Clustering multivariate spatial data based on local measures of spatial autocorrelation.

A growing interest in clustering spatial data is emerging in several areas, from local economic development to epidemiology, from remote sensing data to environment analyses. However, methods and procedures to face such problem are still lacking. Local measures of spatial autocorrelation aim at identifying patterns of spatial dependence within the study region. Mapping these measures provide the basic building block for identifying spatial clusters of units. If this may work satisfactorily in the univariate case, most of the real problems have a multidimensional nature. Thus, we need a clustering method based on both the multivariate data information and the spatial distribution of units. In this paper we propose a procedure for exploring and discover patterns of spatial clustering. We discuss an implementation of the popular partitioning algorithm known as K-means which incorporates the spatial structure of the data through the use of local measures of spatial autocorrelation. An example based on a set of variables related to the labour market of the Italian region Umbria is presented and deeply discussed.

Geography and economic performance: exploratory spatial data analysis for Great Britain

This paper uses the techniques of exploratory spatial data analysis to analyse patterns of spatial association for different indicators of economic performance, and in so doing identify and describe the spatial structure of economic performance for Great Britain. This approach enables us to identify a number of significant local regimes – clusters of areas in which income per worker differs significantly from the global average – and investigate whether these come about primarily through spatial association in occupational composition or in productivity. Our results show that the contributions of occupational composition and productivity vary significantly across local regimes. The ‘winner’s circle’ of areas in the south and east of England benefits from both above average levels of productivity and better than average occupational composition, while the low income regime in the north of England suffers particularly from poor occupational composition. Keywords; regional disparities, income per worker, productivity, occupational composition, spatial autocorrelation JEL Classification: O18, O4, R11, R12

Fine-Grained Car Detection for Visual Census Estimation

Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201

Package ecespa

Documentation for the R-package "ecespa

A Spatially Explicit Census Reveals Population Structure and Recruitment Patterns for a Narrowly Endemic Pine, Pinus torreyana

We conducted a census of the rare pine, Pinus torreyana ssp.  torreyana, in order to determine: a) what is the population size and is it stable, growing or declining; b) what is the spatial variation in population structure; c) what is the spatial patterning of trees in different life stages; and, d) what environmental factors are related to seedling recruitment?  Trees were classified into four stages classes: adult (160 cm tall with cones); sub-adult (160 cm without cones); saplings (30-160 cm), and seedlings (30 cm).  Stem diameter was measured for adults and sub-adults, and height for saplings and seedlings.  Stands were defined by spatial clustering of the tree map.  Univariate and bivariate point pattern analyses were used to explore spatial patterns for adult and juvenile trees and identify potential stand development processes such as density dependence, dispersal limitations, and patchy recruitment.  Logistic regression was used to analyze seedling establishment and survival in relation to environmental variables derived from digital maps.  We expected to find little or no recruitment based on earlier studies.  Instead, 5422 trees were mapped and measured, and tree size had “reverse J-shaped†distribution suggestive of a recruiting population.  However, population structure was variable among stands.  The predominant spatial pattern detected for adult and juvenile trees was clustering at lag distances 10 m.  Bivariate pattern analysis did not suggest repulsion between adult and juvenile size classes.  Seedlings tended to be found close to adults and on certain soil types.  Taken together, this suggests that the clustered patterns resulting from patchy recruitment and survival of juveniles persist over time.

The European Enlargement Process and Regional Convergence Revisited: Spatial Effects Still Matter.

This paper has two main goals. First, it reconsiders regional growth and convergence processes in the context of the enlargement of the European Union to new member states. We show that spatial autocorrelation and heterogeneity still matter in a sample of 237 regions over the period 1993-2002. Spatial convergence clubs are defined using exploratory spatial data analysis and a spatial autoregressive model is estimated. We find strong evidence that the growth rate of per capita GDP for a given region is positively affected by the growth rate of neighbouring regions. The second objective is to test the robustness of the results with respect to non-normality, outliers and heteroskedasticity using two other methods: The quasi maximum Likelihood and the Bayesian estimation methods.

Characterizing the spatial determinants and prevention of malaria in Kenya

The United Nations' Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used-Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino-southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.Published versio

Quality of life in the regions: An exploratory spatial data analysis for West German labor markets

Which of Germanys regions is the most attractive? Where is it best to live and work - on objective grounds? These questions are summed up in the concept quality of life. This paper uses recent research projects that determine this parameter to examine the spatial distribution of quality of life in Germany. For this purpose, an Exploratory Spatial Data Analysis is conducted which focuses on identifying statistically significant (dis-)similarities in space. An initial result of this research is that it is important to choose the aggregation level of administrative units carefully when considering a spatial analysis. The level plays a crucial role in the strength and impact of spatial effects. In concentrating on various labor market areas, this paper identifies a significant spatial autocorrelation in the quality of life, which seems to be characterized by a North-Mid-South divide. In addition, the ESDA results are used to augment the regression specifications, which helps to avoid the occurrence of spatial dependencies in the residuals. --Quality of Life,Exploratory Spatial Data Analysis,Functional Economic Areas,Spatial Econometrics,LISA Dummies

CLUB CONVERGENCE & REGIONAL SPILLOVERS IN EAST JAVA

This study try to identify the â-convergence process among regions in East Java using panel data of 37 regencies & municipalities between 1983-2002, taking into account the presence of spatial heterogeneity and spillover effects. Detection of spatial regimes using G-I* statistics (Getis & Ord, 1995) on regional per capita GDP values in 1983 found cluster of high income regions (group of “rich”) in central & eastern part of East Java, and cluster of low income regions (group of “poor”) in western part. The result of OLS & GLS regression on absolute convergence model found the existence of â-divergence process of East Java in overall period (1983-2003), consistent with the ó convergence which showing upward trend (divergence). Meanwhile, the same divergence process is also found in absolute convergence equation estimated for each club, even though in slower rate than East Java divergence rate. Using the methodology proposed by Burn, Combes, & Renard (2002) this study founds the existence of negative spillover effects between regions in “rich” clubs and from “rich” clubs to the “poor” one, where the magnitude is greater in the latter case. The club of “poor” regions is diverging faster than the “rich”. This finding is robust in every convergence equation (with or without the spillover effects). The lack of diversity on East Java’s manufacturing industries (Santosa & Michael, 2005 and Landiyanto, 2005) seems contribute to its divergence process by engaging a competitive mode between regions.â-convergence, divergence, spatial regimes, spillover effects