Enhancing Urban Safety: Optimal Patrol Route Strategies for Volunteer Security Squads Based on Integrated BIM-GIS Data

Abstract

Urban safety is becoming an increasingly crucial issue due to rising crime rates and urbanization. The concept of a “Safe City” aims to ensure citizen safety through effective crime prevention and rapid response strategies. Volunteer security teams play a vital role in supplementing police efforts to maintain community safety. However, current patrol routes are often set unsystematically, relying on experience, leading to inefficient resource use and reduced effectiveness in crime prevention. This study optimized patrol routes for volunteer security teams using advanced data analysis techniques and route optimization algorithms. By integrating various data sources and applying advanced algorithms, the study systematically improved patrol efficiency and effectiveness. By analyzing security facility locations, crime data, and weak areas in Gangseo-gu, this study identified gaps between infrastructure and vulnerable areas. The novelty of this research lies in its comprehensive approach to deriving a security vulnerability index and designing optimal patrol routes based on integrated BIM-GIS data. This optimized approach ensures effective coverage of critical zones, significantly enhancing the operational efficiency of volunteer security teams.

1. Introduction

Urban safety and security are increasingly critical social issues in modern society [1,2]. The rapid pace of urbanization and growing population density have led to a significant rise in crime rates globally, necessitating effective measures to ensure citizen safety [3,4]. The concept of a “Safe City” aims to create environments where citizens can live without fear, achieved through effective crime prevention and rapid response strategies [5,6]. Globally, the expansion of cities has heightened the complexities of maintaining public safety. Urban crime rates have increased, particularly in densely populated areas where social inequalities, unemployment, and inadequate policing are prevalent [7]. These trends are mirrored in South Korea’s major cities, such as Seoul, Busan, and Incheon, where shifts in crime patterns, especially crimes against vulnerable populations like women, children, and the elderly, have underscored the need for targeted safety measures [8,9]. Initiatives like Seoul’s “Safe Village” project, which focuses on enhancing security in high-crime areas through improved lighting, CCTV, and emergency systems, exemplify efforts to protect these groups [8,10].

In this context, urban safety refers to systematic efforts to protect urban populations through proactive and reactive measures, ensuring that public spaces and communities remain secure. Optimal patrol routes are essential in maintaining urban safety, as strategically planned and continuously optimized routes enable volunteer security teams and law enforcement to cover critical areas efficiently [11,12]. Integrated BIM-GIS data plays a crucial role in this process by providing a comprehensive and spatially accurate framework for analyzing and optimizing these routes. The integration of Building Information Modeling (BIM) with Geographic Information Systems (GIS) supports the identification of high-risk areas, facilitating the planning of patrol routes that are both efficient and responsive to the unique characteristics of the urban landscape [13,14].

Despite their importance, volunteer security teams often rely on experience-based, unsystematic patrol routes, leading to inefficiencies in resource use and reduced effectiveness in crime prevention [15]. Furthermore, there is a notable gap in research related to the optimization of patrol routes for volunteer security teams that specifically incorporates GIS data on vulnerable populations, such as the elderly. The absence of studies testing the prioritization of regions using such demographic data highlights the necessity and originality of this research in Gangseo District. This study seeks to address these challenges by analyzing and optimizing patrol routes for volunteer security teams, contributing to the realization of Safe Cities. By applying route optimization algorithms that consider crime occurrence data and various influencing factors, this research aims to evaluate the effectiveness of these optimized routes and derive practical policy implications.

This study explores several critical aspects of patrol route optimization within the Safe Cities framework. Key research questions focus on establishing the most effective patrol routes, assessing their impact on crime prevention, and evaluating the significance of variables such as crime frequency, time of day, and location. Hypotheses tested include the effectiveness of routes that account for crime types and frequency, the impact of time-based crime patterns on patrol efficiency, and the benefits of continuous data analysis and route optimization on overall urban safety [16,17,18]. The findings of this study are expected to enhance the operational efficiency of volunteer security teams, maximizing crime prevention impacts. Furthermore, this research offers practical implications for urban policy and safety management, contributing to the creation of safer and more secure urban environments.

