Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)

Allen, Michael J.; Allen, Thomas R.; Davis, Christopher A.; McLeod, George

doi:10.3390/ijgi10050310

Cited by 18 publications

(4 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the analysis of the disaster, Baidu Maps was used to obtain the coordinates of the location where the waterlogging occurred in Weibo texts. The coordinates of these waterlogging points were represented in ArcGIS, and then the spatial distribution characteristics of waterlogging were explored by the Kernel Density Analysis (Allen et al 2021). Kernel Density Analysis is a method used to calculate the unit density of the measured values of point and line elements within a speci ed neighborhood.…”

Section: Analysis Of Disaster Situationmentioning

confidence: 99%

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Gao

Xiao

et al. 2023

Nat Hazards

View full text Add to dashboard Cite

The exploding popularity of social networks, provides a new opportunity to study disasters and public emotion. Among the social networks, Weibo is one of the largest microblogging services in China. Taking Guangdong and Guangxi in the south of China as a case, Web Scraper was used to obtain Weibo texts related to oods in 2020. The spatial distribution of oods was analyzed using Kernel Density Estimation.Public emotion was analyzed using Natural Language Processing (NLP) tools. The association between oods and public emotion was explored through correlation analysis methods. The results indicated that:(1) Weibo texts could be utilized as an effective data to identify urban waterlogging risk in Guangdong and Guangxi.(2) More oods occurred in the southeast than in the central and northwest, and more in the south than in the north in Guangdong and Guangxi. The coastal cities and provincial capitals were severely affected.(3) The public emotion was mainly negative and varied signi cantly over time, generally showing stronger negative emotion during periods of heavy precipitation. ( 4) There was a strong correlation between public emotion and oods in spatial-temporal variation. The degree of negative public emotion was signi cantly in uenced by the number of waterlogging points. The presented results serve as the pre-liminary data for future planning and designing of emergency management. HighlightsWeibo texts can be used to study the ooding situation and public emotion.The distribution of waterlogging points was spatially heterogeneous.The type of public response was dominated by the description of the ood.The overall public emotion during the ood was negative.There was a strong correlation between public emotion and ooding.

show abstract

Section: Analysis Of Disaster Situationmentioning

confidence: 99%

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Gao

Xiao

et al. 2023

Nat Hazards

View full text Add to dashboard Cite

show abstract

“…A time-step-dependent hexagonal grid is used to generate spatialtemporal cubes. Each cube cell has a fixed position, and the sentiment value of each cube is calculated by averaging the sentiment values of all tourists within its spatio-temporal range [52]. Some grids may have a count of zero data points across all time steps, and the spatio-temporal bars of these grids will not be included in the spatio-temporal pattern analysis.…”

Section: Cold Hot Pattern Miningmentioning

confidence: 99%

Sensing Tourist Distributions and Their Sentiment Variations Using Social Media: Evidence from 5A Scenic Areas in China

Wang

Lei

2022

IJGI

View full text Add to dashboard Cite

The distribution and sentiment characteristics of tourists directly reflect the state of tourism development, and are an important reference for tourists to choose scenic areas. Sensing the tourist distributions and their sentiment variations can provide decision support for the development planning of scenic areas. In this study, we crawled tourist social media data to explore tourist distribution characteristics and the patterns of tourist sentiment variations. First, we used web crawlers to obtain social media data (tourist comment data) and the location data of China’s 5A scenic areas from the Ctrip tourism platform. Second, SnowNLP (Simplified Chinese Text Processing) was optimized and used to classify the sentiment of tourists’ comments and calculate the sentiment value. Finally, we mined the distribution characteristics of tourists in 5A scenic areas and the spatio-temporal variations in tourists’ sentiments. The results show that: (1) There is a negative correlation between the number of tourists to China’s 5A scenic areas and tourist sentiment: the number of tourists is highest in October and lowest in March, while tourist sentiment is highest in March and lowest in October. (2) The spatio-temporal distribution of tourists has obvious aggregation: temporally mainly in July, August and October, spatially mainly in the Yangtze River Delta city cluster, Beijing-Tianjin-Hebei city cluster, and Guanzhong Plain city cluster. (3) Tourist sentiment cold/hot spots vary significantly by city clusters: the Yangtze River Delta city cluster is always a sentiment hot spot; the northern city cluster has more sentiment cold spots; the central city cluster varies significantly during the year; the southwestern city cluster has more sentiment hot spots.

show abstract

“…This formula has been tested many times, and the data selection search bandwidth is 333.6 km to more intuitively reflect the spatial distribution of sports tourism resources. Yoo et al [14] and Allen et al [15] made use of kernel density in order to determine the center position of a specific element. The density is the highest at the center position, and it decays with distance.…”

Section: Kernel Densitymentioning

confidence: 99%

Spatial Distribution Pattern and Influencing Factors of Sports Tourism Resources in China

Zuo

Chen

Pan

et al. 2021

IJGI

View full text Add to dashboard Cite

Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China’s sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of “depending on the city, near the scenery” and “large concentration, small dispersion”. (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people’s living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor.

show abstract

Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960–2019)

Cited by 18 publications

References 49 publications

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Analysis of public emotion on flood disasters in southern China in 2020 based on social media data

Sensing Tourist Distributions and Their Sentiment Variations Using Social Media: Evidence from 5A Scenic Areas in China

Spatial Distribution Pattern and Influencing Factors of Sports Tourism Resources in China

Contact Info

Product

Resources

About