Spatial socio-econometric modeling (SSEM): a low-code toolkit for spatial data science and interactive visualizations using R
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| Format: | Buch |
| Sprache: | English |
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Cham
Springer
[2023]
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| Schriftenreihe: | Springer texts in social sciences
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xli, 503 Seiten Illustrationen, Diagramme 235 mm |
| ISBN: | 9783031248566 |
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Datensatz im Suchindex
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| adam_text | Contents Part I Conceptual and Theoretical Underpinnings 1 SPlaces SPlaces: Spaces, Places, and Spatial Socioeconometric Modeling Spaces Places SPlaces Inequality in Mobility Prospects Measuring Inequality and Growing Inequality Neighborhood Effects and Concentration of (dis)Advantages Spiace -Based Modeling Challenges Causality in Spatial Modeling Closing Thoughts and Next Steps Next Steps Discussion Questions References 3 3 4 5 7 9 13 14 17 17 20 20 20 21 2 Operationalizing SPlaces Delimiting and Operationalizing Neighborhoods as Splaces Representing Physical Spaces and Nesting Structures Zooming in Across Administrative Boundaries Shapefiles as Spaces Place-Based Indicators Contributing to Building Splaces Neighborhood Operationalization and Disaggregation Data Point Differentiation Across Neighborhood Levels Illustration of Splaces and Data Point Gains Tradeoffs of Data Point Differences Bringing Concepts, Shapefiles, and Place-Based Indicators Together 25 26 27 28 28 30 32 34 34 36 39 xix
CONTENTS XX ACS Published or Pre-tabulated Data Identifying Proxies for Poverty Identifying Proxies for Median Income Identifying Proxies for Unemployment Identifying Proxies for Housing Quality Identifying Proxies for Family Structure Closing Thoughts and Next Steps Next Steps Discussion Questions References Data Formats, Coordinate Reference Systems, and Differential Privacy Frameworks 55 Types of Geo-Referenced Data: Raster and Vector Data Raster Data Vector Data Vector to Raster Transformations andVice Versa Vector to Raster DataTransformations Moving From Raster to Vector Data Coordinate Reference Systems Elements of CRS Implications of Distortions Resulting fromMap Projections Commonly Used Coordinate ReferenceSystems Differential Privacy Framework (DPF) and Changes to Census Micro Data Are Differential Privacy and Synthetic Data the Same Privacy Protection Strategy? What are Differential Privacy Algorithms? Relevance of Differential Privacy For SSEM Strategies to Protect Privacy Methodological Implications of DPF for SSEM Next Steps Discussion Questions References 3 Part II 4 41 43 45 47 48 50 52 52 53 53 56 56 61 66 66 70 74 74 78 79 81 82 84 86 87 90 92 92 93 Data Science SSEM Identification Tools: Distances, Networks, and Neighbors Access and Management of Spatial or Geocoded Data -R Tutorial Installation R Infrastructure Code Rationale Reading Data from an External Source Creating Datasets from Within R Merging Joining Data 97 97 98 99 99 101 101 104
CONTENTS 5 ХХІ Installing Packages Reading Polygon Shapefiles Reading Polygons at the Country Level Reading Polygons at the County Level Reading Polygons at the ZIP Code Tabulated Area (ZCTA) Level Reading Polygons at the Census Tract Level Reading Polygons at the Block Group Level Reading Line Shapefiles All Roads Shapefiles Primary Roads Shapefiles Primary and Secondary Roads Shapefiles Reading Point Shapefiles Point Geocoding or Georeferencing Batch Geocoding Using Addresses in R Point Batch Geocoding Using ZCTAs in R Crosswalking Lower to Higher Level Crosswalking Place-Based Data Access at the Polygon Level Applying for a Census API Key Place-Based Data Access at the Point Level Joining Points with Polygons Data Closing Thoughts Next Steps Discussion Questions Replication Exercises References 106 109 109 113 Distances Distances Geolocated Data: Polygons, Points, or Bothi Why is Distance Estimation Relevant for SSEM? Data Source and Data Requirements Projections, Distortions, and Bias Concerns? Network Analysis Tools and Data Transformations Approaches to Distance Connections Identification Multiple Sources of Points Matrix to Edgelist Transformations As the Crow Flies Distance Calculations From a Matrix to a List of Connections (Edgelist) with Distances “As the Crow Flies” Distances Including Multiple Unit Types Network Route Distance Calculations: As Humans Walk As Humans Walk Distances Between Two Points Batch “As Humans Walk” Distances 165 165 166 167 169 172 173 173 175 178 183 116 118 120 121 121 123 125 128 132 133 138 141 145 146 148 155 158 160 160 161 162 162
