Spatial Data Analysis Techniques Research Articles

Structural Covariates of Violent Crime Rates in Germany: Exploratory Spatial Analyses of Kreise

This research applies the techniques of exploratory spatial data analysis (ESDA) and spatial regression modeling to explain variation in robbery and assault rates across 413 districts or “Kreise” in Germany. The findings from ESDA reveal a distinct clustering of low rates in southern Germany, especially for robbery. The results of the spatial regression analyses indicate that for the nation at large, Kreise with high robbery and assault rates tend to be those with comparatively high levels of socioeconomic deprivation and a more urbanized environment, findings consistent with those commonly reported with data for areal units in the USA. We also observe net regional effects for the south in the regression models that pose puzzles for further inquiry into the German case, and “null effects” of the eastern region that have implications for more general debates of the potentially criminogenic consequences of the transition to market economies.

The spatio-temporal pattern of housing redevelopment in suburban Chicago, 2000–2010

This study examines the process of housing redevelopment, using data of single-family residential redevelopment that occurred in all 128 inner-ring suburbs of Chicago, USA, located in Cook County, between 2000 and 2010. Using exploratory spatial data analysis techniques, I identify the magnitude and the spatial locations of redevelopment, revealing the different types of suburban neighbourhoods in which redevelopment occurs. I then examine how the location and extent of redevelopment changed between 2000 and 2010 and how the physical manifestation of redevelopment varied across different types of suburban neighbourhoods. Findings reveal that redevelopment is spatially clustered, occurring in a variety of places ranging from modest middle-income neighbourhoods to very highly affluent neighbourhoods. Redevelopment often began in areas with high property values, and as house prices rose rapidly through the first half of the decade, it expanded into adjacent, less affluent neighbourhoods, retracting again at the end of the decade.

Research on Geographic Information System of Natural Zoning for Highway

The natural zoning for highway cannot accurately reflect the relationship between the natural geographical factors and the highway engineering. The main reasons: study lacked basic data; spatial data analysis technique fell behind; index analysis mainly relied on the subjective experience; it was difficult to fully promote the application of the zoning results. The establishment of Geographic Information System of Natural Zoning for Highway (HZ-GIS) provides the powerful tools for the research work of highway zoning: realizes the seamless integration of GIS function platform and spatial database through spatial data engine, improves the storage and utilization efficiency of spatial data; carries on the quantitative analysis of zoning indices with the intelligent model running pool; establishes the zoning results release system based on Web Service, and realizes the network distribution of zoning results.

Spatial-Temporal Analysis of the Economic and Environmental Coordination Development Degree in Liaoning Province

This study selects 20 indices of economic and environmental conditions over 15 years (1996–2010) for 14 cities in Liaoning province, China. We calculate the economic score and environmental score of each city by processing 4200 data points through SPSS 16.0 and establish synthesis functions between the economy and the environment. For the time dimension, we study the temporal evolution of the economic and environmental coordination development degree . Based on Exploratory Spatial Data Analysis (ESDA) techniques and using GeoDa, we calculate Moran's index of local spatial autocorrelation and explore the spatial distribution character of in Liaoning province through a LISA cluster map. As we found in the temporal dimension, the results show that of the 14 cities has been rising for 15 years and that increases year by year, which indicates that the economic and environmental coordination development condition has been improving from disorder to highly coordinated. A smaller gap between economic strength and environmental carrying capacity in Liaoning province exists, which means that economic development and environmental protection remain synchronized. In the spatial dimension, the highly coordinated cities have changed from a scattering to a concentration in the middle-south region of Liaoning province. Poorly coordinated cities are scattered in the northwestern region of Liaoning province.

