700 0.449 0.372 0.619