188 190 191 194 196
xxii 6 CONTENTS humans_walk_batch Function Application Navigation/Travel Time Distances “Travel Distance” Data Format and Requirements travel_times Function Applications Closing Thoughts Next Steps Discussion Questions Replication Exercises References 200 206 207 208 213 213 214 214 215 Geographical Networks as Identification Tools Neighboring Structures and Networks What is a Network and How is it Different from or Similar to Neighboring Structures? From Distances (or Travel Times) to Networks and Neighboring Structures Point-Based Network and Neighboring Structures Identification Rules Radius-Based Approach Kth Closest Neighbor(s) Approach Inverse Distances From Neighboring Structures to Weights Code Application Moving Forward and Beyond These Standard Identification Approaches Crow Flies Versus Road Networks Distances Crow Flies Applying Road Networks Distances: “As Humans Walk” Using our Own Network Distances (and/or Travel Times) to Identify Neighboring Structures rad Function klosest Function rad_kth_row Function rad_kth_inv Function Moving Forward Identifying Neighboring Structures Among Different Types of Units Identifying the Local Presence of Units of Different Type Indirect Neighboring Structures Moving Forward and Feedback Loops Two-Mode Kth Closest Identificationand Selection Polygons and Matrices of Influence Rook’s Bishop’s Queen’s 217 218 219 220 221 221 223 224 225 227 229 230 230 231 233 235 237 238 239 240 241 241 247 254 256 264 264 265 266
CONTENTS Application Higher Order Neighbors Closing Thoughts Next Steps Discussion Questions Replication Exercises References ХХІІІ 266 269 271 273 273 274 275 Part III SSEM Hypothesis Testing of Cross-Sectional and Spatio-Temporal Data and Interactive Visualizations 7 SODA: Spatial Outcome Dependence or Autocorrelation SODA: Spatial Outcome Dependence or Autocorrelation Why Is SODA Statistically Concerning? Assessing SODA Based on Polygon Data Moran’s I Regression Approach Moran’s I Code Application with Polygon Data Is the First Order Neighboring Structure Enough? Assessing SODA Based on Point Data Machine Learning Tools to Assess SODA Decadence Moran’s I Code Application with One-Mode Point Data Data Source and Outcome of Interest Neighboring Structures and Weight Matrix Moran’s I Code Application with Two-Mode Point Data Two- To One-Mode Transformations and Rationale Causal Chains Through Spillovers in SSEM Local Moran’s I Visualizing Local Moran’s I Code Application Local Moran’s I Polygon Data Code Application Local Moran’s I One-Mode Point Data Code Application Local Moran’s I Two-Mode Point Data To Retain or Exclude Neighborless Units Code Application to Exclude Neighborless Units Social Outcome Dependence or Autocorrelation: SODA 2.0 Relationships in SODA 2.0 Application of SODA 2.0 Next Steps Discussion Questions Replication Exercises References 8 279 280 280 282 284 286 290 296 297 297 298 299 305 306 312 313 315 316 319 323 327 328 332 332 337 346 347 348 349 SSEM Regression Based Analyses 353 Residual SODA and the Importance of Spatial Regression Modeling SODA
Mechanisms in Regression Residuals Testing for RSODA 354 355 356
