Crime Cluster Area Identity and Police station site select

In recent years, various types of crime in New York City (NYC) have been on the rise (Baumer & Wolff, 2020)(see Figure 1). The high incidence of crime not only threatens the safety of citizens' lives and property, but also hinders NYC's social order and economic development. Based on the above environment, how to effectively identify crime hotspots in NYC, analyze the strengths and weaknesses of local police agencies, and plan and establish new police stations have become major issues that the local planning department of NYC needs to solve urgently.

In an environment where urban crime problems are becoming increasingly prominent, geographic information systems (GIS) provide a scientific, accurate and efficient analysis solution to solve the above problems. Based on a series of GIS spatial analysis, researchers can deeply explore the spatial characteristics and spatiotemporal distribution patterns of crime data, and further understand the social and geographical factors behind crime. Spatial analysis such as cluster analysis can identify crime hot spots and provide important reference for formulating targeted crime prevention and combat strategies(Kennedy et al., 2011).

This story map will start from the reality of crime problems in New York City, and systematically explore how to use GIS technology to analyze the current crime clustering situation in New York and the subsequent location planning of police stations. As a first step we will analyze the distribution of crime hotspots in the current crime landscape to better understand the nature and scale of the problem. The second step will be to conduct an in-depth study of the security strengths and weaknesses of each local police station and areas for improvement. The third step will explore how to strengthen crime prevention and combat capabilities through planning new police stations to enhance urban security and social stability.

Through the analysis and discussion of these issues, this story map aims to provide scientific ideas and suggestions for solving the crime problem in New York City, and at the same time demonstrate the important role and application prospects of GIS technology in the research and governance of social issues.

This section will introduce the specific data and methods used in the experiment. The experimental data are shown in Table 1. The experiment involves 2080analysis, Network Analysis, Summarizing with and other GIS analysis methods. The specific process is shown in Figure 2. The analysis method and process are as follows.

1. XY Table To Point: Mainly converts NYC Crime location table data, and converts longitude and latitude coordinates into vector point data for the next step of analysis;

2. 80-20 Analysis: The 80-20 rule is a theoretical concept that most events occur in a few locations, for example, 80% of events occur in 20% of locations. This tool is used to identify the points where the top 80% of crime clusters are in 20% of the area. Based on the crime point data generated in the previous step, the location of crime clusters in NYC can be obtained;

3. Summarize Within: This tool is used in multiple analysis processes. It is used to count the total number, total length and total area of points, lines and surfaces within a polygon. It can also summarize the statistical data of a certain field. The nodes used count the total population in the service area of each police station and the total number of crime incidents;

4. Build Network: This tool is used to build NYC's road network. In the road network, a model with length as impedance and three different speed models with time as impedance are designed to carry out the service area and route of existing police stations. Plan analysis and service area analysis of candidate police stations;

5. Service area: This tool is used to analyze the service range of existing and candidate police stations based on the road network;

6. Route plan: This tool is used to analyze the best route planning based on the road network between geographical locations;

7. Location Allocation: This tool analyzes the best site allocation from candidate sites based on the supply capacity of service facilities and the demand of demand points.

Figure 3 shows the locations of crime events in NYC and the results of 80-20 Analysis. As can be seen from the figure, the area with the most dense crime clusters is in Manhattan (the largest dark blue circle in the figure), followed by Queens. In the 4-5 areas with larger clusters, Brooklyn has a lighter degree of crime clustering than other locations. The Manhattan area is a very prosperous area in NYC with a large population, so there may be a lot of crimes. However, the local police department should strengthen its control to prevent vicious incidents from happening.

According to the hot spot analysis results shown in Figure 4, the distribution of existing police stations in different regions can be clearly seen. Brooklyn appears to have more police hotspots, which may reflect the relatively high level of crime in the area. This is followed by Queens and the Bronx, which also have a number of police hotspots, indicating some level of criminal activity in these areas as well. Of concern, however, are hotspots in Manhattan and Staten Island with little police presence. This situation is particularly concerning considering that Manhattan is one of the major hotspots for criminal activity. Therefore, there is a need to strengthen police deployment in these areas, especially in Manhattan, to effectively respond to criminal activities and maintain public safety. Strengthening police deployment may include increasing patrol frequency, establishing more police stations, or strengthening cooperation with communities to improve police response speed and efficiency to better protect citizens' lives and property.