2. Literature Review

This review encompasses various aspects, from the foundational principles of creating secure cities to the specific techniques used to optimize patrol routes and integrate advanced data systems. By examining these elements, we aim to provide a comprehensive understanding of the current state of research and practices in urban safety and security.

2.1. Concept of Safe and Secure Cities and Autonomous Security Teams

Safe and secure cities are urban areas designed to enhance the safety and well-being of their inhabitants [19]. These cities encompass various strategies and technologies aimed at preventing crime and ensuring rapid response to emergencies [20]. The literature identifies key elements essential for building secure cities, including urban design, infrastructure, and community involvement [21].

Urban infrastructure significantly impact crime reduction through improved lighting, surveillance systems, and the creation of community-oriented spaces [22]. Implementing advanced technologies such as CCTV, emergency alert systems, and smart city solutions is crucial for effective monitoring and incident response. Community involvement, through initiatives like neighborhood watch programs, further strengthens the overall security framework.

Autonomous security teams, which are often referred to as voluntary or community patrols, are critical components of this framework [22]. These teams, composed of local volunteers, support official law enforcement by patrolling neighborhoods, reporting suspicious activities, and providing a visible security presence [23]. The effectiveness of these teams is enhanced by the strategic planning of patrol routes, as highlighted in this study. The literature provides various models of autonomous security teams and examines their integration with formal policing efforts, which directly relates to the optimization techniques discussed in our methodology.

2.2. Patrol Route Optimization

Optimizing patrol routes for security teams involves the development of efficient and effective routes that maximize coverage and minimize response times [24]. The related literature focuses on methodologies such as graph theory and algorithms, geospatial analysis, simulation, and modeling [25].

Graph theory and algorithms, including the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP), are utilized to determine optimal patrol paths [26,27]. Geospatial analysis leverages Geographic Information Systems (GIS) to map crime hotspots, predict potential incidents, and design patrol routes that prioritize high-risk areas [28]. Additionally, simulation tools are used to test and refine patrol strategies under various scenarios, ensuring that routes are adaptable to changing conditions and emergent threats [29].

Research has also explored the integration of real-time data, such as crime reports and sensor alerts, to dynamically adjust patrol routes and improve overall effectiveness.

2.3. Integration and Visualization of BIM-GIS Data

Building Information Modeling (BIM) and Geographic Information Systems (GIS) are powerful tools for managing urban infrastructure and spatial data [30,31,32]. The literature on BIM-GIS integration emphasizes data fusion techniques, interoperability standards, and visualization approaches [33].

Data fusion techniques involve methods for combining BIM data, which provide detailed information on buildings and infrastructure, with GIS data, which offer spatial context and broader geographic insights [34]. Developing and adopting interoperability standards ensures seamless data exchange between BIM and GIS platforms, thereby facilitating more comprehensive analyses. Advanced visualization techniques enhance the understanding and usability of integrated BIM-GIS data, including 3D modeling, augmented reality (AR), and virtual reality (VR) applications, providing immersive and interactive views of urban environments [35,36].

The integration of BIM and GIS provides accurate and actionable insights into urban planning, disaster management, and security operations. It supports the creation of digital twins in cities that can be used for simulation, analysis, and decision-making processes.

This literature review highlights the critical elements of creating safe and secure cities, optimizing patrol routes, and integrating and visualizing GIS data. Urban design, technological integration, and community involvement are essential for establishing secure urban areas, and autonomous security teams play vital roles in these efforts. Optimizing patrol routes ensures efficient and effective security operations, whereas fusing BIM and GIS data enhances the precision of urban management and security tasks. The combination of these factors has significantly contributed to the improvement of urban safety and well-being.