xxiv CONTENTS Simultaneous Autoregressive (SAR) Modeling Mechanisms and Implications of RSODA SAÆ Application to Polygon Data Assessing Whether RSODA was Handled Building a SAR Model While Addressing Place-based Multicollincarity Application of Feature Selection Via Random Forests Application Simultaneous Autoregressive Models SAR Application to Two-Mode Point Data Data Preparation and Transpormations Outcome Indicators and Feature Selection Rationale Two-mode to One-mode Transformations Feature Selection with Point Data Building SAR Model While Addressing Place-based Multicollinearity Multilevel SAR Models Multilevel Data Statistical Description of Multilevel SAR Multilevel SAR Function Application SAR or Multilevel SAR Testing for Spatial Heterogeneity Via Geographically Weighted Regression How Does SAR differ from GW Approaches? Distance and Travel Time Matrices and Kernel Functions Do we Need GW?: GW Multiscale Summary Statistics Geographically Weighted Regression and Visualization Spatio-Temporal SAR: A Difference in Differences Application Spatio-Temporal Data or Panel Data with SpatialInformation Testing for RSODA in Panel SAR SAR Panel Set Up SAR Panel Data Source and Setting Falsification Test Identification SAR Panel Application SAR Panel Function SAU and Multilevel SAR with Social Data Multilevel SAR Constrains for SODA 2.0 Social Multilevel SAR social_multilevel_SAR(...) Application Closing Thoughts and Next Steps Discussion Questions Replication Exercises References 359 359 361 362 365 366 370 373 373 375 176 380 380 384 385 387 392 401 401 403 404 406 413 420
421 422 422 424 425 426 427 433 434 435 436 440 441 442 444
CONTENTS 9 Visualization, Mining, and Density Analyses of Spatial and Spatio-Temporal Data SSEM Visualizations Polygon Data- Visualization poly_map (...) Function Application Exploratory Spatio-Temporal Data Mining and Visualization spatio_panel_visual (...) Implementation Point Data Visualization point_map (. ■ . ) Function Application Geospatial Point Density Methodological Approach What Questions May We Address with Geospatial Point Density ? Code Application for the Maps Next Steps in Gesopatial Point Density Geographical Network Visualizations Data Sources Preparation Rationale geographical_networks (... ) Application Two-Mode Networks Application One-Mode Geographical Networks Procedures Closing Thoughts Discussion Questions Replication Exercises References 10 Final Words References XXV 447 447 449 449 452 453 456 458 459 460 462 464 468 468 470 470 471 473 478 479 480 482 482 485 489 Glossary 491 Index 497
|
| adam_txt |
Contents Part I Conceptual and Theoretical Underpinnings 1 SPlaces SPlaces: Spaces, Places, and Spatial Socioeconometric Modeling Spaces Places SPlaces Inequality in Mobility Prospects Measuring Inequality and Growing Inequality Neighborhood Effects and Concentration of (dis)Advantages Spiace -Based Modeling Challenges Causality in Spatial Modeling Closing Thoughts and Next Steps Next Steps Discussion Questions References 3 3 4 5 7 9 13 14 17 17 20 20 20 21 2 Operationalizing SPlaces Delimiting and Operationalizing Neighborhoods as Splaces Representing Physical Spaces and Nesting Structures Zooming in Across Administrative Boundaries Shapefiles as Spaces Place-Based Indicators Contributing to Building Splaces Neighborhood Operationalization and Disaggregation Data Point Differentiation Across Neighborhood Levels Illustration of Splaces and Data Point Gains Tradeoffs of Data Point Differences Bringing Concepts, Shapefiles, and Place-Based Indicators Together 25 26 27 28 28 30 32 34 34 36 39 xix
CONTENTS XX ACS Published or Pre-tabulated Data Identifying Proxies for Poverty Identifying Proxies for Median Income Identifying Proxies for Unemployment Identifying Proxies for Housing Quality Identifying Proxies for Family Structure Closing Thoughts and Next Steps Next Steps Discussion Questions References Data Formats, Coordinate Reference Systems, and Differential Privacy Frameworks 55 Types of Geo-Referenced Data: Raster and Vector Data Raster Data Vector Data Vector to Raster Transformations andVice Versa Vector to Raster DataTransformations Moving From Raster to Vector Data Coordinate Reference Systems Elements of CRS Implications of Distortions Resulting fromMap Projections Commonly Used Coordinate ReferenceSystems Differential Privacy Framework (DPF) and Changes to Census Micro Data Are Differential Privacy and Synthetic Data the Same Privacy Protection Strategy? What are Differential Privacy Algorithms? Relevance of Differential Privacy For SSEM Strategies to Protect Privacy Methodological Implications of DPF for SSEM Next Steps Discussion Questions References 3 Part II 4 41 43 45 47 48 50 52 52 53 53 56 56 61 66 66 70 74 74 78 79 81 82 84 86 87 90 92 92 93 Data Science SSEM Identification Tools: Distances, Networks, and Neighbors Access and Management of Spatial or Geocoded Data -R Tutorial Installation R Infrastructure Code Rationale Reading Data from an External Source Creating Datasets from Within R Merging Joining Data 97 97 98 99 99 101 101 104