Figure 5 shows the binary variable analysis of NYC strong and weak sercurity Police Precincts, based on the regional division of NYC Police Precincts. The experiment used summary within to count the number of crimes and the number of police stations in each Police Precincts, based on the binary variable of ArcGIS Pro Analyzing symbolically, the light blue area in the figure represents a place with a large number of crimes and a weak police force, the dark blue area represents a place with a large number of crimes and a large number of police forces, and the rose red area represents an area with a large number of police forces and a low number of crimes. Therefore, Light blue areas are defined as places with poor safety factors. From the picture, we can see that there is a low-security area in Manhattan, while many areas in Brooklyn and Queens are low-security, especially Brooklyn.

Figure 6 shows the vehicle range of the existing police station of 5000m calculated based on distance. From the figure, we can see that the vehicle range of the police station in Manhattan almost covers the entire area, so although it is not represented in the hot spot analysis There are obvious hot spots, but it shows obvious advantages in the analysis of the traffic range of the police station. Based on this point of view, the police force in Manhattan is relatively sufficient. In contrast, in the three districts of Staten Island, Brooklyn and Queens, the vehicle range of their police stations does not cover the entire area. Especially in Staten Island, the vehicle coverage area of the police station in this area is very small. In the next section, a comprehensive evaluation will be conducted on the service area of each police station and the number of crimes that occur in this area.

Figure 7 shows the score of each police station. The score is based on the area of the service area/crime count of each police station. The scoring mechanism is that the larger the area of the service area and the smaller the number of crimes, the better the score of the police station. high. This can reflect the effectiveness of the police station in maintaining public security within its jurisdiction. As can be seen from Figure 7, although Manhattan has a large number of police stations and a wide range of services, its score is not high. It reflects that despite the wide coverage of the police department, the number of crimes in Manhattan is relatively high, indicating problems with the resource allocation and management efficiency of the police department. In this case, simply increasing the number of police stations and expanding service areas cannot fully guarantee regional security. What is more important is to improve the work efficiency and crime prevention capabilities of the police stations. In contrast, the police departments in Queens and Brooklyn performed better in this score, especially the police department in south Brooklyn. In these areas, police departments are not only able to effectively manage larger service areas but are also relatively more successful in crime control. This is linked to local community engagement and the effective implementation of police strategies, demonstrating the important impact that good community policing and strategic planning can have on improving regional safety. In summary, the analysis in Figure 7 reveals differences in crime prevention and resource utilization efficiency among police stations in different regions. This information is important for policymakers and community leaders, who can use the data to adjust resource allocation and optimize police deployment to improve safety throughout the city.

In order to follow up the new police station site selection analysis, this step requires the evaluation of three speed models based on the previous network. The first is based on the Max speed traffic events of Open Street Map, and the second is based on the current NYC. The maximum driving speed is 25 mph. The third type is based on the 20 mph that will be implemented in NYC in the future. The experiment selected two important landmarks in New York City: the Empire State Building and New York University as the starting point and end point of the analysis. By comparing travel times under these three speed models with actual travel times provided by Google Maps, the real-world applicability of each model can be more accurately assessed. , respectively evaluate the driving time of the three speed models and the driving time of Google Maps (the results are shown in Figure 8). The results show that 20 mph is the most suitable for actual driving conditions, and the 20 mph speed model is used in the subsequent location-allocation analysis. This approach will help determine the best location for a new police station, ensuring police can respond to emergencies in the shortest possible time, improving policing efficiency and public safety across the city. Through this refined site selection analysis, police resources can be more rationally deployed and police service coverage in various districts of the city can be optimized, thereby achieving more efficient urban security management in the future.

Based on a series of analyzes in the previous sections, we can draw the following conclusions: 1) There are no obvious police station hot spots in Manhattan, and the score of each police station is not high. But its driving range covers the entire Manhattan area. Therefore, the suggestion for the Manhattan area is to maintain the existing police station while increasing the police force in each station and improving the quality of the police; 2) The traffic range of the police station in Queen and Brooklyn does not cover the entire study area, but the traffic range of the police station Score relatively few. The suggestion for the Queen and Brooklyn areas is to build new police stations in areas with poor safety factors that are not covered by the existing police stations (see the light blue area in Figure 5); 3) For the Bronx and Staten Island areas , although the service scope of its police station is poor, the number of crimes is small and the demand is small. The proposal for these two areas is to maintain the existing status and does not require large-scale changes in police force or new police stations.

