scholarly journals A spatial Analysis of Crime and Neighborhood Characteristics in Detroit Census Block Groups

2021 ◽  
Vol 4 ◽  
pp. 1-8
Author(s):  
Esther Akoto Amoako
Keyword(s):  
Violent Crime ◽  
Social Disorganization ◽  
Ordinary Least Squares ◽  
Social Disorganization Theory ◽  
Spatial Error ◽  
Classical Statistics ◽  
Log Likelihood ◽  
Theory Of Crime ◽  
Spatial Autoregressive ◽  
The City

Abstract. Crime has an inherent geographical quality and when a crime occurs, it happens within a particular space making spatiality essential component in crime studies. To prevent and respond to crimes, it is first essential to identify the factors that trigger crimes and then design policy and strategy based on each factor. This project investigates the spatial dimension of violent crime rates in the city of Detroit for 2019. Crime data were obtained from the City of Detroit Data Portal and demographic data relating to social disorganization theory were obtained from the Census Bureau. In the presence of spatial spill over and spatial dependence, the assumptions of classical statistics are violated, and Ordinary Least Squares estimations are inefficient in explaining spatial dimensions of crime. This paper uses explanatory variables relating to the social disorganization theory of crime and spatial autoregressive models to determine the predictors of violent crime in the City for the period. Using GeoDa 1.18 and ArcGIS Desktop 10.7.1 software package, Spatial Lag Models (SLM) and Spatial Error Models were carried out to determine which model has high performance in identifying predictors of violent crime. SLM outperformed SEM in terms of efficiency with (AIC:5268.52; Breusch-Pagan test: 9.8402; R2: 16% & Log Likelihood: −2627.26) > SEM (AIC: 5275.24; Breusch-Pagan test: 9.7601; R2: 15% & Log Likelihood: −2630.6194). Strong support is found for the spatial disorganization theory of crime. High percent ethnic heterogeneity (% black) and high college graduates are the strongest predictors of violent crime in the study area.

scholarly journals The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach

2019 ◽  
Vol 8 (1) ◽  
pp. 51 ◽  
Author(s):  
Lu Wang ◽  
Gabby Lee ◽  
Ian Williams
Keyword(s):  
Violent Crime ◽  
Property Crime ◽  
Criminal Activity ◽  
Ordinary Least Squares ◽  
Crime Rates ◽  
Individual Characteristics ◽  
Suitable Model ◽  
Similar Distribution ◽  
Wide Range ◽  
The City

Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services.

Applying a Home-Based Approach to the Understanding Distribution of Economic Impacts of Traffic Crashes

2020 ◽  
Vol 2674 (12) ◽  
pp. 360-371
Author(s):  
Amin Mohamadi Hezaveh ◽  
Christopher R. Cherry
Keyword(s):  
Travel Behavior ◽  
Positive Association ◽  
Economic Cost ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Significant Positive Association ◽  
Traffic Crashes ◽  
Spatial Error ◽  
Home Address ◽  
Spatial Autoregressive

The current practice of road safety attributes traffic crash costs to the location of traffic crashes. Therefore it is challenging to estimate the economic cost of traffic crashes and individuals who are more prone to the burden of traffic crashes. To address this limitation, this study used the home address of individuals who were involved in traffic crashes in the Knoxville Regional Travel Model (KRTM) region between 2015 and 2016. After geocoding the home addresses, 110,312 individuals were assigned to the Traffic Analysis Zone (TAZ) corresponding to their home address and the economic cost of traffic crashes per capita (ECCPC) was calculated for each TAZ. The average ECCPC in the study area was $1,250. The KRTM output was used for extracting travel behavior data elements for modeling ECCPC at the zonal level. This study also established an index to measure average zonal activity in the transportation system for each TAZ. Analysis indicates that the burden of traffic crashes was more tangible in the TAZs with lower-income households and higher average zonal activities. To account for spatial autocorrelation, a Spatial Autoregressive model (SAR) and a spatial error model (SEM) were used. The SAR model was more suitable compared with SEM and ordinary least squares regression. Findings indicate that average zonal activity and traffic exposure have a significant positive association with ECCPC. The ECCPC could be used as an index for allocating proper countermeasures and interventions to groups and areas where the burden of traffic crashes is more tangible.

Testing Theories of Social Disorganization in Nigeria

2018 ◽  
Author(s):  
Faisal Umar ◽  
Shane D. Johnson ◽  
James A. Cheshire
Keyword(s):  
Social Disorganization ◽  
Final Section ◽  
Empirical Test ◽  
Survey Methods ◽  
Social Disorganization Theory ◽  
Urban District ◽  
Units Of Analysis ◽  
The Social ◽  
Using Data ◽  
The City

This chapter focuses on the social disorganization approach to understanding variations in area-level rates of crime. It first provides context through a brief description of the study area, Badarawa-Malali, an urban district in the city of Kaduna, Nigeria (Section 17.2). Section 17.3 provides a review of the different components of social disorganization theory, the mechanisms through which they are believed to operate, how they have been estimated in previous studies, and whether they are meaningful in the context of Nigeria. Section 17.4 describes the data and survey methods employed, while Section 17.5 discusses the geographical units of analysis used in this present study. Section 17.6 presents an empirical test of social disorganization theory using data for Nigeria. The final section discusses the findings and their implications for criminological understanding.

