001471705 000__ 05581cam\\22006257i\4500
001471705 001__ 1471705
001471705 003__ OCoLC
001471705 005__ 20230908003310.0
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001471705 008__ 230713s2023\\\\sz\a\\\\ob\\\\001\0\eng\d
001471705 019__ $$a1389340387
001471705 020__ $$a9783031248573$$qelectronic book
001471705 020__ $$a3031248570$$qelectronic book
001471705 020__ $$z3031248562
001471705 020__ $$z9783031248566
001471705 0247_ $$a10.1007/978-3-031-24857-3$$2doi
001471705 035__ $$aSP(OCoLC)1390204511
001471705 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dYDX$$dEBLCP$$dYDX$$dOCLCQ
001471705 049__ $$aISEA
001471705 050_4 $$aHA29$$b.G66 2023
001471705 08204 $$a300.15195$$223/eng/20230713
001471705 1001_ $$aGonzález Canché, Manuel S.,$$eauthor.
001471705 24510 $$aSpatial socio-econometric modeling (SSEM) :$$ba low-code toolkit for spatial data science and interactive visualizations using R /$$cManuel S. González Canché.
001471705 264_1 $$aCham, Switzerland :$$bSpringer,$$c2023.
001471705 300__ $$a1 online resource (518 pages) :$$billustrations (black and white, and color).
001471705 336__ $$atext$$btxt$$2rdacontent
001471705 337__ $$acomputer$$bc$$2rdamedia
001471705 338__ $$aonline resource$$bcr$$2rdacarrier
001471705 4901_ $$aSpringer texts in social sciences
001471705 504__ $$aIncludes bibliographical references and index.
001471705 5050_ $$aPart I Conceptual and Theoretical Underpinnings -- Chapter 1. SPlaces -- Chapter 2. Operationalizing Splaces -- Chapter 3. Data Formats, Coordinate Reference Systems, and Differential Privacy Frameworks -- Part II Data Science SSEM Identification Tools: Distances, Networks, and Neighbors -- Chapter 4. Access and Management of Spatial or Geocoded Data -- Chapter 5. Distances -- Chapter 6. Geographical Networks as Identification Tools -- Part III SSEM Hypothesis Testing of Cross-sectional and Spatio-temporal Data and Interactive Visualizations -- Chapter 7. SODA: Spatial Outcome Dependence or Autocorrelation -- Chapter 8. SSEM Regression Based analyses -- Chapter 9. Visualization, Mining, and Density Analyses of Spatial and Spatio-temporal Data -- Chapter 10. Final Words -- Glossary -- Index.
001471705 506__ $$aAccess limited to authorized users.
001471705 520__ $$aWith the primary goal of expanding access to spatial data science tools, this book offers dozens of minimal or low-code functions and tutorials designed to ease the implementation of fully reproducible Spatial Socio-Econometric Modeling (SSEM) analyses. Designed as a University of Pennsylvania Ph.D. level course for sociologists, political scientists, urban planners, criminologists, and data scientists, this textbook equips social scientists with all concepts, explanations, and functions required to strengthen their data storytelling. It specifically provides social scientists with a comprehensive set of open-access minimal code tools to: Identify and access place-based longitudinal and cross-sectional data sources and formats Conduct advanced data management, including crosswalks, joining, and matching Fully connect social network analyses with geospatial statistics Formulate research questions designed to account for place-based factors in model specification and assess their relevance compared to individual- or unit-level indicators Estimate distance measures across units that follow road network paths Create sophisticated and interactive HTML data visualizations cross-sectionally or longitudinally, to strengthen research storytelling capabilities Follow best practices for presenting spatial analyses, findings, and implications Master theories on neighborhood effects, equality of opportunity, and geography of (dis)advantage that undergird SSEM applications and methods Assess multicollinearity issues via machine learning that may affect coefficients' estimates and guide the identification of relevant predictors Strategize how to address feedback loops by using SSEM as an identification framework that can be merged with standard quasi-experimental techniques like propensity score models, instrumental variables, and difference in differences Expand the SSEM analyses to connections that emerge via social interactions, such as co-authorship and advice networks, or any form of relational data The applied nature of the book along with the cost-free, multi-operative R software makes the usability and applicability of this textbook worldwide.
001471705 588__ $$aDescription based on print version record.
001471705 650_0 $$aSocial sciences$$xStatistical methods.
001471705 650_0 $$aSpatial analysis (Statistics)
001471705 650_0 $$aR (Computer program language)
001471705 655_0 $$aElectronic books.
001471705 77608 $$iPrint version:$$aGONZALEZ CANCHE, MANUEL S.$$tSPATIAL SOCIO-ECONOMETRIC MODELING (SSEM).$$d[Place of publication not identified] : SPRINGER INTERNATIONAL PU, 2023$$z3031248562$$w(OCoLC)1356502028
001471705 830_0 $$aSpringer texts in social sciences.
001471705 852__ $$bebk
001471705 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-24857-3$$zOnline Access$$91397441.1
001471705 909CO $$ooai:library.usi.edu:1471705$$pGLOBAL_SET
001471705 980__ $$aBIB
001471705 980__ $$aEBOOK
001471705 982__ $$aEbook
001471705 983__ $$aOnline
001471705 994__ $$a92$$bISE