Spatial autocorrelation

While the geography of crime has been a focal concern in criminology from the very start of the discipline, the development and use of statistical methods specifically designed for spatially referenced data has evolved more recently. This chapter gives an overview of the application of such methods in research on crime and criminal justice, and provides references to the general

This paper examines the per capita income convergence process among 23 Argentinian provinces over the period 1983-2002. The purpose of this work is to apply new estimation methods following two-step procedure as in Badinger et al. (2004). We combine a spatial filtering of variables to remove the spatial correlation (Getis and Griffith, 2002) and suitable estimators for dynamic panels (using first-differenced and system GMM estimators). Our estimations on filtered variables reveal a conditional convergence process between Argentinian provinces and a positive and significant impact of agglomeration variables on growth rate. Hence, our results show that ignoring spatial distortions due to geographic proximity misleads estimations and underestimates the speed of convergence specifically for provinces which are distant from Buenos Aires. Moreover, we improve estimations of agglomeration effects when spatial autocorrelation is removed.

The postprocessing algorithm introduced by Yao for imposing the spectral amplitudes of a target covariance model is shown to be efficient in correcting the smoothing effect of estimation maps, whether obtained by kriging or any other interpolation technique. As opposed to stochastic simulation, Yao's algorithm yields a unique map starting from an original, typically smooth, estimation map. Most importantly it is shown that reproduction of a covariance/semivariogram model (global accuracy) is necessarily obtained at the cost of local accuracy reduction and increase in conditional bias. When working on one location at a time, kriging remains the most accurate (in the least squared error sense) estimator. However, kriging estimates should only be listed, not mapped, since they do not reflect the correct (target) spatial autocorrelation. This mismatch in spatial autocorrelation can be corrected via stochastic simulation, or can be imposed a posteriori via Yao's algorithm.

This empirical study focuses on competition between local
governments as they strive to attract companies through the
auction of land use rights. In the literature on competition between
local governments, the focus has tended to be toward growth rates
based on performance evaluation or tax rate-based competition
for attracting companies. In China, the property tax system is still
underdeveloped and local governments cannot independently set tax
rates. Therefore, this study focuses on a type of ‘dumping’ activity by
which local governments auction land use rights to attract industrial
companies and empirically test this hypothesis using the spatial lag
model.

The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and two-dimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.

Background: Most heat-related deaths occur in cities and future trends in global climate change and urbanization may amplify this trend. Understanding how neighborhoods affect heat mortality fills an important gap between studies of individual susceptibility to heat and broadly comparative studies of temperature-mortality relationships in cities.

Objectives: We estimated neighborhood effects of population characteristics and the built and natural environments on deaths due to heat exposure in Maricopa County, Arizona (2000-2008).

Methods: We used U.S. Census data and remotely sensed vegetation and land surface temperature to construct indicators of neighborhood vulnerability and a Geographic Information System to map vulnerability and residential addresses of people who died from heat exposure in 2,081 census block groups. Binary logistic regression and spatial analysis were used to associate deaths with neighborhoods.

Results: Neighborhood scores on three factors – Socioeconomic Vulnerability, Elderly/Isolation, and Unvegetated Area – varied widely throughout the study area. The preferred model (based on fit and parsimony) for predicting the odds of one or more deaths from heat exposure within a census block group included the first two factors and surface temperature in residential neighborhoods, holding constant population size. Spatial analysis identified clusters of neighborhoods with the highest heat vulnerability scores. A large proportion of deaths occurred among people, including homeless persons, who lived in the inner cores of the largest cities and an industrial corridor.

Conclusions: Place-based indicators of vulnerability complement analyses of person-level heat risk factors. Surface temperature might be used in Maricopa County to identify the most heat-vulnerable neighborhoods but more attention to the socio-ecological complexities of climate adaptation is needed.

Transit oriented design (TOD) – an increase in density around transit stations - has arisen in many of Australia’s capital cities as a way to encourage mass transit ridership as well as to efficiently utilise the increase in foot and vehicle traffic that transit stations create. However, the implementation of TODs in Melbourne has faced strong opposition due to residents’ perception that the disamenities of a TOD will outweigh the benefits resulting in negative impacts on property prices. This research analyses the relationship between proximity to a TOD and residential home prices.  Results indicate that proximity to a TOD is positively related to property prices, even after controlling for neighborhood factors such as street connectivity and overall land use mix.  By testing a variety of transformations of distance, we find that the benefits of TOD proximity extend approximately 1250 meters from the Box Hill station.  From a methodological standpoint, we find that more flexible treatments of distance variables in spatial autoregressive and spline models produce better model fit and lead to results more in line with urban economic theory.