Spatial autoregressive analysis of nationwide street network patterns with global open data

2021 ◽  
pp. 239980832098784
Author(s):  
Qi Zhou ◽  
Hao Lin ◽  
Junya Bao
Keyword(s):  
Open Data ◽  
Ordinary Least Squares ◽  
Street Network ◽  
Spatial Error ◽  
Spatial Lag ◽  
Spatial Lag Model ◽  
Physical Form ◽  
Lag Model ◽  
Spatial Autoregressive ◽  
Network Patterns

The study of street network patterns is beneficial in understanding the layout or physical form of a city. Many studies have analyzed street network patterns, but the similarity and/or difference of street network patterns across a country or region are rarely quantitatively understood. To fill this gap, this research proposes a quantitative analysis of street network patterns nationwide. Specifically, the street network patterns across a country or region were first mapped, and then the relationship between such patterns and various landscape factors (calculated based on global open data) was quantitatively investigated by employing three regression models (ordinary least squares, spatial lag model, and spatial error model). Not only the whole region of China but also its subregions were used as study areas, which involved a total of 362 prefecture-level cities and 2081 built-up areas for analysis. Results showed that (1) similar street network patterns are spatially aggregated; (2) a number of factors, including both land-cover and terrain factors, are found to be significantly correlated with street network patterns; and (3) the spatial lag model is preferred in most of the application scenarios. Not only the analytical method and data can be applied to other countries and regions but also these findings are useful for understanding street network patterns and their associated urban forms in a country or region.

scholarly journals Gambling Outlets Location in Urban Areas: A Case Study of Madrid

2021 ◽  
Author(s):  
Levi Pérez ◽  
Ana Rodríguez ◽  
Andrey Shmarev
Keyword(s):  
Urban Areas ◽  
Local Governments ◽  
Spatial Econometric ◽  
Spatial Error ◽  
Spatial Dependencies ◽  
Key Factor ◽  
Sar Model ◽  
Spatial Autoregressive ◽  
The City

AbstractCities are certainly a key factor in the location of gambling facilities. This paper aims to map the location of gambling outlets in urban areas and to examine potential links between neighborhoods socioeconomic and demographic characteristics and gambling supply, taking into account spatial dependencies of neighboring areas. This correlation is of interest because neighborhood characteristics may attract sellers, and because the presence of gambling sellers may cause changes in neighborhood demographics. Using detailed official data from the city of Madrid for the year 2017, three spatial econometric approaches are considered: spatial autoregressive (SAR) model, spatial error model (SEM) and spatial lag of X (explicative variables) model (SLX). Empirical analysis finds a strong correlation between neighborhoods characteristics and co-location of gambling outlets, highlighting a specific geographic patterning of distribution within more disadvantaged urban areas. This may have interesting implications for gambling stakeholders and for local governments when it comes to the introduction and/or increase of gambling availability.

scholarly journals Hate Crimes and Minority-Owned Businesses

2019 ◽  
Vol 46 (1) ◽  
pp. 3-21
Author(s):  
Karl R. Geisler ◽  
Carl E. Enomoto ◽  
Theophilus Djaba
Keyword(s):  
Spatial Dependence ◽  
Negative Impact ◽  
Ordinary Least Squares ◽  
Hate Crimes ◽  
Spatial Error ◽  
Spatial Estimation ◽  
Sar Model ◽  
Spatial Autoregressive ◽  
Least Squares Estimates ◽  
Error Method

This article estimates effects of hate crimes on the number of Black-owned firms, Hispanic-owned firms, Asian-owned firms, Asian Indian–owned firms, and White-owned firms. Using county data for Kentucky, the number of all minority-owned firms was found to decrease in counties with more hate crimes. No effect on White-owned firms was found due to hate crimes. The finding that the number of minority-owned businesses declines in response to the number of hate crimes was robust to spatial estimation techniques. Spatial error method (SEM) and spatial autoregressive (SAR) model estimates found impacts similar to the ordinary least squares estimates even where spatial dependence was present. The negative impact hate crimes are found to have on the number of minority-owned businesses implies that policies to fight hate crimes should lead to increased economic development.

scholarly journals Non-Timber Forest Products and the Cosmetic Industry: An Econometric Assessment of Contributions to Income in the Brazilian Amazon

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 588
Author(s):  
Aghane Antunes ◽  
Cynthia S. Simmons ◽  
Joao Paulo Veiga
Keyword(s):  
Smallholder Farmers ◽  
Forest Products ◽  
Brazilian Amazon ◽  
Community Partnerships ◽  
Ordinary Least Squares ◽  
Spatial Error ◽  
Household Level ◽  
Cosmetic Industry ◽  
Non Timber Forest Products ◽  
Spatial Autoregressive

This study explores Non-Timber Forest Products (NTFPs) production and company–community partnerships with the multinational cosmetic industry. The objectives are to critically assess: (1) how income generated from market-oriented NTFPs extraction impacts small farmers’ livelihoods; and (2) whether membership in cooperatives linked to such partnerships is a factor in improved livelihood. Household-level data from 282 surveys conducted in remote communities in four municipalities in the Northeast region of the State of Pará provide empirical insight into NTFPs extraction and processing activities by smallholder farmers in the Brazilian Amazon. We employ a spatial econometric approach to assess if engagement in NTFPs extraction and membership in cooperatives result in statistically significant increases in the overall household income. A series of spatial regression models are used, including Ordinary Least Squares (OLS), Spatial Autoregressive Regression (SAR), Spatial Error Model (SEM), Spatial Durbin Model (SDM) and their corresponding alternative Bayesian models. Our study finds that NTFP extraction and membership in cooperatives tied to company–community partnerships are statistically significant and result in increases in total income at the household level. Findings also show that distance to transportation modes and markets are statistically significant with more distant households earning greater income. This finding presents challenges for the long-term sustainability of green alternatives to development that rely on remote, inaccessible environments for the commodities of interest. This is especially pronounced given the commitment of the Amazonian Nations, and the massive national and international investments, in the Initiative for the Integration of Regional Infrastructure in South America (IIRSA), which has as its goal the creation of a multimodal transportation hub to integrate the continent with global markets and make accessible far reaches of the Amazon.

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