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 Volunteered Geographical Information and Digital Social Networks applied to Tourism Dynamics in the “Alta and University of Coimbra” (UNESCO world Heritage Site)

2019 ◽  
Author(s):  
José Santos ◽  
Liliana Azevedo ◽  
Joaquim Patriarca ◽  
Luis Leitão
Keyword(s):  
Social Networks ◽  
Spatial Analysis ◽  
Hot Spot ◽  
Geographic Information ◽  
World Heritage Site ◽  
Geographical Information ◽  
Regionalized Variables ◽  
Heritage Site ◽  
Unesco World Heritage Site ◽  
Distance Weighting

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this 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 sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables 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) & Inverse Distance Weighting (IDW) Interpolation.

scholarly journals Geostatistics and Digital Social Networks—A Study of Tourism Dynamics in “Alta and University of Coimbra” (UNESCO world Heritage Site)

2019 ◽  
Author(s):  
José Gomes dos Santos ◽  
Liliana Raquel Simões de Azevedo ◽  
Joaquim António Saraiva Patriarca ◽  
Luis Carlos Roseiro Leitão
Keyword(s):  
Social Networks ◽  
Spatial Analysis ◽  
Hot Spot ◽  
Geographic Information ◽  
World Heritage Site ◽  
Regionalized Variables ◽  
Heritage Site ◽  
Unesco World Heritage Site ◽  
Distance Weighting ◽  
Voluntary Geographic Information

Spatial modeling in Geographic Information Systems (GIS) always involves choices. The existence of constraints, either of a financial nature or related to the specifics of the software itself, to the algorithms, the uncertainty and even the reliability of the data, the purposes and the applications of the studies, make this 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 voluntary geographic information generated by tourists sharing their topophilic ties through DSN, the present analysis aims to evaluate the contribution of modern techniques of spatial analysis applied to tourism in the “Alta and University of Coimbra” area. Concepts and procedural tasks related to density determination, cluster analysis and identification of patterns associated with regionalized variables 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) & Inverse Distance Weighting (IDW) Interpolation.

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 SPATIAL ANALYSIS OF CRIME HOT-SPOT IN THE NORTHEAST PENANG ISLAND DISTRICT AND KUCHING DISTRICT, MALAYSIA

2021 ◽  
Vol 19 ◽  
Author(s):  
Tarmiji Masron ◽  
Mohd Norashad Nordin ◽  
Nur Faziera Yaakub ◽  
Norita Jubit
Keyword(s):  
Spatial Analysis ◽  
Drug Abuse ◽  
Hot Spots ◽  
Hot Spot ◽  
Hot Spot Analysis ◽  
Police Station ◽  
Points Of View ◽  
Spatial Analysis Of Crime ◽  
Over Time ◽  
Spot Analysis

Over time, the relation between criminal acts with drug abuse cases has been discussed pedantically. From social and spatial points of view, this paper aims to determine the hot spot areas of burglary cases in the Northeast Penang Island District and Kuching District. The gained results of burglary cases are then being correlated with the presence of drug abuse cases. Both study areas came with location coordinates of the incident based on police stations boundaries and police station sector boundaries from the year 2015. The type of analysis used for this research is Optimized Hot Spot Analysis. Results for burglary cases of both areas are divided into two (2) which are daytime and nighttime. The spatial analysis revealed that there are five (5) sectors identified as hot spots for the Northeast Penang Island District which involve Jelutong Police Station boundary and Ayer Itam Police Station boundary, while none of the areas identified as hot spot areas in Kuching District.

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.

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.

scholarly journals Spatial Analysis of Accidents at the Suburban Intersections Using Kernel Density Estimation and Spatial Autocorrelation Methods

2015 ◽  
Vol 3 (2) ◽  
pp. 21-42
Author(s):  
Saye Zeynali ◽  
Farhad Hosseinali ◽  
Abolghasem Sadeghi Niaraki ◽  
Mohammad Kazemi Beydokhti ◽  
Meysam Effati ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Spatial Autocorrelation ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density
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