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>

scholarly journals Assessing Changes in Clusters of Wildlife Road Mortalities after the Construction Wildlife Mitigation Structures

2021 ◽  
Author(s):  
Thomas Yamashita ◽  
Trinity Livinigston ◽  
Kevin Ryer ◽  
John Young ◽  
Richard Kline
Keyword(s):  
Spatial Distribution ◽  
Hot Spots ◽  
Hot Spot ◽  
Mortality Data ◽  
Road Mortality ◽  
Hot Spot Analysis ◽  
Generalized Linear Modelling ◽  
Before And After ◽  
Road Intersections ◽  
Spot Analysis

Collisions with vehicles are a major threat to wildlife populations and often occur in identifiable patterns. To reduce wildlife road mortalities, mitigation structures including exclusionary fencing and wildlife crossings are constructed. Openings in fencing at road intersections may lead to concentration of road mortality hot spots at openings leading to a belief that these gaps concentrate road mortalities. However, it is also possible that hot spots existed at these locations before construction indicating that road mortality patterns have not changed with mitigation structure construction. Therefore, to assess mitigation structure effectiveness, it is important to examine both road mortality numbers and road mortality spatial distribution. Wildlife road mortality data was collected on a 15-km section of rural highway in Texas, USA before, during, and after the construction of wildlife mitigation structures. We expected that the number of road mortalities would decrease after construction compared to before construction and that road mortalities would become more concentrated around openings in the fence. We used ANOVA to compare numbers of road mortalities and emerging hot spot analysis and generalized linear modelling to assess changes in road mortality spatial distribution. Road mortalities were not significantly different in the before and after construction periods (p = 0.092). While there were no significant changes in road mortality patterns with construction, cluster intensity was greater when nearer to fence openings in all three time periods. Emerging hot spot analysis provides an effective and easy way to visualize road mortality patterns through time, however, due to low numbers of mortalities in many road mortality studies, including this one, the power of this analysis to detect significant changes in road mortality may be limited. This technique can provide both ecologists and transportation planners an effective tool for identifying patterns that may warrant further investigation using traditional statistical techniques.

scholarly journals Spatial-Temporal Analysis of Point Distribution Pattern of Schools Using Spatial Autocorrelation Indices in Bojnourd City

2020 ◽  
Vol 12 (18) ◽  
pp. 7755
Author(s):  
Mostafa Ghodousi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Farzaneh Rabiee ◽  
Soo-Mi Choi
Keyword(s):  
Spatial Distribution ◽  
High Schools ◽  
Primary Schools ◽  
Nearest Neighbor ◽  
Hot Spot ◽  
School Building ◽  
Hot Spot Analysis ◽  
Building Area ◽  
Per Capita ◽  
Spot Analysis

In recent years, attention has been given to the construction and development of new educational centers, but their spatial distribution across the cities has received less attention. In this study, the Average Nearest Neighbor (ANN) and the optimized hot spot analysis methods have been used to determine the general spatial distribution of the schools. Also, in order to investigate the spatial distribution of the schools based on the substructure variables, which include the school building area, the results of the general and local Moran and Getis Ord analyses have been investigated. A differential Moran index was also used to study the spatial-temporal variations of the schools’ distribution patterns based on the net per capita variable, which is the amount of school building area per student. The results of the Average Nearest Neighbor (ANN) analysis indicated that the general spatial patterns of the primary schools, the first high schools, and the secondary high schools in the years 2011, 2016, 2018, and 2021 are clustered. Applying the optimized hot spot analysis method also identified the southern areas and the suburbs as cold polygons with less-density. Also, the results of the differential Moran analysis showed the positive trend of the net per capita changes for the primary schools and first high schools. However, the result is different for the secondary high schools.

