scholarly journals Criminal Justice Article Review: Aggravated Assault

2020 ◽  
Vol 9 (3) ◽  
pp. 164
Author(s):  
Yunyi Xiao
Keyword(s):  
Criminal Justice ◽  
Geographically Weighted Regression ◽  
Hot Spot ◽  
Weighted Regression ◽  
Hot Spot Analysis ◽  
Aggravated Assault ◽  
The Impact ◽  
Spot Analysis

The study uses geographically weighted regression (WGR) and the emerging hot spot analysis (EHSA) methodologies to examine the impact of immigrant and ethnic and racial concentration on patterns of aggravated assault and larceny in Miami.

Towards spatial machine learning to reveal hidden patterns and relationships in national and international geochemical databases

2020 ◽  
Author(s):  
Chaosheng Zhang
Keyword(s):  
Machine Learning ◽  
Geographically Weighted Regression ◽  
Hot Spot ◽  
Local Level ◽  
Environmental Geochemistry ◽  
Machine Learning Techniques ◽  
Weighted Regression ◽  
Hot Spot Analysis ◽  
Learning Techniques ◽  
Spot Analysis

<p>Environmental geochemistry is playing an increasingly important role in mineral exploration, environmental management, agricultural practices as well as links with health. With rapidly growing databases available at regional, national, and global scales, environmental geochemistry is facing the challenges in the “big data” era. One of the main challenges is to find out useful information hidden in a large volume of data, with the existence of spatial variation found at all the sizes of global, regional (in square kilometers), field (in square meters) and micro scales (in square centimeters). Meanwhile, the rapidly developing techniques in machine learning become useful tools for classification, identification of clusters/patterns, identification of relationships and prediction. This presentation demonstrates the potential uses of a few practical spatial machine learning techniques (spatial analyses) in environmental geochemistry: neighborhood statistics, hot spot analysis and geographically weighted regression.</p><p> </p><p>Neighborhood (local) statistics are calculated using data within a neighborhood such as a moving window. In this way, spatial variation at the local level can be quantified and more details are revealed. Hot spot analysis techniques are capable of revealing hidden spatial patterns. The techniques of hot spot analysis including local index of spatial association (LISA) and Getis Ord Gi* are investigated using examples of geochemical databases in Ireland, China, the UK and the USA. The geographically weighted regression (GWR) explores the relationships between geochemical parameters and their influencing factors at the local level, which is effective in identifying the complex spatially varying relationships. Machine learning techniques are expected to play more important roles in environmental geochemistry. Challenges for more effective “data analytics” are currently emerging in the era of “big data”.</p><p> </p>

scholarly journals Analysis of Built-Up Areas of Small Polish Cities with the Use of Deep Learning and Geographically Weighted Regression

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 223
Author(s):  
Maciej Adamiak ◽  
Iwona Jażdżewska ◽  
Marta Nalej
Keyword(s):  
Economic Development ◽  
Rural Areas ◽  
Geographically Weighted Regression ◽  
Hot Spot ◽  
Weighted Regression ◽  
Economic Factors ◽  
Small Cities ◽  
Large Cities ◽  
The Impact ◽  
Socio Economic Factors

Small cities are an important part of the settlement system, a link between rural areas and large cities. Although they perform important functions, research focuses on large cities and metropolises while marginalizing small cities, the study of which is of great importance to progress in social sciences, geography, and urban planning. The main goal of this paper was to verify the impact of selected socio-economic factors on the share of built-up areas in 665 small Polish cities in 2019. Data from the Database of Topographic Objects (BDOT), Sentinel-2 satellite imagery from 2015 and 2019, and Local Data Bank by Statistics Poland form 2019 were used in the research. A machine learning segmentation procedure was used to obtain the data on the occurrence of built-up areas. Hot Spot (Getis-Ord Gi*) analysis and geographically weighted regression (GWR) was applied to explain spatially varying impact of factors related to population, spatial and economic development, and living standards on the share of built-up areas in the area of small cities. Significant association was found between the population density and the share of built-up areas in the area of the cities studied. The influence of the other socio-economic factors examined, related to the spatial and economic development of the cities and the quality of life of the inhabitants, showed great regional variation. The results also indicated that the share of built-up areas in the area of the cities under study is a result of the conditions under which they were established and developed throughout their existence, and not only of the socio-economic factors affecting them at present.

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

Government failures and non-performing loans in European countries: a spatial approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ferdinando Ofria ◽  
Massimo Mucciardi
Keyword(s):  
Public Debt ◽  
Geographically Weighted Regression ◽  
European Countries ◽  
Weighted Regression ◽  
Second Step ◽  
Ols Regression ◽  
Content Type ◽  
The Impact ◽  
The Relationship ◽  
The Eu

PurposeThe purpose is to analyze the spatially varying impacts of corruption and public debt as % of GDP (proxies of government failures) on non-performing loans (NPLs) in European countries; comparing two periods: one prior to the crisis of 2007 and another one after that. The authors first modeled the NPLs with an ordinary lest square (OLS) regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the authors utilized the geographically weighted regression (GWR) to explore regional variations in the relationship between NPLs and the proxies of “Government failures”.Design/methodology/approachThe authors first modeled the NPL with an OLS regression and found clear evidence of spatial instability in the distribution of the residuals. As a second step, the author utilized the Geographically Weighted Regression (GWR) (Fotheringham et al., 2002) to explore regional variations in the relationship between NPLs and proxies of “Government failures” (corruption and public debt as % of GDP).FindingsThe results confirm that corruption and public debt as % of GDP, after the crisis of 2007, have affected significantly on NPLs of the EU countries and the following countries neighboring the EU: Switzerland, Iceland, Norway, Montenegro, and Turkey.Originality/valueIn a spatial prospective, unprecedented in the literature, this research focused on the impact of corruption and public debt as % of GDP on NPLs in European countries. The positive correlation, as expected, between public debt and NPLs highlights that fiscal problems in Eurozone countries have led to an important rise of problem loans. The impact of institutional corruption on NPLs reports that the higher the corruption, the higher is the level of NPLs.

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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