SPATIAL ECONOMETRIC ANALYSIS OF CRIME: EVIDENCE FROM NUTS 3 REGIONS OF V4 COUNTRIES
Gorde:
| Argitaratua izan da: | Economic and Social Development: Book of Proceedings (Oct 18/Oct 19, 2018), p. 194 |
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| Egile nagusia: | |
| Argitaratua: |
Varazdin Development and Entrepreneurship Agency (VADEA)
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full Text Full Text - PDF |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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MARC
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| 045 | 2 | |b d20181018 |b d20181019 | |
| 084 | |a 237530 |2 nlm | ||
| 100 | 1 | |a Chocholata, Michaela |u University of Economics in Bratislava, Slovakia michaela.chocholata@euba.sk | |
| 245 | 1 | |a SPATIAL ECONOMETRIC ANALYSIS OF CRIME: EVIDENCE FROM NUTS 3 REGIONS OF V4 COUNTRIES | |
| 260 | |b Varazdin Development and Entrepreneurship Agency (VADEA) |c Oct 18/Oct 19, 2018 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a This paper deals with the spatial analysis of crime in 2016 across the 113 NUTS 3 (Nomenclature of Units for Territorial Statistics) regions of the Czech Republic, Hungary, Poland and Slovakia (known as the V4, i.e. Visegrad 4, countries). The analysis is based on a total number of crimes per 1000 persons and includes conduction of an exploratory spatial data analysis (ESDA) and spatial econometric modelling. Both the box plot and box map of the distribution of the crime are presented. To investigate the spatial effects, the global Moran's I statistic together with the Moran scatterplot were employed. The presence of statistically significant positive spatial autocorrelation (based on queen case contiguity weight matrix) was confirmed. The local indicators of spatial association (LISA) were used to identify the local clusters. Since the statistically significant high-high clusters were confirmed for the 7 Hungarian regions, the low-low clusters occur across the 18 regions of the Czech Republic, Poland and Slovakia. The spatial outliers are represented by 2 Polish regions. Finally, the spatial econometric models were employed to assess the impact of location as well as of some economic and demographic indicators (GDP per capita, rate of employed persons and population density) on crimes in concrete region. | |
| 610 | 4 | |a Eurostat | |
| 651 | 4 | |a Czech Republic | |
| 651 | 4 | |a Chicago Illinois | |
| 651 | 4 | |a Poland | |
| 651 | 4 | |a Slovakia | |
| 651 | 4 | |a United States--US | |
| 653 | |a Research | ||
| 653 | |a Outliers (statistics) | ||
| 653 | |a Software | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Crime | ||
| 653 | |a Criminal statistics | ||
| 653 | |a Population density | ||
| 653 | |a Demographics | ||
| 653 | |a Nuts | ||
| 653 | |a Indicators | ||
| 653 | |a Data analysis | ||
| 653 | |a Clusters | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Conduction | ||
| 653 | |a Criminology | ||
| 653 | |a Data processing | ||
| 653 | |a Geography | ||
| 653 | |a Nomenclature | ||
| 653 | |a Spatial data | ||
| 653 | |a Impact analysis | ||
| 653 | |a Sociology | ||
| 653 | |a Weight | ||
| 653 | |a Spatial distribution | ||
| 653 | |a Economic models | ||
| 653 | |a Per capita | ||
| 653 | |a Gross Domestic Product--GDP | ||
| 653 | |a Econometrics | ||
| 653 | |a Lagrange multiplier | ||
| 653 | |a Maximum likelihood method | ||
| 653 | |a Statistical significance | ||
| 653 | |a Economic | ||
| 773 | 0 | |t Economic and Social Development: Book of Proceedings |g (Oct 18/Oct 19, 2018), p. 194 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/2139003485/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/2139003485/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/2139003485/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |