GIS-Based Simulated Annealing Algorithm for the Optimum Location of Fire Stations in the Madrid Region, Spain: Monitoring the Collapse Index

Martín-Fernández, Susana; Martínez-Falero, Eugenio; Peribáñez, José Ramón; Ezquerra, A.

doi:10.3390/app11188414

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…e corresponding datasets should be constructed before the prediction results are calculated. In this paper, the appropriate function is used to clarify the relationship between the simulated annealing algorithm and community public sports (Martin-Fernandez [11]). Under the conditions of preset accuracy and threshold constraints, the moderate function performs multiple iterative analyses, guides the operator to calculate in the positive direction, reduces the occurrence rate of local extreme values and "false eigenvalues" (Petroff [12]), and improves the accuracy of predictive results, as shown by the following formula:…”

Section: E Accuracy Of the Results For Predicting The Level Of Public...mentioning

confidence: 99%

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

Zhen

2022

Computational Intelligence and Neuroscience

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The state vigorously develops the application of community sports and takes it as an important unit of social and economic development. With the development of the economy, residents’ living standards are getting better, which also greatly promotes the development of community public sports. Community public sports have made great progress in terms of scale, hardware, and services, and their impact on society is getting worse. Come bigger. However, there are still some deficiencies in the development of community sports. How to accurately evaluate and predict its facilities, construction, and service levels and put forward corresponding development strategies is an urgent problem to be solved in the field of community public sports. In this paper, an iterative simulated annealing algorithm is proposed to evaluate the level of public sports services and help community public sports institutions identify their own development deficiencies. Through the simulation analysis of the community sports organizations, community members, venue facilities and funds, venue facilities and funds, administrators, and the community sports activities, it is found that the identification accuracy of the evaluation results of the five indicators is above 95%, while the initial threshold of community public sports and social and economic development and the prediction accuracy required by the standard of development status are both 10%. At the same time, the recognition time of the evaluation results in the five indicators is less than 7 seconds. In community public sports development trend prediction and service level judgment, the identification time of the evaluation result is less than 4 seconds, and the overall judgment result is better.

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Section: E Accuracy Of the Results For Predicting The Level Of Public...mentioning

confidence: 99%

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

Zhen

2022

Computational Intelligence and Neuroscience

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“…Table 1 presents various techniques used by previous studies in the optimisation of fire station locations, considering a global perspective. Previous studies employed various techniques such as the Analytical Hierarchy Process (AHP) in Nepal (Chaudhary et al, 2016) and multiobjective spatial optimisation model (Chen et al, 2023b;Tao et al, 2022) in China to Greedy Adding and Greedy Adding with Substitution in Indonesia, and simulated annealing methods in Spain (Fernández et al, 2021). Some studies focused in integrated road-network accessibility (Tian et al, 2023) in China and location-allocation model (KC et al, 2018;Shahparvari et al, 2020;Tian et al, 2023) to examine the geographic distribution and optimize fire service coverage in Australia.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining fire service coverage and potential sites for fire station locations in Kathmandu, Nepal

KC,

Ardianto,

Wang

2024

Urban Info

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In the densely populated Kathmandu Metropolitan City, inadequate fire service coverage poses a significant fire risk. With the demand for fire services increasing due to rapid growth in population and infrastructure development, the limited number of fire stations, insufficient human and physical resources, poor street connectivity, narrow streets, and delays in response to calls for emergency services have resulted in significant property loss, environmental impact, serious injuries, and even loss of life. Although a substantial body of research exists on understanding fire patterns and fire station site selection in developed countries, there is a noticeable lack of similar research in developing countries, which can be attributed to a lack of available data and interest. This study utilises ward-level urban fire incident data in Kathmandu from July 2019 to February 2023 to examine the spatial pattern of fire risk, fire service coverage and identify potential sites for fire stations. Results reveal a significantly higher fire rate in the central part of the city. Fire rescue service cannot reach most areas in Kathmandu within 5 min, with some areas exceeding 10 min response time. The findings from this study will enable fire agencies to enhance the operational efficiency of emergency response and help develop policies and programs related to fire risk prevention and mitigation. This includes targeted education and awareness campaigns and the allocation of finite fire service resources, such as the strategic placement of fire stations.

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“…In the same year, Jiang et al considered how negative factors, such as current fire risk, land cover, the spatial distribution of fire stations, and traffic congestion, affect the choice of fire station location [1]. The locations of optimized facilities are mainly divided into the following types: commercial enterprise location [16,17], fire station location [18][19][20], medical facility location [21][22][23], school location, and school district division [24][25][26]. In addition, one must consider airport locations [27], the optimal layout of rural settlements [28,29], transport-hub locations [30], refuge location [31], logistics distribution centers, etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-Criterion Spatial Optimization of Future Police Stations Based on Urban Expansion and Criminal Behavior Characteristics

Jiang

Guo²,

Yan³

2022

IJGI

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Lanzhou’s rapid development has raised new security challenges, and improving public safety in areas under the jurisdiction of police stations is an effective way to address the problem of public security in urban areas. Unfortunately, the existing studies do not consider how factors such as future land changes, building functions, and characteristics of criminal behavior influence the choice of areas for police stations and the optimization of police stations with respect to traffic congestion. To solve these problems, we apply multiple methods and multi-source geospatial data to optimize police station locations. The proposed method incorporates a big data perspective, which provides new ideas and technical approaches to site selection models. First, we use the central city of Lanzhou as the study area and erase the exclusion areas from the initial layer to identify the undeveloped areas. Second, historical crime data, point of interest, and other data are combined to assess the potential crime risk. We then use the analytic hierarchy process to comprehensively assess undeveloped areas based on potential crime hotspots and on socioeconomic drivers and orography. In addition, according to China’s Road Traffic Safety Law and the current traffic congestion in the city, a minimum speed is determined, so that the target area can be reached in time even in congested traffic. Finally, we draw the spatial coverage map of police stations based on the location-allocation model and network analysis and optimize the map by considering the coverage rate of high-risk areas and building construction, in addition to maintenance and other objectives. The result shows that crime concentrates mainly in densely populated areas, indicating that people and wealth are the main drivers of crime. The differences in the spatial distribution of crime hotspots and residential areas at different spatial scales mean that the ratio of public security police force to household police force allocated to different police stations is spatially nonuniform. The method proposed herein reduces the overlap of police station service areas by 22.8% and increases the area coverage (12.01%) and demand point coverage (7.25%). The area coverage means an area potentially accessible within five minutes, and point coverage implies an effective drive. Within reasonable optimization, this allows us to eventually remove 13 existing police stations and add 24 candidate police stations.

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GIS-Based Simulated Annealing Algorithm for the Optimum Location of Fire Stations in the Madrid Region, Spain: Monitoring the Collapse Index

Cited by 5 publications

References 45 publications

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

An Annealing Model Analysis and Research on the In of Community Public Sports on Economic Development

Examining fire service coverage and potential sites for fire station locations in Kathmandu, Nepal

Multi-Criterion Spatial Optimization of Future Police Stations Based on Urban Expansion and Criminal Behavior Characteristics

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