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

Early stage hot spot analysis through standard cell base random pattern generation

2017 ◽  
Author(s):  
Joong-Won Jeon ◽  
Jaewan Song ◽  
Jeong-Lim Kim ◽  
Seongyul Park ◽  
Seung-Hune Yang ◽  
...  
Keyword(s):  
Early Stage ◽  
Hot Spot ◽  
Pattern Generation ◽  
Random Pattern ◽  
Standard Cell ◽  
Hot Spot Analysis ◽  
Cell Base ◽  
Spot Analysis

scholarly journals Geographic variation in preventable hospitalisations across Canada: a cross-sectional study

BMJ Open ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. e037195
Author(s):  
Piotr Wilk ◽  
Shehzad Ali ◽  
Kelly K Anderson ◽  
Andrew F Clark ◽  
Martin Cooke ◽  
...  
Keyword(s):  
Geographic Variation ◽  
Small Area ◽  
Service Providers ◽  
Hot Spot ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Discharge Abstract Database ◽  
Cross Sectional ◽  
Hot Spot Analysis ◽  
Spot Analysis

ObjectiveThe objective of this study is to examine the magnitude and pattern of small-area geographic variation in rates of preventable hospitalisations for ambulatory care-sensitive conditions (ACSC) across Canada (excluding Québec).Design and settingA cross-sectional study conducted in Canada (excluding Québec) using data from the 2006 Canadian Census Health and Environment Cohort (CanCHEC) linked prospectively to hospitalisation records from the Discharge Abstract Database (DAD) for the three fiscal years: 2006–2007, 2007–2008 and 2008–2009.Primary outcome measurePreventable hospitalisations (ACSC).ParticipantsThe 2006 CanCHEC represents a population of 22 562 120 individuals in Canada (excluding Québec). Of this number, 2 940 150 (13.03%) individuals were estimated to be hospitalised at least once during the 2006–2009 fiscal years.MethodsAge-standardised annualised ACSC hospitalisation rates per 100 000 population were computed for each of the 190 Census Divisions. To assess the magnitude of Census Division-level geographic variation in rates of preventable hospitalisations, the global Moran’s I statistic was computed. ‘Hot spot’ analysis was used to identify the pattern of geographic variation.ResultsOf all the hospitalisation events reported in Canada during the 2006–2009 fiscal years, 337 995 (7.10%) events were ACSC-related hospitalisations. The Moran’s I statistic (Moran’s I=0.355) suggests non-randomness in the spatial distribution of preventable hospitalisations. The findings from the ‘hot spot’ analysis indicate a cluster of Census Divisions located in predominantly rural and remote parts of Ontario, Manitoba and Saskatchewan and in eastern and northern parts of Nunavut with significantly higher than average rates of preventable hospitalisation.ConclusionThe knowledge generated on the small-area geographic variation in preventable hospitalisations can inform regional, provincial and national decision makers on planning, allocation of resources and monitoring performance of health service providers.

scholarly journals What are the contextual risk factors for low colorectal cancer screening uptake in El Paso County, Texas? Spatial cross-sectional analysis

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e038342
Author(s):  
Jennifer Salinas ◽  
Jacquelyn Brito ◽  
Cheyenne Rincones ◽  
Navkiran K Shokar
Keyword(s):  
Colorectal Cancer ◽  
Census Tract ◽  
Hot Spot ◽  
El Paso ◽  
P Value ◽  
Hot Spot Analysis ◽  
Crc Screening ◽  
Screening Uptake ◽  
Fit Testing ◽  
Spot Analysis

ObjectiveThis study examines the geographical and socioeconomic factors associated with uptake of colorectal cancer (CRC) screening (colonoscopies or faecal immunochemical test (FIT) testing).DesignSecondary data analysis.SettingThe Against Colorectal Cancer in our Community (ACCION) programme was implemented in El Paso County, Texas, to increase screening rates among the uninsured and underinsured.ParticipantsWe successfully geocoded 5777 who were offered a free colonoscopy or FIT testing kit.Primary outcome measureCensus-tract CRC screening uptake average.ResultsMedicare recipient mortality (β=0.409, p-value=0.049) and % 65 years or older (β=−0.577, p value=0.000) were significant census tract contextual factors that were associated with the prevalence of CRC screening uptake in the geographically weighted Poisson regression. Neither Latino ethnicity nor immigrant concentration were significant predictors of CRC screening uptake in the ACCION programme. Hot spot analysis demonstrated that there was a significant low-value cluster in South Central El Paso. There was a similar hot spot for % 65 years or older in this same area, suggesting that uptake was lowest in an area that had the highest concentration of older adults.ConclusionThe results from this study revealed not only feasibility of hot spot analysis but also its utility in geographically tracking successful CRC screening uptake in cancer prevention and intervention programmes.

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