3. Method

The methodology for this study involves a comprehensive approach to analyzing security vulnerabilities and optimizing patrol routes within Gangseo District. This process is divided into five key steps, each of which aims to systematically identify and address the areas of concern, as shown in Figure 1.

• Step 1: Security Facility Status Analysis

First, we identified the locations of security installations such as CCTV cameras, emergency bells, and security lights within Gangseo District. By mapping these facilities, we gained an understanding of their distribution across the districts. Following this, we conducted a coverage analysis for each type of facility to determine the areas that lacked sufficient security infrastructure. This analysis helped pinpoint zones that required additional security measures to enhance overall safety.

• Step 2: Crime and Security Data Analysis

Second, we collected and analyzed data from police agencies focusing on crime statistics, including five major violent crimes and security-related civil complaints. By examining these data, we identified crime occurrence patterns and selected regions that were particularly vulnerable to security issues. This step provided a detailed understanding of the crime landscape in Gangseo District.

• Step 3: Security Weak Area Analysis

The third step involved analyzing areas with potential security weaknesses. Factors such as building age, areas with permits for entertainment establishments, locations with high floating populations, and crime-prone areas, such as parks and trails, were considered. By evaluating these factors, we identified areas with increased security vulnerabilities, allowing us to focus on the regions that require security improvements.

• Step 4: Derivation of Regional Security Vulnerability Index

In the fourth step, we derived the regional security vulnerability index (SVI) for different regions within Gangseo District. This index quantifies and visually represents the security vulnerabilities across various areas, allowing for targeted crime prevention measures. The process involved several key stages:

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Variable Identification: We identified several critical variables that influence security vulnerability. These include the number of security facilities (such as CCTV cameras, emergency bells, and security lights), the frequency of major crimes (including murder, robbery, rape, theft, and assault), the average age and usage of buildings, the density of permits for entertainment establishments, the floating population, and the presence of crime-prone areas.

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Normalization: Each variable was standardized to ensure comparability. This step involved adjusting the values of each variable to a common scale, which allowed for a balanced integration of different factors contributing to the overall vulnerability index.

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Weight Assignment: Weights were then assigned to each variable according to their relative importance in contributing to security vulnerability. These weights were determined based on expert opinions and statistical analysis, ensuring that the most significant factors had a greater influence on the final index.

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Integration of Variables: The various normalized and weighted variables were then combined to produce the security vulnerability index. This index reflected the combined influence of all identified factors, providing a comprehensive measure of security risk for each region.

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Visualization: The resulting index was visualized using GIS tools, creating a detailed map of the security landscape in Gangseo District. This visualization helped to identify areas that require additional resources and attention to enhance security.

• Step 5: Optimization of Patrol Routes for Voluntary Security

The final step focused on designing optimal patrol routes for voluntary security teams based on the identified security-vulnerable areas. We visualized the security vulnerability index by street and used advanced algorithms to select efficient patrol routes. For instance, the shortest path algorithm was employed to ensure that the patrol routes were both effective and efficient in covering the most critical areas. This included a detailed visualization of the security vulnerability index for all streets in Gangseo District to ensure comprehensive coverage and improved security.

3.1. Selection of the Study Area

Gangseo-gu, Seoul, was selected as the study area due to its unique urban characteristics and security challenges. As one of the largest districts in Seoul in terms of both area and population, Gangseo-gu presents a complex urban environment that requires effective safety management. The district is home to significant infrastructure, including Gimpo International Airport, which not only increases the transient population but also introduces unique security concerns related to airport operations and high-density public areas. Additionally, Gangseo-gu hosts the headquarters of several airport-related companies, R&D facilities, and critical amenities such as large shopping malls, hospitals, and financial institutions, all of which contribute to the need for robust security measures.

