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Inference for Income Mobility Measures in the Presence of Spatial Dependence

Income mobility measures provide convenient and concise ways to reveal the dynamic nature of regional income distributions. Statistical inference about these measures is important especially when it comes to a comparison of two regional income …

Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

Markov chain modeling for air pollution index based on maximum a posteriori method

Air pollution is a major environmental problem, which brings about a threat to human health and the natural environment. Thus, determination and assessment of the level of air pollution is an important component in monitoring of the air quality. This …

A roundtable discussion: Defining urban data science

The field of urban analytics and city science has seen significant growth and development in the past 20 years. The rise of data science, both in industry and academia, has put new pressures on urban research, but has also allowed for new analytical …

Smoothed Estimators for Markov Chains with Sparse Spatial Observations

Empirical applications of the Markov chain model and its spatial extensions suffer from issues induced by the sparse transition probability matrix, which usually results from adopting maximum likelihood estimators (MLEs). Two discrete kernel …

MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale

Geographically weighted regression (GWR) is a spatial statistical technique that recognizes that traditional global regression models may be limited when spatial processes vary with spatial context. GWR captures process spatial heterogeneity by …

A comment on geographically weighted regression with parameter-specific distance metrics

Scale is a fundamental geographic concept, and a substantial literature exists discussing the various roles that scale plays in different geographical contexts. Relatively little work exists, though, that provides a means of measuring the geographic …

Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the …

Inference in Multiscale Geographically Weighted Regression

A recent paper expands the well-known geographically weighted regression (GWR) framework significantly by allowing the bandwidth or smoothing factor in GWR to be derived separately for each covariate in the model---a framework referred to as …

Regional inequality dynamics, stochastic dominance, and spatial dependence

Stochastic dominance tests are used to measure whether distributions are directionally-distinct. Using stochastic dominance measures, an income distribution can be measured to be more favorable for its members at all income levels than another …