Titlebook: Spatial Socio-econometric Modeling (SSEM); A Low-Code Toolkit f Manuel S. González Canché Textbook 2023 The Editor(s) (if applicable) and T

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Data Formats, Coordinate Reference Systems, and Differential Privacy Frameworksmats (vector and raster), coordinate reference systems (projected and unprojected), and data privacy or projection frameworks (data swapping, differential privacy, and jittering). Accordingly, the purpose of this chapter is to provide readers with the set of practical elements and understandings req

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Access and Management of Spatial or Geocoded Datate all the analyses presented in this book. The main goal of this code presentation is to illustrate the rationale required for analysts to . this code in their own projects. Accordingly, in addition to the code discussion presented in these chapters, standalone code files are being made available f

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Distanceswe demonstrate how to use these geocoded databases (along with others) to measure distances, with an emphasis on approaches that are more precise in . or . capturing travel distances and navigation times compared to the traditional distance estimation method that only accounts for Earth’s curvature—

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SODA: Spatial Outcome Dependence or AutocorrelationA occurs when the outcomes of one unit [co]vary in the same direction of the outcomes of its neighboring units. Since these neighbors are identified based on distance, travel times, or contiguity rules, as we discussed in Chap. ., the resulting covariation inherently has a spatial component that is

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SSEM Regression Based Analysesunivariate, which constrain our capacity to model factors that may impact outcome variation. Specifically, in the social sciences we are more interested in multivariate analyses, wherein we may explain the expected variation of an outcome as a function of predictor and control indicators in a statis

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Visualization, Mining, and Density Analyses of Spatial and Spatio-Temporal Dataskillset with the mapping of points, polygons, and relationships cross-sectionally and over time, with the added benefit of describing the distribution of values associated with those features and/or outcomes. Additionally, we present a new visualization tool called Geospatial Point Density, wherein

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Final Wordsatitude mostly in first person. I will remain forever grateful with my professors, mentors, and all the many authors’ contributions, from conceptual to applied perspectives, that made possible the materialization of this book. Although it took me over a decade to prepare to formally start the develo

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Access and Management of Spatial or Geocoded Datater for Education Statistics, and the Internal Revenue Service. We then showcase how to geocode, merge or join, and crosswalk different data sources to form .. As part of this discussion, we also show how to replicate most of the figures presented in previous chapters.