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The Advancement of the BID Movement BIDs in the United States Context (Historic, Political,Economic) The Emergence of the BID in Philadelphia Center City.

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Presentation on theme: "The Advancement of the BID Movement BIDs in the United States Context (Historic, Political,Economic) The Emergence of the BID in Philadelphia Center City."— Presentation transcript:

1 The Advancement of the BID Movement BIDs in the United States Context (Historic, Political,Economic) The Emergence of the BID in Philadelphia Center City District Other BIDs in Philadelphia State Enabling Legislation (comparative analysis) Empirical Work on BIDs

2 STUDY ONE Do BIDs services impact crime patterns? STUDY TWO If so, do BID services push criminal activity to adjacent neighborhoods?

3 Research Design Study One Method: Regression Analysis Research Questions: Do BID services discourage crime (clusters)? Data Sources: Crime (1998 & 1999), Cartographic, Census, etc. Property = Burglary, Theft, Auto Theft Violent = Rape, Robbery, Aggravated Assault Related Research: Kelling, Jacobs, Knox and Mantel

4 Rationale/Context BIDs focus on clean and safe by… enhancing informal surveillance enhancing formal surveillance

5 Context City of Philadelphia Integrated Municipal GIS No Data Layer for 9 BIDs Primary Data Collection - Surveys and Interviews

6 Crime Hot Spots Spatial Clusters (Area, Grid, and Point) Space-time Clusters (Knox, Mantel, and K-function)

7 Customized GIS Conceptual Framework A new point-level method of analyis Simultaneously considers space and time Captures information about how a pattern grows Operational Framework Unit of analysis is the individual crime event GIS calculates a cluster value (using Avenue) Analyst determines spatial and temporal parameters Treats each incident as if it initiated the cluster Excludes those crimes already counted Note GIS can compute cluster values for 10,000 records in 4 minutes

8 Spatio-temporal Analysis Space Time

9 Spatio-temporal Analysis

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11 Calculating Cluster Value

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15 Data Dependent Variable: Violent Crime Clusters Property Crime Clusters Independent Variables: Security Staff (FTE) Sanitation Staff (FTE) Control Variables: Number of Businesses Zoned Residential Median Household Income

16 Findings PROPERTY CRIME Explanatory power of model is.297 (R-squared) Security regression coeficient is negative and significant Sanitation is positive and significant VIOLENT CRIME Explanatory power of model is.085 (R-squared) Clusters are an inappropriate measure for predicting violent crime

17 Research Design Study Two Methods: Summary Statisitcs & Time Series Analysis Research Question: Do BID services push criminal activity to adjacent neighborhoods? Data Sources: Crime (1998 through 2001) and Cartographic Property = Robbery, Burglary, Theft, Auto Theft Quality of Life = Vandalism, Prostitution, Drug Activity, Drunkeness, Disorderly Conduct Related Research: McIver, Spiegel, and Hellman

18 Context 9 Business Improvement Districts 33 Large Commercial Areas 500 Foot Buffer

19 Empirical Work on BIDs Property Crimes in BIDs and Commercial Areas Commercial Areas1998199920002001 4,4704,4214,0194,071-9% Business Improvement Districts1998199920002001 Center City District5,7715,1624,3364,328-25% South Street892779723592-34% Germantown601449384398-34% Frankford3983804194124% Manayunk221208237149-33% City Ave451405286259-43% Old City665643547493-26% University City3,5373,4904,0003,5901% Mercy-Health1,9902,1361,8331,686-15% Total14,52613,65212,76511,907-18%

20 Empirical Work on BIDs Property Crimes adjacent to BIDs and Commercial Areas Adjacent Commercial Areas1998199920002001 2,8322,9562,8172,803-19% Adjacent Business Improvement Districts1998199920002001 Adjacent Center City District991792663616-38% Adjacent South Street409296254249-39% AdjacentGermantown297213152159-46% Adjacent Frankford302295263272-10% Adjacent Manayunk11811310782-31% Adjacent City Ave219181197165-25% Adjacent Old City151987264-58% Adjacent University City277316266222-20% Adjacent Mercy-Health379389313276-27% Total3,1432,6932,2872,105-33%

21 Empirical Work on BIDs Quality of Life Crimes in BIDs and Commercial Areas Commercial Areas1998199920002001 2,9803,0073,0173,33512% Business Improvement Districts1998199920002001 Center City District2,1321,2001,0111,389-35% South Street5254845685596% Germantown17911013420313% Frankford515449334311-40% Manayunk195151139107-45% City Ave103919574-28% Old City1861501671986% University City1,2801,1731,1641,266-1% Mercy-Health1,1991,1911,0631,62736% Total6,3144,9994,6755,734-9%

22 Empirical Work on BIDs Adjacent Commercial Areas1998199920002001 2,8593,1612,5873,28315% Adjacent Business Improvement Districts1998199920002001 Adjacent Center City District477276227294-38% Adjacent South Street228137150206-10% AdjacentGermantown88626383-6% Adjacent Frankford302203218286-5% Adjacent Manayunk1357310986-36% Adjacent City Ave3936756054% Adjacent Old City38141119-50% Adjacent University City175144115138-21% Adjacent Mercy-Health21728826129034% Total1,6991,2331,2291,462-14% Quality of Life Crimes adjacent to BIDs and Commercial Areas

23 Findings PROPERTY CRIME Rate is decreasing more rapidly in BIDs than in commercial areas (9% in Commercial Areas, 18% in BIDs) Rate is significantly lower in neighborhoods adjacent BIDs (Down 35% overall) Steady in neighborhoods adjacent commercial areas QUALITY OF LIFE CRIME Rate is up in commercial areas and adjacent neighborhoods (12% AND 15%, respectively) Rate is down in BIDs and adjacent neighborhoods ( 9% AND 14%, respectively)

24 Summary BID organizations have a positive impact within the BID BIDs not have a (negative) impact around the BID Need to look beyond Philadelphia Need to move beyond crime


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