Exploratory Spatial Data Analysis Linking SpaceStat and ArcView

Anselin, Luc; Bao, Shuming

doi:10.1007/978-3-662-03499-6_3

Cited by 166 publications

(140 citation statements)

References 37 publications

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“…It is important that we model these spatial influences, and much has been written about the spatial techniques used to control for these issues (Anselin 1998. To account for these problems we created equivalent spatial crime lag variables for each of the models, e.g.…”

Section: Spatial Lag Variablesmentioning

confidence: 99%

Criminal Neighbourhoods: Does the Density of Prior Offenders in an Area Encourage others to Commit Crime?

et al. 2014

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Using crime data over a period of a decade for Glasgow, this paper explores whether the density of prior offenders in a neighbourhoods has an influence on the propensity of others to (re)commence offending. The study shows that the number of 'newly active' offenders in a neighbourhood in the current quarter is positively associated with the density of prior offenders for both violent and property crime from the previous two years. In the case of 'newly active' property offenders, the relationship with active prior offenders is only apparent when prior offender counts exceed the median. The paper postulates that intraneighbourhood social mechanisms may be at work to create these effects. The results suggest that policies which concentrate offenders in particular neighbourhoods may increase the number of 'newly active' offenders, and point to evidence of a threshold at which these effects take place.

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Section: Spatial Lag Variablesmentioning

confidence: 99%

Criminal Neighbourhoods: Does the Density of Prior Offenders in an Area Encourage others to Commit Crime?

et al. 2014

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“…ESDA [13] is a collection of techniques to describe and visualize spatial distributions; identify spatial outliers; discover patterns of spatial association, clusters or hot-spots; and suggest spatial regimes or other forms of spatial heterogeneity. ESDA was applied based on Global Moran's I, Moran Scatterplot, and LISA statistics.…”

Section: Exploratory Spatial Data Analysismentioning

confidence: 99%

“…This model assumed that the autoregressive process only occurs in the response variable. The spatial lag model that was possible to be formed in this research is as follows: (13) with ρ is spatial lag coefficient parameters on the response variable.…”

Section: Spatial Regressionmentioning

confidence: 99%

Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia

Januardi¹,

Utomo²

2017

CST

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Java Island is the center of development in Indonesia, and yet poverty remains its major problem. The pockets of poverty in Java are often located in urban and rural areas, dominated by productive age group population with low education. Taking into account spatial factors in determining policy, policy efficiency in poverty alleviation can be improved. This paper presents a Spatial Error Model (SEM) approach to determine the impact of education on poverty alleviation in Java. It not only focuses on the specification of empirical models but also in the selection of parameter estimation methods. Most studies use Maximum Likelihood Estimator (MLE) as a parameter estimation method, but in the presence of normality disturbances, MLE is generally biased. The assumption test on the poverty data of Java showed that the model error was not normally distributed and there was spatial autocorrelation on the error terms. In this study we used SEM using Generalized Methods of Moment (GMM) estimation to overcome the biases associated with MLE. Our results indicate that GMM is as efficient as MLE in determining the impact of education on poverty alleviation in Java and robust to non-normality. Education indicators that have significant impact on poverty alleviation are literacy rate, average length of school year, and percentage of high schools and university graduates.

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“…Some GIS include scripts runnable from a graphical user interface (GUI) that can calculate the local Moran's I and other local indicators of spatial association (LISA) but they are poorly integrated. Openshaw (1994) and Anselin (1994Anselin ( , 1998 attempt to define the type of exploratory data analysis techniques that GIS should try to incorporate. Anselin (1994) advocates the integration in the GIS of local measures of spatial association, spatial lag pies, spatial lag scatterplots, Moran scatterplots as well as variogram clouds and pocket plots.…”

Section: Introductionmentioning

confidence: 99%

“…Haslett, Bradley, Craig, Unwin, and Wills (1991) link histograms, double histograms, scatterplot matrices, and varioclouds (see Section 4) with the maps using the Pascal language. Anselin and Bao (1997) implement the methods advocated in Anselin (1994) linking ArcView and SpaceStat. Brundson (1998) implements the scatterplot matrix, the neighbour plot and the angle plot (see Section 4) plus some spatial smoothing of maps for trend detection with XLISP-STAT.…”

Section: Introductionmentioning

confidence: 99%

GeoXp: AnRPackage for Exploratory Spatial Data Analysis

Laurent¹,

Ruiz‐Gazen²,

Thomas‐Agnan³

2012

J. Stat. Soft.

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We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use a data set concerning public schools of the French MidiPyrénées region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots, histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.

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Exploratory Spatial Data Analysis Linking SpaceStat and ArcView

Cited by 166 publications

References 37 publications

Criminal Neighbourhoods: Does the Density of Prior Offenders in an Area Encourage others to Commit Crime?

Criminal Neighbourhoods: Does the Density of Prior Offenders in an Area Encourage others to Commit Crime?

Application of spatial error model using GMM estimation in impact of education on poverty alleviation in Java, Indonesia

GeoXp: AnRPackage for Exploratory Spatial Data Analysis

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