The district’s diverse urban landscape, which includes residential areas, commercial zones, and industrial sectors, further complicates the task of maintaining public safety. The presence of vulnerable populations, including the elderly and transient workers, heightens the importance of optimizing patrol routes and enhancing urban safety in Gangseo-gu. Furthermore, Figure 2 illustrates the location of Gangseo-gu within Seoul, along with key facilities such as the airport, major shopping centers, and other significant landmarks, highlighting the strategic importance of this district for the study challenges that are relevant to other large metropolitan areas.

3.2. Data Collection and Visualization

This process is illustrated in Figure 3, which provides a visual explanation of the steps involved in data collection, database construction, and the subsequent analysis and visualization stages.

For research and analysis, data were gathered and refined through a structured process. The initial step involved defining and collecting the necessary data. This required identifying the source data needed for analysis, collecting data from each source, and refining it using various tools according to the data type as shown in Appendix A. The types of data handled in this study include structured data (e.g., databases and spreadsheets), semi-structured data (e.g., JSON and XML), and unstructured data (e.g., text and images) [37,38].

The database construction and analysis process began by establishing a source database and assigning attribute information. This involved integrating data on building age and usage, demographic and household information, daily active population, surveys of Seoul citizens and their daily lives, crime- and security-related data, road network data, and isochronous data. The importance of integrating diverse data types for urban analysis has been well documented in previous studies [39]. Subsequently, datasets were created to analyze current issues, such as identifying weak security areas and determining optimal patrol routes. The next step was to construct an operational database tailored to the goals of the analysis. Finally, the analysis results were derived and visualized to facilitate better understanding and decision-making. The use of advanced visualization techniques, as supported by recent research, enhances the interpretation of complex urban data and supports informed decision-making in urban safety management [40].

4. Results

This section presents the findings of our comprehensive analysis of security vulnerabilities and the optimization of patrol routes within Gangseo-gu. The results are structured to highlight the status of security facilities, crime, and security data analysis, weak security area analysis, derivation of the regional security vulnerability index, and optimization of patrol routes for voluntary security teams.

4.1. Security Facility Status Analysis

In Gangseo-gu, 1775 locations were equipped with 3617 CCTV cameras based on permit data from July 2024. As shown in Figure 4, the analysis revealed that Hwagok 1-dong, Hwagokbon-dong, and Banghwa 2-dong had the highest number of CCTVs installed. By contrast, Gayang 2-dong, Gayang 3-dong, and Deungchon 1-dong had significantly fewer CCTV units. This disparity highlights areas with potentially lower surveillance coverage, indicating the need for the strategic placement of additional security facilities to enhance overall urban safety and monitoring capabilities.

According to the latest data, Gangseo-gu has 32,780 security lights, 841 emergency bells, and 13,764 smart streetlights. As shown in Figure 5, the combined total of these facilities was greatest in Banghwa 2-dong, Gonghang-dong, and Gayang 1-dong. Conversely, Gayang 3-dong, Hwagok 2-dong, and Gayang 2-dong have fewer security installations. Notably, Gonghang-dong has a particularly high number of security lights. This distribution reveals areas with varying levels of security infrastructure, suggesting that additional installations may be necessary to enhance urban safety and monitoring capabilities.

By filtering the parcels in Gangseo-gu that did not have the previously mentioned security facilities, such as CCTVs, security lights, emergency bells, and smart streetlights, and visualizing these data as a heat map, it was found that out of the 42,850 parcels in Gangseo-gu, 11,759 parcels had these security facilities, while 31,092 parcels did not. It is important to note that these results are based on point data (X and Y coordinates) and may differ from the actual coverage provided by security facilities, as shown in Figure 6.

Examining the parcels lacking the aforementioned security facilities, as illustrated in Figure 7, it appears that Banghwa 2-dong and Gonghang-dong are deficient in security infrastructure. However, because Gimpo Airport is located in Banghwa 2-dong and Gonghang-dong, the administrative districts lacking sufficient security facilities are Hwagok 1-dong, Hwagokbon-dong, and Banghwa 1-dong.