The combined use of remote sensing based land cover classification and landscape metrics has provided a positive step toward gaining a comprehensive understanding of the features of landscape structure. However, numerous limitations of land cover classification indicate that the utilization of classified thematic maps in landscape pattern analysis is questionable and may even lead to large errors in subsequent analyses. Instead of generating and employing detailed land cover classification maps, the utility of local spatial autocorrelation indices derived directly from satellite imagery to measure landscape fragmentation was examined. Since local spatial autocorrelation can capture spatial pattern at a local scale, it can be expected to detail the spatial heterogeneity for various parts of a landscape instead of providing a single value as in the case with the global measures. This study compares the traditional landscape metrics to the use of satellite imagery based local spatial autocorrelation measures in quantifying landscape structure over Phoenix urban area. Two local spatial autocorrelation indices, the Getis statistic and the local Moran's I were employed in evaluating landscape pattern, using normalized indices as the inputs. Results show that there is a clear relationship between local spatial autocorrelation indices and FRAGSTATS metrics. Both the Getis statistic and the local Moran's I can serve as useful indicators of landscape heterogeneity, for the entire landscape, and for different land use and land cover types. The paper provides a feasible methodology for urban planners and resource managers for effectively characterizing landscape fragmentation using continuous dataset.

Journal of Real Estate Finance and Economics, Vol. 17:1, 61±85 (1998) © 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. ... Analysis of Spatial Autocorrelation in House Prices ... SABYASACHI BASU Statistics Department, Southern Methodist ...

genalex is a user-friendly cross-platform package that runs within Microsoft Excel, enabling population genetic analyses of codominant, haploid and binary data. Allele frequency-based analyses include heterozygosity, F statistics, Nei's genetic distance, population assignment, probabilities of identity and pairwise relatedness. Distance-based calculations include amova, principal coordinates analysis (PCA), Mantel tests, multivariate and 2D spatial autocorrelation and twogener. More than 20 different graphs summarize data and aid exploration. Sequence and genotype data can be imported from automated sequencers, and exported to other software. Initially designed as tool for teaching, genalex 6 now offers features for researchers as well. Documentation and the program are available at http://www.anu.edu.au/BoZo/GenAlEx/

Transaction activities at Jetwash Auto Spa are still recorded manually. In service transactions, customer acceptance only relies on manual transaction evidence, it makes the output format is not standardized. Therefore, sometimes it can effect lost documents. The stock of goods in car salons is only recorded in some books so that it can not be controlled, such as how many the amount of goods available and that is exhausted, otherwise it is difficult to adjust the price of goods with market prices is also a constraint due to the absence of price history of goods. Then, with the design analysis Jetwash Auto Spa application Tangerang web-based it becomes easier to do data collection.

Ordinary kriging and cokriging are two geostatistical techniques used to create continuous maps of spatially autocorrelated attributes. Both procedures use the same primary variable, but cokriging, like multivariate statistics, incorporates additional independent variables. In this study, the primary variable is cubic foot volume and the secondary variable, mean normalized difference vegetation index, is calculated from an August 1996 Landsat Thematic

In the first part of this chapter we provide a brief overview of the range of different ecological theories that have been advanced to explain the link between socially disadvantaged neighbourhoods and levels of crime and perceptions of (or fear of) crime. In the second part of the chapter we consider the two main weaknesses of neighbourhood effect studies – definition of neighbourhood and selection bias – and we demonstrate, by way of empirical example, how understanding of the ways in which neighbourhoods affect local residents can be further extended by allowing for the additional influences of surrounding local areas, using data from the British Crime Survey. This draws upon methodological work incorporating spatial autocorrelation in neighbourhood  models (see for example Morenoff et al., 2001). In the third and final part of the chapter, we discuss the extent to which this range of neighbourhood effects research has influenced policing and crime reduction policy initiatives in England and Wales. Here we discuss the growing importance that neighbourhoods and local communities have held in policy strategies to reduce crime and raise public confidence. We highlight a number of initiatives including neighbourhood watch, neighbourhood policing, new deals for communities, community wardens (latterly Police Community Support Officers) and crime and disorder reduction partnerships.

Long-term modeling of agricultural land use is central in global scale assessments of climate change, food security, biodiversity, and climate adaptation and mitigation policies. We present a global-scale dynamic land use allocation model and show that it can reproduce the broad spatial features of the past 100 years of evolution of cropland and pastureland patterns. The modeling approach integrates economic theory, observed land use history, and data on both socioeconomic and biophysical determinants of land use change, and estimates relationships using long-term historical data, thereby making it suitable for long-term projections. The underlying economic motivation is maximization of expected profits by landowners within each grid cell. The model predicts fractional land use for cropland and pastureland within each grid cell based on socioeconomic and biophysical driving factors that change with time. The model explicitly incorporates the following key features: (1) land use competition, (2) spatial heterogeneity in the nature of driving factors across geographic regions, (3) spatial heterogeneity in the relative importance of driving factors and previous land use patterns in determining land use allocation, and (4) spatial and temporal autocorrelation in land use patterns.

We show that land use allocation approaches based solely on previous land use history (but disregarding the impact of driving factors), or those accounting for both land use history and driving factors by mechanistically fitting models for the spatial processes of land use change do not reproduce well long-term historical land use patterns. With an example application to the terrestrial carbon cycle, we show that such inaccuracies in land use allocation can translate into significant implications for global environmental assessments. The modeling approach and its evaluation provide an example that can be useful to the land use, Integrated Assessment, and the Earth system modeling communities.