Using ESDA with social weights to analyze spatial and social patterns of preschool children's behavior

To study the development of spatial and social behavior of preschool children, micro-level spatiotemporal data were collected for the first time in both spatial and social context using a novel behavioral coding system. These unique behavioral data enable us to explore the group-level, dynamic, spatial, and social patterns of preschool children's playing behavior from a hybrid geographic and social perspective. In this research, GIS and exploratory spatial data analysis (ESDA) techniques are employed together to study group-level spatial and social behavior emerging from children's everyday activities and interactions. ESDA with social weights is proposed to explore spatial and social patterns of preschool children's behavior at the same time. The results highlight the utility of this approach for studying the relationships between preschool children's playing behavior and preschool's environmental settings and the relationships between preschool children's personal activities and the formation of their social network space.

Exploring Hotspots of Drug Offences in Toronto: A Comparison of Four Local Spatial Cluster Detection Methods

Spatial cluster detection is an exploratory spatial data analysis technique that identifies areas or groups of areas with disproportionately high risk. Several local cluster detection methods have been developed; yet no research has critiqued these methods as they contribute to spatial studies of crime. This study aims to identify the locations of drug offence hotspots in Toronto and compare the clusters detected through four methods: (1) spatial scan statistic – Euclidean distance, (2) spatial scan statistic – non-Euclidean contiguity, (3) flexibly shaped scan statistic, and (4) local Moran's I. It was found that all methods detected clusters in the downtown, with fewer methods detecting clusters in the west and east of Toronto. It was observed that the spatial scan statistic detected the largest and most circular clusters, making it a suitable tool to inform general policing initiatives and highlight possible variables to be included in confirmatory research. The local Moran's I method, in contrast, found the smallest and most compact clusters, indicating that it is an appropriate test for identifying areas where resource intensive crime prevention and policing efforts should be targeted.

Population Distribution and Influencing Factors Based on ESDA

Abstract Based on the exploratory spatial data analysis (ESDA) technique and geographic information system (GIS) platform, with statistic data of counties in 2005, this paper confirms that there is a large population density gap between counties in 2005 because the Gini coefficient is 0.55. Population distribution does not change a lot during the past decades, and the southeast China is still much more densely populated than the northwest China. The global spatial autocorrelation of population distribution is obvious because Moran’s I scores 0.42 and local spatial autocorrelation is partly significant. Climate and elevation are still the main natural influencing factors. Meanwhile industrial structure and transportation significantly influence population distribution. Different combinations of natural factors have different effects on population distribution. For a long term, climate and terrain factor stability affect population distribution. But its influence will be weakened by progress of technology. ...

Spatial Patterns of Marriage Migration in Turkey

Even though recent societal phenomena such as a heightened sense of individualism, economic well-being, and institutionalization have caused a shift in people's life course and made their lives less standard in developed societies, marriage still remains an important life course event in Turkey. Between the years 1995 and 2000 marriage migration comprised 7.4% of the interprovincial migration in Turkey, and of these marriage migrants 94% were woman. Young Turkish women tend toward marriage migration to escape the patriarchal family structure and gain more autonomy, economic security, and well-being. Focusing on the spatial patterns of marriage migration relationships in Turkey, this study seeks to reveal the economic and sociocultural background of male and female marriage migration and to visualize, explore, and model spatial data by using spatial data analysis (SDA) techniques. The results showed that marriage migration in different regions of Turkey varied by gender. Even though SDA techniques have previously been used in other social sciences studies, no other marriage migration study in the literature uses these techniques, thus enabling the article to contribute to the literature.

Where do Overweight Women in Ghana Live? Answers from Exploratory Spatial Data Analysis.

Contextual influence on health outcomes is increasingly becoming an important area of research. Analytical techniques such as spatial analysis help explain the variations and dynamics in health inequalities across different context and among different population groups. This paper explores spatial clustering in body mass index among Ghanaian women by analysing data from the 2008 Ghana Demographic and Health Survey using exploratory spatial data analysis techniques. Overweight was a more common occurrence in urban areas than in rural areas. Close to a quarter of the clusters in Ghana, mostly those in the southern sector contained women who were overweight. Women who lived in clusters where the women were overweight were more likely to live around other clusters where the women were also overweight. The results suggest that the urban environment could be a potential contributing factor to the high levels of obesity in urban areas of Ghana. There is the need for researchers to include a spatial dimension to obesity research in Ghana paying particular attention the urban environment.