4.2. Crime and Security Data Analysis

As shown in Figure 8, based on an analysis of public crime statistics, Gangseo-gu’s precincts and district police stations exhibited varying crime grades. Deungchon 3-dong (Gayang Police District) had the highest crime grade of 5. A visualization map was created to show crime grades and detailed crime levels for each precinct and district police station jurisdiction. Currently, data are being compiled to define the jurisdictional boundaries for each precinct and district police station.

4.3. Security Weak Area Analysis

As shown in Figure 9, as of May 2024, the building register header was processed to analyze the weak security areas. By combining parcel spatial data with administrative district spatial data, a visualization was created to highlight parcels and administrative districts with a high number of older buildings. The analysis revealed that the administrative districts with the most aged buildings, defined as those over 40 years old, were Banghwa 2-dong (655 buildings), Hwagok 1-dong (336 buildings), and Gonghang-dong (296 buildings).

As shown in Figure 10, data from the Local Government Permit Data Open Platform were used to compile the locations of entertainment establishments such as bars and karaoke lounges. After filtering for currently operating businesses, a heat map was generated based on their locations. The districts with the highest numbers of establishments were Hwagok 1-dong (122 establishments), Hwagok 6-dong (62 establishments), and Gayang 1-dong (37 establishments).

As shown in Figure 11, a dataset of the resident population in Seoul by enumeration district as of June 2024 was created using data from the Seoul Open Data Plaza. The morning peak (7–9 a.m.) and evening peak (5–7 p.m.) periods were also considered. Visualization was performed by assigning location data to each enumeration district to identify areas with high population densities. The darker the color, the more densely populated the area within Gangseo-gu during peak times.

As shown in Figure 12, using data on parks within Gangseo-gu, a dataset was constructed to calculate park density. Based on the calculated density, a heat map was created to identify crime-prone areas using the central points of the parks. Darker colors on the heat map indicate higher park density in the Gangseo-gu area.

To analyze weak security areas, several factors were considered: parcels with a high building age, areas with permits for entertainment establishments, locations with high resident populations, and crime-prone areas such as parks. As shown in Figure 13, these factors were combined to perform hotspot spatial analysis. Gangseo-gu was divided into a 10 m × 10 m grid. The density of each of these data points was calculated and summed, resulting in a combined density for each grid cell. A spatial autocorrelation analysis was performed using the following steps.

• Define spatial neighbors using an adjacency matrix.
• Transform into a spatial weight matrix to calculate spatial correlation.
• Compute the Getis–Ord’s G* (star) statistic.

The Getis–Ord’s G* statistic measures how observations at specific locations differ from neighboring values, allowing for the detection of hotspots and cold spots in spatial data.

4.4. Derivation of Regional Security Vulnerability Index

We aimed to derive a regional security vulnerability index for Gangseo-gu to identify areas with weak security and visually present zones that require enhanced security measures and crime prevention. To achieve this, we first defined several key variables: the number of security facilities (CCTV, emergency bells, security lights) as s, the number of crimes within the jurisdiction of a precinct (murder, robbery, rape, theft, and assault) as c, the average age and usage of buildings as b, the density of permits for entertainment establishments as e, the floating population as p, and the presence of crime-prone areas as d.

Each of these variables was normalized to a value between 0 and 1. After normalization, weights were assigned to each variable based on expert opinion or statistical analysis. The Crime Index (CI), representing the security vulnerability index, was calculated using the following formula:

CI = w1 × (1 − S′) + w2 × C′ + w3 × B′ + w4 × E′ + w5 × P′ + w6 × D′,

where w represents the weights for each variable, and S′, C′, B′, E′, P′, and D′ are the normalized values of each variable. The weights were defined as follows: w1 is the importance of security facilities, w2 is the importance of crime frequency, w3 is the importance of building age and usage, w4 is the importance of entertainment establishment density, w5 is the importance of the floating population, and w6 is the importance of crime-prone areas.

