Research on the Change of Land Use Agglomeration Based on Kernel Density Estimation and Hot Spot Analysis

2021 ◽  
pp. 987-1003
Author(s):  
Aijia Zhong ◽  
Guang Yang
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spot ◽  
Kernel Density ◽  
Hot Spot Analysis ◽  
Spot Analysis

scholarly journals Use of Spatial Analysis Techniques to Identify Statistically Significant Crash Hot Spots in Metropolitan Melbourne

2020 ◽  
Vol 31 (4) ◽  
pp. 36-58
Author(s):  
Elizabeth Hovenden ◽  
Gang-Jun Liu
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Hot Spots ◽  
Hot Spot ◽  
Kernel Density ◽  
Spatial Dependency ◽  
Moran's I ◽  
Analysis Techniques ◽  
Global Moran’S I

Understanding where, when, what type and why crashes are occurring can help determine the most appropriate initiatives to reduce road trauma. Spatial statistical analysis techniques are better suited to analysing crashes than traditional statistical techniques as they allow for spatial dependency and non-stationarity. For example, crashes tend to cluster at specific locations (spatial dependency) and vary from one location to another (non-stationarity). Several spatial statistical methods were used to examine crash clustering in metropolitan Melbourne, including Global Moran’s I statistic, Kernel Density Estimation and Getis-Ord Gi* statistic. The Global Moran’s I statistic identified statistically significant clustering on a global level. The Kernel Density Estimation method showed clustering but could not identify the statistical significance. The Getis-Ord Gi* method identified local crash clustering and found that 15.7 per cent of casualty crash locations in metropolitan Melbourne were statistically significant hot spots at the 95 per cent confidence level. The degree, location and extent of clustering was found to vary for different crash categories, with fatal crashes exhibiting the lowest level of clustering and bicycle crashes exhibiting the highest level of clustering. Temporal variations in clustering were also observed. Overlaying the results with land use and road classification data found that hot spot clusters were in areas with a higher proportion of commercial land use and with a higher proportion of arterial and sub-arterial roads. Further work should investigate network based hot spot analysis and explore the relationship between crash clusters and influencing factors using spatial techniques such as Geographically Weighted Regression.

Case Study of Crash Severity Spatial Pattern Identification in Hot Spot Analysis

2019 ◽  
Vol 2673 (9) ◽  
pp. 684-695 ◽  
Author(s):  
Myungwoo Lee ◽  
Aemal J. Khattak
Keyword(s):  
Density Estimation ◽  
Motor Vehicle ◽  
Safety Concern ◽  
Hot Spot ◽  
Estimation Method ◽  
Spatial Relationship ◽  
Crash Severity ◽  
Hot Spot Analysis ◽  
Spot Analysis

Traffic crash hot spot analyses allow identification of roadway segments that may be of safety concern. Understanding geographic patterns of existing motor vehicle crashes is one of the primary steps for geostatistical-based hot spot analysis. Much of the current literature, however, has not paid particular attention to differentiating among cluster types based on crash severity levels. This study aims at building a framework for identifying significant spatial clustering patterns characterized by crash severity and analyzing identified clusters quantitatively. A case study using an integrated method of network-based local spatial autocorrelation and the Kernel density estimation method revealed a strong spatial relationship between crash severity clusters and geographic regions. In addition, the total aggregated distance and the density of identified clusters obtained from density estimation allowed a quantitative analysis for each cluster. The contribution of this research is incorporating crash severity into hot spot analysis thereby allowing more informed decision making with respect to highway safety.

scholarly journals Unfolding Spatial-Temporal Patterns of Taxi Trip based on an Improved Network Kernel Density Estimation

2020 ◽  
Vol 9 (11) ◽  
pp. 683
Author(s):  
Boxi Shen ◽  
Xiang Xu ◽  
Jun Li ◽  
Antonio Plaza ◽  
Qunying Huang
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Network Flow ◽  
Network Flows ◽  
Kernel Density ◽  
Temporal Patterns ◽  
Flow Patterns ◽  
Urban Traffic ◽  
Mobility Data

Taxi mobility data plays an important role in understanding urban mobility in the context of urban traffic. Specifically, the taxi is an important part of urban transportation, and taxi trips reflect human behaviors and mobility patterns, allowing us to identify the spatial variety of such patterns. Although taxi trips are generated in the form of network flows, previous works have rarely considered network flow patterns in the analysis of taxi mobility data; Instead, most works focused on point patterns or trip patterns, which may provide an incomplete snapshot. In this work, we propose a novel approach to explore the spatial-temporal patterns of taxi travel by considering point, trip and network flow patterns in a simultaneous fashion. Within this approach, an improved network kernel density estimation (imNKDE) method is first developed to estimate the density of taxi trip pick-up and drop-off points (ODs). Next, the correlation between taxi service activities (i.e., ODs) and land-use is examined. Then, the trip patterns of taxi trips and its corresponding routes are analyzed to reveal the correlation between trips and road structure. Finally, network flow analysis for taxi trip among areas of varying land-use types at different times are performed to discover spatial and temporal taxi trip ODs from a new perspective. A case study in the city of Shenzhen, China, is thoroughly presented and discussed for illustrative purposes.