The Impact of Slavery on Racial Inequality in Poverty in the Contemporary U.S. South

Despite Civil Rights legislation, racial inequality persists, especially in the context of poverty. This study advances the literature on racial inequality and the Southern legacy of slavery by examining slavery's relationship with inequality in poverty. I analyze county-level U.S. Census data using regression and spatial data analysis techniques. I find the 1860 slave concentration is related to contemporary black-white inequality in poverty, independent of contemporary demographic and economic conditions, racialized wealth disparities and racial threat. My research suggests the importance of slavery for shaping existing U.S. racial inequality patterns. Insights derived from this research, including the formulation of legacy as a place-based, continuous phenomenon that is distinct from racial threat, provide the basis for future research on legacy's mechanisms.

Urban Crime and Ethnicity

Using spatial data analysis techniques, we compare the spatial distribution of crime and the black population density across the London boroughs. We show that the higher is the density of the black population in a given borough, the higher is the crime rate. This effect is still positive but lower for neighboring boroughs and ceases to exist beyond a 40 minute driving distance. Such results are consistent with models of social interactions where relationships are stronger between individuals of the same ethnic group and highly localized.

Geographic Variations in Heart Failure Hospitalizations Among Medicare Beneficiaries in the Tennessee Catchment Area

IntroductionAlthough differences in heart failure (HF) hospitalization rates by race and sex are well documented, little is known about geographic variations in hospitalizations for HF, the most common discharge diagnosis for Medicare beneficiaries. MethodsUsing exploratory spatial data analysis techniques, the authors examined hospitalization rates for HF as the first-listed discharge diagnosis among Medicare beneficiaries in a 10-state Tennessee catchment area, based on the resident states reported by Tennessee hospitals from 2000 to 2004. ResultsThe age-adjusted HF hospitalization rate (per 1000) among Medicare beneficiaries was 23.3 [95% confidence interval (CI), 23.3–23.4] for the Tennessee catchment area, 21.4 (95% CI, 21.4–21.5) outside the catchment area and 21.9 (95% CI, 21.9–22.0) for the overall United States. The age-adjusted HF hospitalization rates were also significantly higher in the catchment area than outside the catchment area and overall, among men, women and whites, whereas rates among the blacks were higher outside the catchment area. Beneficiaries in the catchment area also had higher age-specific HF hospitalization rates. Among states in the catchment area, the highest mean county-level rates were in Mississippi (30.6±7.6) and Kentucky (29.2±11.5), and the lowest were in North Carolina (21.7±5.7) and Virginia (21.8±6.6). ConclusionsKnowledge of these geographic differences in HF hospitalization rates can be useful in identifying needs of healthcare providers, allocating resources, developing comprehensive HF outreach programs and formulating policies to reduce these differences.

Does Economic Geography Matter for Pakistan? A Spatial Exploratory Analysis of Income and Education Inequalities.

Generally, econometric studies on socio-economic inequalities consider regions as independent entities, ignoring the likely possibility of spatial interaction between them. This interaction may cause spatial dependency or clustering, which is referred to as spatial autocorrelation. This paper analyses for the first time, the spatial clustering of income, income inequality, education, human development, and growth by employing spatial exploratory data analysis (ESDA) techniques to data on 98 Pakistani districts. By detecting outliers and clusters, ESDA allows policy makers to focus on the geography of socio-economic regional characteristics. Global and local measures of spatial autocorrelation have been computed using the Moran‘s I and the Geary‘s C index to obtain estimates of the spatial autocorrelation of spatial disparities across districts. The overall finding is that the distribution of district wise income inequality, income, education attainment, growth, and development levels, exhibits a significant tendency for socio-economic inequalities and human development levels to cluster in Pakistan (i.e. the presence of spatial autocorrelation is confirmed). Keywords: Pakistan, Spatial Effects, Spatial Exploratory Analysis, Spatial Disparities, Income Inequality, Education Inequality, Spatial Autocorrelation