To illustrate the calculation, we assume the following values:

• S is 50 (with Smin = 0 and Smax = 100);
• D is 1 (indicating a crime-prone area);
• C was 45 (murder, 2; robbery, 5; rape, 3; theft, 20; and assault, 15; with Cmin = 0 and Cmax = 100);
• B is 30 (with Bmin = 0 and Bmax = 50);
• E is 10 (with Emin = 0 and Emax = 20);
• P is 200 (with Pmin = 50 and Pmax = 300).

Using these values, we normalized each variable with weights defined as w1: 0.2, w2: 0.3, w3: 0.1, w4: 0.15, w5: 0.15, and w6: 0.1. The security vulnerabislity index (CI) was calculated as

CI = 0.2 × (1 − 0.5) + 0.3 × 0.45 + 0.1 × 0.6 + 0.15 × 0.5 + 0.15 × 0.6 + 0.1 × 1

CI = 0.1 + 0.135 + 0.06 + 0.075 + 0.09 + 0.1                      

CI = 0.56                                            

This example demonstrates the calculation of the security vulnerability index (CI) using normalized values and the assigned weights for each variable. As shown in Figure 14, the security vulnerability index was classified into 100 grades and visualized on a map, where areas closer to grade 100 indicated higher security vulnerability. These results align with the previous hotspot analysis, showing that the administrative districts with the highest vulnerability indices are Banghwa 1-dong, Hwagok 1-dong, Hwagokbon-dong, and Hwagok 2-dong.

4.5. Optimization of Patrol Routes for Voluntary Security

The visualization of vulnerable areas is depicted in Figure 15. Road shape data were constructed using the Address-Based Industry Support Service. By spatially joining the previously derived “high vulnerability parcels” with “road spatial data”, roads with high vulnerability indices were filtered. The administrative districts containing roads with the highest vulnerability indices corresponded to those identified in the earlier vulnerability index derivation.

A visualization of the patrol routes is shown in Figure 16. The center points of the road shapes with high vulnerability indices were identified. (In the actual analysis, the routes were based on the actual road network rather than center points.) The Dijkstra algorithm was used to determine the shortest paths between road shapes within 250 m. The edges were connected to derive the optimal patrol routes, which were then visualized.

4.6. Discussion

This study aimed to enhance urban safety in Gangseo-gu by optimizing patrol routes for voluntary security teams, a crucial aspect in the broader context of creating safer cities. The findings presented in Section 4 directly address the key objectives and themes of the study, which were focused on understanding the distribution and effectiveness of existing security infrastructure, analyzing crime and security-related data, identifying areas with heightened security vulnerabilities, and ultimately designing optimal patrol routes.

Linking security facility analysis to objectives: The initial analysis of security facility distribution revealed significant disparities in the placement of CCTV cameras, security lights, emergency bells, and smart streetlights across Gangseo-gu. These findings highlight areas with insufficient surveillance and security coverage, which directly relate to the study’s objective of identifying regions that require enhanced security infrastructure. The strategic placement of additional facilities in these under-served areas aligns with the broader goal of optimizing urban safety measures.

Crime and security data analysis: The crime and security data analysis further supported the study’s objectives by pinpointing regions with higher crime rates and inadequate policing resources. The visualization of crime grades across different precincts provided actionable insights into the specific needs of each district. This analysis is crucial for prioritizing regions that require more intensive patrols, which is directly linked to the study’s theme of optimizing patrol routes to maximize crime prevention.

Security weak area analysis: The analysis of weak security areas, considering factors like building age, entertainment establishment density, population density during peak hours, and the presence of crime-prone areas such as parks, was integral to identifying zones with increased security risks. These factors were combined in a hotspot analysis to pinpoint the most vulnerable areas within Gangseo-gu. This comprehensive approach addresses the study’s theme of integrating multiple data sources to provide a holistic view of urban security challenges.