scholarly journals FAST INSIGHT ABOUT THE SEVERITY OF HURRICANE IMPACT WITH SPATIAL ANALYSIS OF TWITTER POSTS

2019 ◽  
Vol XLII-4/W14 ◽  
pp. 221-225
Author(s):  
K. Spasenovic ◽  
D. Carrion ◽  
F. Migliaccio ◽  
B. Pernici
Keyword(s):  
Spatial Distribution ◽  
Hot Spot ◽  
Kernel Density ◽  
Comprehensive Analysis ◽  
High Impact ◽  
Hot Spot Analysis ◽  
The Usa ◽  
Hurricane Impact ◽  
Density Map ◽  
Spot Analysis

<p><strong>Abstract.</strong> Social media could be very useful source of data for a people interested in disasters, since it can provide them with on-site information. Posted georeferenced messages and images can help to understand the situation of the area affected by the event. Considering this type of resource as a real-time crowdsource of crisis information, the spatial distribution of geolocated posts related to an event can represent an early indicator of the severity of impact. The aim of this paper is to explore the spatial distribution of Twitter posts related to hurricane Michael, occurred in the USA in 2018 and to analyse their potential in providing a fast insight about the event impact. Kernel density estimation has been applied to explore the spatial distribution of Twitter posts, after which Hot Spot analysis has been performed in order to analyse the spatiotemporal distribution of the data. Hot Spot analysis has shown to be the most comprehensive analysis, detecting the area of high impact. The Kernel density map has shown to be useful as well.</p>

APPLICATION OF KERNEL DENSITY ESTIMATION TO IDENTIFY MOTORCYCLE THEFT HOT SPOTS IN KUCHING, SARAWAK

2021 ◽  
Vol 19 ◽  
Author(s):  
Norita Jubit ◽  
Tarmiji Masron ◽  
Azizan Marzuki
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Spatial Data ◽  
Hot Spot ◽  
Kernel Density ◽  
City Centre ◽  
Prevention Measures ◽  
Sector Boundary ◽  
Police Station ◽  
Road Data

Motorcycle theft is the most frequently reported cases worldwide, including in Malaysia. This study aims to identify the hot spot areas for motorcycle theft in Kuching. The spatial data include police station sector boundary, road data and latitud and longitude data while attribute data consists of motorcycle theft by year, address of the incident and time. Kernel Density Estimation (KDE) helps to find the hot spot areas of motorcycle theft. Motorcycle theft in Kuching has been reported as more frequent during the day at 54.8% and at 45% during the night from the year 2015 to 2017. Hot spot locations change by year and time. The study found that most of the hot spot areas of motorcycle theft were detected within the Sentral boundary. This indicates that the city centre is an area with a high density of motorcycle theft. This study can help authorities to improve the prevention measures for motorcycle theft while the findings can help in preventing motorcycle theft by police sector boundary.

Spotting Premium Hot Spots for Urban Tourism Based on Facebook and Foursquare Data Using VGI and GIS

2021 ◽  
pp. 159-186
Author(s):  
José Gomes dos Santos ◽  
Liliana Raquel Simões Azevedo ◽  
Luís Carlos Roseiro Leitão
Keyword(s):  
Social Networks ◽  
Cluster Analysis ◽  
Spatial Analysis ◽  
Density Estimation ◽  
Hot Spot ◽  
Kernel Density ◽  
Urban Tourism ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Spot Analysis

Spatial modeling always involves choices. The existence of constraints, the uncertainty and even the reliability of the data, the purposes and the applications of the studies make these reflections a kind of guiding compass for GIS analysts. Building on a previous exercise of data acquisition (check-ins) based on two digital social networks (DSN – Facebook and Foursquare) and on the awareness of the use of volunteered geographic information (VGI) generated by tourists through DSN, this work aims to evaluate the contribution of spatial analysis applied to urban tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis, and identification of patterns have thus been implemented with the purpose of evaluating and comparing the results obtained through the application of two techniques of spatial analysis, kernel density estimation (KDE) and optimized hot spot analysis (OHSA) and inverse distance weighting (IDW) interpolation.

scholarly journals An Evaluation and Region Division Method for Ecosystem Service Supply and Demand Based on Land Use and POI Data