A Simplified Indicator of Social Well-Being in the United States: Examining the Ecological Impact of Family Formation within a County Level Framework

In 1995, a study entitled “Does Marriage Matter?” was published by Linda Waite in the journal of Demography, which was concerned with the direction of such causal relationships. While Waite’s examination of the causal relationships associated with marriage, and most other analyses of this type, is primarily concerned with the individual level effects of marriage on a variety of outcomes, little is understood concerning the ecological effect of community marriage rates on levels of aggregate well-being. This study aims to contribute to this gap through the implementation of a recent conceptualization of social well-being as a multi-dimensional measure incorporating both biological, operationalized as average life expectancy, and social phenomena, operationalized as, community level crime rates (Raphael, Making the links: what do health promotion, crime prevention, and social development have in common? in 2004). It is important to understand such aggregate level effects in the face of the existing literature, which relies heavily on relational associations which could be subject to ecological fallacy. Analytic techniques incorporate Exploratory Spatial Data Analysis and spatial regression techniques, due to the high existence of spatial autocorrelation often evident in census data, as a way of understanding the effect of the aggregate level marriage rate on the constructed social well being indicator.

Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression

Significant relationships between land use and water quality have been found in watersheds around the world. The relationships are commonly examined by conventional statistical methods, such as ordinary least squares regression (OLS) and Spearman’s rank correlation analysis, which assume the relationships are constant across space. However, the relationships often might vary over space because watershed characteristics and pollution sources are not the same in different places. This study applies an exploratory spatial data analysis (ESDA) technique, geographically weighted regression (GWR), to analyze the spatially varying relationships between six land use and fourteen water quality indicators across watersheds with different levels of urbanization in eastern Massachusetts, USA. The study finds that the relationships between water quality and land use and the abilities of land use indicators to explain water quality vary across the urbanization gradient in the studied watersheds. Percentages of commercial and industrial lands have stronger positive relationships with the concentrations of water pollutants in less-urbanized areas than in highly-urbanized areas. Percentages of agricultural land, residential land, and recreation use show significant positive relationships with the concentrations of water pollutants at some sampling sites within less-urbanized areas, whereas they have significant negative relationships at some sampling sites within highly-urbanized areas. Thus, the adverse impact of land use changes on water quality is more substantial in less-urbanized suburban areas than that in highly-urbanized central cities. Pollution control policies should be adjusted in different areas based on the spatially varying pollution sources and good predictors of water quality.

Terrestrial laser scanning and exploratory spatial data analysis for the mapping of weathering forms on rock art panels

Rock art conservators are faced with complex decisions to prioritize rock art panels for protection from destructive forces of weathering. We provide a system to facilitate such decision making that blends traditional remote sensing with interactive techniques of exploratory spatial data analysis. Our system, ‘mapping weathering forms in three-dimensional (3D)’ (MapWeF) uses a 3D laser scanning device for sub-centimetre data collection from in situ rock surfaces. After image and digital surface model processing, key rock weathering forms are highlighted through classification. Supervised classification builds training classes as a user probes known weathering forms. Guided by these training classes, the user then interactively brushes and assembles pixels from scatter plots until the user is confident that all manifestations of a particular weathering form have been mapped. The purpose of MapWeF is to construct detailed maps that highlight regions of decay on rock art panels. These maps can help rock art conservators take action on panels in need of urgent preservation or remediation.

Spatial and temporal dimensions of neighborhood effects on high school graduation

Research into the effects of neighborhood characteristics on children’s behavior has burgeoned in recent years, but these studies have generally adopted a limited conceptualization of the spatial and temporal dimensions of neighborhood effects. We use longitudinal data from the Panel Study of Income Dynamics and techniques of spatial data analysis to examine how both the socioeconomic characteristics of extralocal neighborhoods—neighborhoods surrounding the immediate neighborhood of residence—and the duration of exposure to disadvantaged neighborhoods throughout the childhood life course influence the likelihood of graduating from high school. Among blacks and whites, socioeconomic advantage in the immediate neighborhood increases the likelihood of completing high school, but among whites higher levels of socioeconomic advantage in extralocal neighborhoods decrease high school graduation rates. Extralocal neighborhood advantage suppresses the influence of advantage in the immediate neighborhood so that controlling for extralocal conditions provides stronger support for the neighborhood effects hypothesis than has previously been observed. Exposure to advantaged neighborhoods over the childhood life course exerts a stronger effect than point-in-time measures on high school graduation, and racial differences in exposure to advantaged neighbors over the childhood life course help to suppress a net black advantage in the likelihood of completing high school.