Derivation of regional security vulnerability index: The derivation of the security vulnerability index (SVI) was a critical step in quantifying the security risks across different regions of Gangseo-gu. The SVI provided a clear, visual representation of areas that require immediate attention, which directly ties into the study’s objective of prioritizing regions for security enhancements. Although the SVI effectively identifies vulnerable areas, the methodology for calculating and weighing the variables remains a key area for future research, particularly regarding validation through empirical testing or simulations.

Optimization of patrol routes: The final objective of the study was to design optimal patrol routes for voluntary security teams. By integrating the SVI with advanced algorithms like the Dijkstra algorithm, we were able to propose routes that efficiently cover the most critical areas. This step directly supports the study’s theme of enhancing the operational efficiency of security teams and contributes to the broader goal of urban safety.

Future research and validation: While this study successfully visualized and mapped optimal patrol routes, it is important to acknowledge that the validation of these results was not conducted within the scope of this research. Future work should focus on empirically validating the proposed patrol routes to confirm their effectiveness in real-world scenarios. This will be crucial in demonstrating that the methodologies used are not only theoretically sound but also practically effective in enhancing urban security.

5. Conclusions

This study aimed to enhance urban safety in Gangseo District by optimizing patrol routes for volunteer security teams. Through a comprehensive methodology that included the identification and analysis of security facility locations, crime and security data analysis, security weak area analysis, derivation of a regional security vulnerability index, and optimization of patrol routes, we provided actionable insights and practical implications for improving community safety. The study began by mapping the locations of security installations such as CCTV cameras, emergency bells, and security lights, and conducting a coverage analysis to identify areas lacking sufficient infrastructure. We then analyzed crime data and security-related complaints to identify vulnerable regions. Next, we assessed security weaknesses by considering factors such as building age, entertainment permits, high-floating population areas, and crime-prone zones, which helped identify areas with heightened vulnerabilities. A security vulnerability index was calculated and visualized for better planning and decision-making. Finally, we optimized patrol routes for voluntary security teams using the vulnerability index and advanced algorithms, ensuring efficient coverage of critical areas to enhance overall security.

The findings of this study have significant practical implications for urban policies and safety management. The effectiveness of volunteer security teams can be maximized by systematically optimizing patrol routes and integrating advanced data analysis techniques. This approach not only enhances the operational efficiency of these teams but also contributes to creating a safer and more secure urban environment for all residents. However, it is important to note that while the study successfully visualized various GIS data and mapped optimal patrol routes, the validation of these results was not conducted within the scope of this research. Future work should focus on validating the proposed patrol routes through empirical studies or simulations to confirm the effectiveness of the methodology. Such validation would be crucial in proving that the approach is not only theoretically sound but also practically effective in real-world scenarios.

Additionally, one limitation of this study is the arbitrary selection of weights for the variables used in the security vulnerability index. Future research could address this by incorporating more rigorous techniques, such as the Analytic Hierarchy Process (AHP) or other expert-based methods, to determine the weights more systematically. These methods allow for a structured and objective determination of weights, ensuring that they accurately reflect the relative importance of each factor in contributing to urban security vulnerabilities. This enhancement would improve the robustness and credibility of the security vulnerability index.

In addition, future research could explore the use of more advanced visualization techniques, such as 3D mappings or interactive maps, to present the data more comprehensively. These enhanced visualizations could provide deeper insights and more dynamic representations of urban safety scenarios, thereby improving the understanding and practical application of the findings. The integration of comprehensive data analysis, advanced algorithms, and strategic planning is essential for improving urban safety. The methods and findings of this study provide a robust framework for enhancing community security and serve as a model for other urban areas facing similar challenges. Future research should continue to explore these methods, include validation efforts, and consider the use of advanced visualization tools to strengthen the evidence supporting the effectiveness of optimized patrol routes.