2020 ◽  
Vol 12 (6) ◽  
pp. 2524
Author(s):  
Xiumei Tang ◽  
Yu Liu ◽  
Yuchun Pan
Keyword(s):  
Land Use ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Ecosystem Service ◽  
Kernel Density ◽  
Ecosystem Service Value ◽  
Service Demand ◽  
Service Value ◽  
Cluster Distribution ◽  
Region Division

Mastering the regional spatial differences of ecosystem service supply and ecosystem service demand is of great significance to scientifically planning the development and utilization of national land and maintaining healthy development of ecosystems. Based on the relationship analysis of ecosystem service supply and ecosystem service demand, this study explored the regional ecosystem service supply by ecosystem service value based on grid data and constructed an ecosystem service demand evaluation model that integrated the construction land ecosystem service demand equivalent for static aspects and the point of interest (POI) kernel density estimation for dynamic aspects on the basis of land use and POI data. In the end, it put forward a region division method for ecosystem service supply and ecosystem service demand and conducted an empirical analysis of Haidian District, Beijing. The following results were concluded: (1) the ecosystem service value of different grids in Haidian District was between RMB (Chinese monetary unit, Yuan) 0 and RMB 2.4787 million. In terms of spatial distribution, the ecosystem service supply took on an obvious trend of gradual decrease from the northwest to the southeast, with major ecosystem service supply coming from the northwest. (2) The construction land ecosystem service demand equivalent of Haidian District was characterized by a multicenter cluster: the high equivalent area was in the southeast, while the equivalent of the northwest was relatively low. POI kernel density estimation demonstrated cluster distribution, with a high kernel density estimation in the southeast, a lower kernel density estimation in the central part, and the lowest kernel density estimation in the northwest. The ecosystem service demand index also showed cluster distribution: high index in the southeast, low index in the northwest, and prominent sudden changes from the central part to the south. (3) The bivariate local spatial autocorrelation cluster diagram method was used to divide five types of ecosystem service supply and ecosystem service demand, namely non-significant correlation region, high ecosystem service supply and high ecosystem service demand region, high ecosystem service supply and low ecosystem service demand region, low ecosystem service supply and high ecosystem service demand region, low ecosystem service supply and low ecosystem service demand region. Grids with the highest ratio belonged to the non-significant correlation region; the distribution of low ecosystem service supply and high ecosystem service demand region had the greatest concentration, mainly in the southeast; the grids of high ecosystem service supply and low ecosystem service demand region were mainly present in the northwest and in a continuous way; the grids of low ecosystem service supply and low ecosystem service demand region, and high ecosystem service supply and high ecosystem service demand region were extremely few, with sporadic distribution in the central part. The research results could provide a basis for the adjustment and fine management of regional land use structure.

scholarly journals Spatiotemporal Patterns of Small for Gestational Age and Low Birth Weight Births and Associations With Land Use and Socioeconomic Status

2019 ◽  
Vol 13 ◽  
pp. 117863021986992 ◽  
Author(s):  
Charlene C Nielsen ◽  
Carl G Amrhein ◽  
Prakesh S Shah ◽  
Khalid Aziz ◽  
Alvaro R Osornio-Vargas
Keyword(s):  
Land Use ◽  
Socioeconomic Status ◽  
Birth Weight ◽  
Low Birth Weight ◽  
Critically Ill ◽  
Gestational Age ◽  
Small For Gestational Age ◽  
Hot Spot ◽  
Hot Spot Analysis ◽  
Spot Analysis

In addition to small for gestational age (SGA) and low birth weight at term (LBWT), critically ill cases of SGA/LBWT are significant events from outcomes and economic perspectives that require further understanding of risk factors. We aimed to assess the spatiotemporal distribution of locations where there were consistently higher numbers of critically ill SGA/LBWT (hot spots) in comparison with all SGA/LBWT and all births. We focused on Edmonton (2008-2010) and Calgary (2006-2010), Alberta, and used a geographical information system to apply emerging hot spot analysis, as a new approach for understanding SGA, LBWT, and the critically ill counterparts (ciSGA or ciLBWT). We also compared the resulting aggregated categorical patterns with proportions of land use and socioeconomic status (SES) using Spearman correlation and logistic regression. There was an overall increasing trend in all space-time clusters. Whole period emerging hot spot patterns among births and SGA generally coincided, but SGA with ciSGA and LBWT with ciLBWT did not. Regression coefficients were highest for low SES with SGA and LBWT, but not with ciSGA and ciLBWT. Open areas and industrial land use were most associated with ciLBWT but not with ciSGA, SGA, or LBWT. Differences in the space-time hot spot patterns and the associations with ciSGA and ciLBWT indicate further need to research the interplay of maternal and environmental influences. We demonstrated the novel application of emerging hot spot analysis for small newborns and spatially related them to the surrounding environment.

Sign in / Sign up

Export Citation Format

Share Document