The impact of land development regulation on residential tree cover: An empirical evaluation using high-resolution IKONOS imagery

Public sector policies regulating land development can have a major influence on the environmental impacts of urban development, yet few empirical studies have examined the impact of these policies. Our study attempted to address this gap by examining the relationship between a land development code associated with the protection of trees and the extent of urban tree cover on residential parcels. We developed an accurate very high resolution urban tree cover classification using IKONOS imagery, quantified parcel-specific tree cover and evaluated the relationship between year of construction, adoption of a tree ordinance and extent of canopy cover in Tampa, Florida, USA and in nearby areas lacking similar regulation. Statistical results revealed significantly greater tree cover on parcels with homes built after compared to before adoption of tree protection policies, despite a trend toward increased building cover. After controlling for the effects of spatial dependence and relevant physical and socio-demographic characteristics at the census block, multivariate regression results indicate the percentage of homes built after implementing the policy was a strong predictor of increased tree cover. These results and comparisons with nearby areas further suggest Tampa's 1974 tree protection regulations appears to have been effective at increasing tree cover, but other influential factors were also at work. This study demonstrates the potential for evaluating the impact of specific land development policies using remote sensing and other spatial data analysis techniques. In addition, we argue that scientific evidence of the effectiveness of past policy should be used a guide for the creation of future land development regulation.

Brazilian spatial dynamics in the long term (1872–2000): “path dependency” or “reversal of fortune”?

This paper analyzes the spatial dynamics of Brazilian regional inequalities between 1872 and 2000 using contemporary tools. The first part of the paper provides new estimates of income per capita in 1872 by municipality using census and electoral information on income by occupation. The level of analysis is the Minimum Comparable Areas 1872–2000 developed by Reis et al. (Areas minimas comparaveis para os periodos intercensitarios de 1872 a 2000, 2007). These areas are the least aggregation of adjacent municipalities required to allow consistent geographic area comparisons between census years. In the second section of the paper, Exploratory Spatial Data Analysis, Markov chains and stochastic kernel techniques (spatially conditioned) are applied to the dataset. The results suggest that, in broad terms, the spatial pattern of income distribution in Brazil during that period of time has remained stable.

Application and Integration of Lattice Data Analysis, Network K-Functions, and Geographic Information System Software to Study Ice-Related Crashes

Advances in geographic information system (GIS) software and exploratory spatial data analysis (ESDA) techniques give transportation safety engineers tools to observe and analyze safety-related data from a new perspective. This research takes the use of GIS software and ESDA techniques one step further by incorporating advanced statistical techniques for a more thorough and complex analysis of safety data. This is achieved by implementing a network-constrained cross K-function to analyze the relationship between bridges and the occurrences of ice-related crashes within a county. The counties in Wisconsin included in the analysis were selected through the use of the local Moran's I statistic; this statistic allows for the selection of counties within the same geographical area, which have similar parameters (in this case, ice-related crash rates). The objective of this research is to explore the relationship between ice-related crashes and bridges in counties that display similar ice-related crash rates, to compare and analyze winter maintenance techniques. The results identify clustering of ice-related crashes around bridges in four counties with similar ice-related crash rates in southeast Wisconsin. Similarly, two of four counties show clustering of ice-related crashes around bridges in northwest Wisconsin. These results make a strong case to suggest that counties in these regions should focus additional winter maintenance efforts at bridge locations. In addition, this research shows how the use of advanced spatial statistical techniques, particularly network-based statistics applied within a GIS environment, can be used as a unique and innovative approach toward safety data analysis.