Based on the above conclusion, the experiment selected four locations as new police station areas, and used the service area to calculate the service population within the driving range of 5000m (the results are shown in Figure 9). The original census tratc population is shown in Figure 10. In the next step The optimal two new police stations will be selected using the selected speed model and four locations.

In this step of analysis, the centroid and population of each census tract will be used as demand points and requirements, the four defined new police stations will be used as candidate points, and the crime count in its service area will be used as the weight, based on the above parameters. Location-Allocation analysis. The results are shown in Figure 11. The final location selection results are one in Queens and one in Brooklyn.And the map tour below show the detail of the 2 Location.

This article evaluates NYC's crime cluster areas, the safety factor of each police station, the strength of the police force in each area, and finally selects a location suitable for building a new police station based on the existing situation. But taken together, the experiment still has the following shortcomings:

1. For the analysis of crime clustering, the clustering situation of each crime type is not taken into account. There may be more thefts in residential areas and more robberies in areas with large flow of people such as shopping malls, which will affect further analyze. Making the crime cluster analysis more detailed and taking into account the distribution of different types of crimes in different regions can provide more comprehensive geographical information on crime and better guide the subsequent allocation of police resources and the formulation of public security management strategies.

2. The safety factor analysis of each police station only considers the vehicle range and the number of crimes within the range. In a real analysis there will be many more factors, such as the number of police officers and traffic congestion. Incorporating the remaining factors into the safety factor analysis can provide a more comprehensive assessment of the police station's effectiveness and ability to respond to crime, thereby providing a more accurate basis for subsequent resource allocation.

3. The construction of the speed model does not consider traffic congestion and elevation conditions. These factors affect the actual speed and time the vehicle travels. Incorporating factors such as traffic congestion and terrain elevation into the construction of the speed model can more accurately simulate the actual situation of vehicle driving and provide more reliable data support for the location and planning of police resources.

In summary, for similar future research and experiments, we can consider further refining crime cluster analysis, comprehensively considering multiple factors of the police station's safety factor, and considering more practical factors in the construction of speed models to improve research The comprehensiveness and accuracy provide more effective support and guidance for urban public security management.

Based on ArcGIS Pro and open source GIS data, this article evaluates NYC's crime cluster areas, the safety factor of each police station, the strength of the police force in each area, and finally selects a location suitable for building a new police station based on the existing situation. The conclusion is drawn as follows.

1. Uneven distribution and demand for police resources in Manhattan: Although Manhattan’s police stations cover a wide area, the relatively high number of crimes indicates problems with the allocation and management efficiency of police resources. Police deployment and strategies need to be re-evaluated to ensure they can effectively respond to high crime rates.

2. The police stations in Brooklyn and Queens are more efficient: The police stations in these two areas perform better in crime control and resource utilization efficiency, especially the police stations in southern Brooklyn. This shows that good community policing and strategic planning have a significant impact on making areas safer.

3. Mismatch between crime agglomeration and police station service areas: Some areas, such as Manhattan, have low-security areas, and the police station’s service areas are insufficiently covered. This requires the establishment of additional police stations in high-crime areas or the expansion of the service scope of existing police stations to better meet the security needs of these areas.

4. Multiple factors need to be considered comprehensively to optimize the deployment of police stations: simply increasing the number of police stations and expanding service areas cannot fully guarantee regional security. Various factors such as crime types, traffic conditions, and police quality should be considered, and these factors should be combined to deploy and allocate resources of the police station to maximize its efficiency and effectiveness.

Baumer, E. P., & Wolff, K. T. (2020). Evaluating contemporary crime drop (s) in America, New York City, and many other places. In Understanding New York’s Crime Drop (pp. 5–38). Routledge.

Kennedy, L. W., Caplan, J. M., & Piza, E. (2011). Risk clusters, hotspots, and spatial intelligence: Risk terrain modeling as an algorithm for police resource allocation strategies. Journal of Quantitative Criminology, 27, 339–362.

NYPD Complaint Data Current (Year To Date) | NYC Open Data. (n.d.). Retrieved May 11, 2024, from https://data.cityofnewyork.us/Public-Safety/NYPD-Complaint-Data-Current-Year-To-Date-/5uac-w243/about_data

OpenStreetMap. (n.d.). Retrieved May 11, 2024, from https://www.openstreetmap.org/#map=5/38.007/-95.844

Police Precincts. (n.d.). NYC Open Data. Retrieved May 11, 2024, from https://data.cityofnewyork.us/Public-Safety/Police-Precincts/78dh-3ptz