Fuzzy Cluster–Based Method of Hotspot Detection with Limited Information

Bandyopadhyaya, Ranja; Mitra, Sophie

doi:10.1080/19439962.2014.959583

Cited by 16 publications

(6 citation statements)

References 17 publications

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“…e study of travel characteristics based on big data and urban computing is no longer limited to the traditional coarsegrained classification. In recent years, the thinning hotspot extraction model-based density field has become an important tool to analyze OD features and tracks [26,27].…”

Section: Mining Dispatch Points Based On Hotspot Detectionmentioning

confidence: 99%

The Reliability Model for Bike‐Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing

Sun

Lü

2022

Discrete Dynamics in Nature and Society

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In this article, a novel reliability model for bike-sharing dispatch is established using a hypothesis test. Based on the bike-sharing trajectory data from hotspot detection, we first perform the kernel density analysis to identify the dispatch points. As a result, a buffer area of 500 meters radius is designated as the studied dispatch area. From a systematic perspective, the reliability of the dispatch system is user-oriented during an ideal period when shared bikes constantly enter and leave the area. We propose the performance function of bike-sharing dispatch, in which the difference between origin and destination (OD) is defined as the main parameter of the failure probability of the system. By adopting different distribution forms, including Poisson distribution, Rayleigh distribution, exponential distribution, normal distribution, and gamma distribution, we examine the distribution characteristics of OD differences. The maximum likelihood estimation (MLE) technique is applied for model calibration, and chi-squared statistics are used to identify the acceptance of the null hypothesis. Finally, we take Beijing city as a case to verify this model. The results show that among many distribution models, the fitting goodness of normal distribution is the best. According to the properties and parameters of the distribution functions, we solve the dispatch scale for bike sharing at different confidence levels, allowing the dispatch strategy to be more flexible. Moreover, we find that the variation of dispatch quantity across different time periods and locations follows a systematic fluctuating trend.

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Section: Mining Dispatch Points Based On Hotspot Detectionmentioning

confidence: 99%

The Reliability Model for Bike‐Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing

Sun

Lü

2022

Discrete Dynamics in Nature and Society

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“…Based on the Big Data and Urban Computing, the study on travel characteristics is no longer limited to the traditional coarse-grained classification. The refined hotspot extraction model based on the density field has become an important tool for analyzing the characteristics of OD and tracking in recent years [18] [19].…”

Section: ) Hotspot Detection Based On Kernel Density Analysismentioning

confidence: 99%

Evaluation of Bus Accessibility Based on Hotspot Detection and Matter-Element Analysis

Chao

Quan

2020

IEEE Access

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The paper aims to assess bus accessibility considering the matching between supply and demand for effectively optimizing the level and fairness of urban public transport service, which realizes the quantification of regional balance and accurate positioning the area with the worst balance. We firstly employ hotspot detection procedure based on taxi trajectory data and kernel density analysis to identify the travel sensitive areas, the heat values of which are deployed to represent travel demand spatial-temporally and evaluate weight factors for bus accessibility modeling. Matter-element theory is selected to establish multi-parameter evaluation model of bus accessibility, which has potential for solving incompatibility problems by systematically considering all factors. The correlations between accessibility indexes and heat value are deployed to evaluate weight factors rather than analytic hierarchy process or expert assessment method to improve subjectivity and dynamic updating. An index called the Level Ratio of Accessibility to Demand (LRAD) is addressed finally to quantify the balance between accessibility supply and travel demand of travel sensitive areas, which identifies the regional imbalance to assist the public transport system assignment. Xi'an, a large city, is selected as a case study for methodology verification. Bus accessibility degree of the whole city as well as its travel sensitive areas is evaluated by the matter-element model. It is found the bus transport accessibility of Xi'an is moderate level(M 3). The LRAD results identify the priority-processing area with the poor balance between accessibility supply and travel demand, which is cross referenced with local urban plans for verification.

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“…K-medoids [8] is used to detect hot spots in disease analysis [9] and crime analysis [10]. Fuzzy C-means (for short FCM) [11][12][13] is used by various authors to detect hot spots in crime analysis [14][15][16][17], road traffic crashes [18], and disease analysis [19].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

Cardone

Martino

2022

Electronics

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Cluster-based hot spot detection is applied in many disciplines to analyze the locations, concentrations, and evolution over time for a phenomenon occurring in an area of study. The hot spots consist of areas within which the phenomenon is most present; by detecting and monitoring the presence of hot spots in different time steps, it is possible to study their evolution over time. One of the most prominent problems in hot spot analysis occurs when measuring the intensity of a phenomenon in terms of the presence and impact on an area of study and evaluating its evolution over time. In this research, we propose a hot spot analysis method based on a fuzzy cluster hot spot detection algorithm, which allows us to measure the incidence of hot spots in the area of study. We analyze its variation over time, and in order to evaluate its reliability we use a well-known fuzzy entropy measure that was recently applied to measure the reliability of hot spots by executing fuzzy clustering algorithms. We apply this method in crime analysis of the urban area of the City of London, using a dataset of criminal events that have occurred since 2011, published by the City of London Police. The obtained results show a decrease in the frequency of all types of criminal events over the entire area of study in recent years.

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Fuzzy Cluster–Based Method of Hotspot Detection with Limited Information

Cited by 16 publications

References 17 publications

The Reliability Model for Bike‐Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing

The Reliability Model for Bike‐Sharing Dispatch Based on Hotspot Detection and Hypothesis Test: A Case Study in Beijing

Evaluation of Bus Accessibility Based on Hotspot Detection and Matter-Element Analysis

Fuzzy-Based Spatiotemporal Hot Spot Intensity and Propagation—An Application in Crime Analysis

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