CONTENTS 5 ХХІ Installing Packages Reading Polygon Shapefiles Reading Polygons at the Country Level Reading Polygons at the County Level Reading Polygons at the ZIP Code Tabulated Area (ZCTA) Level Reading Polygons at the Census Tract Level Reading Polygons at the Block Group Level Reading Line Shapefiles All Roads Shapefiles Primary Roads Shapefiles Primary and Secondary Roads Shapefiles Reading Point Shapefiles Point Geocoding or Georeferencing Batch Geocoding Using Addresses in R Point Batch Geocoding Using ZCTAs in R Crosswalking Lower to Higher Level Crosswalking Place-Based Data Access at the Polygon Level Applying for a Census API Key Place-Based Data Access at the Point Level Joining Points with Polygons Data Closing Thoughts Next Steps Discussion Questions Replication Exercises References 106 109 109 113 Distances Distances Geolocated Data: Polygons, Points, or Bothi Why is Distance Estimation Relevant for SSEM? Data Source and Data Requirements Projections, Distortions, and Bias Concerns? Network Analysis Tools and Data Transformations Approaches to Distance Connections Identification Multiple Sources of Points Matrix to Edgelist Transformations As the Crow Flies Distance Calculations From a Matrix to a List of Connections (Edgelist) with Distances “As the Crow Flies” Distances Including Multiple Unit Types Network Route Distance Calculations: As Humans Walk As Humans Walk Distances Between Two Points Batch “As Humans Walk” Distances 165 165 166 167 169 172 173 173 175 178 183 116 118 120 121 121 123 125 128 132 133 138 141 145 146 148 155 158 160 160 161 162 162
188 190 191 194 196
xxii 6 CONTENTS humans_walk_batch Function Application Navigation/Travel Time Distances “Travel Distance” Data Format and Requirements travel_times Function Applications Closing Thoughts Next Steps Discussion Questions Replication Exercises References 200 206 207 208 213 213 214 214 215 Geographical Networks as Identification Tools Neighboring Structures and Networks What is a Network and How is it Different from or Similar to Neighboring Structures? From Distances (or Travel Times) to Networks and Neighboring Structures Point-Based Network and Neighboring Structures Identification Rules Radius-Based Approach Kth Closest Neighbor(s) Approach Inverse Distances From Neighboring Structures to Weights Code Application Moving Forward and Beyond These Standard Identification Approaches Crow Flies Versus Road Networks Distances Crow Flies Applying Road Networks Distances: “As Humans Walk” Using our Own Network Distances (and/or Travel Times) to Identify Neighboring Structures rad Function klosest Function rad_kth_row Function rad_kth_inv Function Moving Forward Identifying Neighboring Structures Among Different Types of Units Identifying the Local Presence of Units of Different Type Indirect Neighboring Structures Moving Forward and Feedback Loops Two-Mode Kth Closest Identificationand Selection Polygons and Matrices of Influence Rook’s Bishop’s Queen’s 217 218 219 220 221 221 223 224 225 227 229 230 230 231 233 235 237 238 239 240 241 241 247 254 256 264 264 265 266
CONTENTS Application Higher Order Neighbors Closing Thoughts Next Steps Discussion Questions Replication Exercises References ХХІІІ 266 269 271 273 273 274 275 Part III SSEM Hypothesis Testing of Cross-Sectional and Spatio-Temporal Data and Interactive Visualizations 7 SODA: Spatial Outcome Dependence or Autocorrelation SODA: Spatial Outcome Dependence or Autocorrelation Why Is SODA Statistically Concerning? Assessing SODA Based on Polygon Data Moran’s I Regression Approach Moran’s I Code Application with Polygon Data Is the First Order Neighboring Structure Enough? Assessing SODA Based on Point Data Machine Learning Tools to Assess SODA Decadence Moran’s I Code Application with One-Mode Point Data Data Source and Outcome of Interest Neighboring Structures and Weight Matrix Moran’s I Code Application with Two-Mode Point Data Two- To One-Mode Transformations and Rationale Causal Chains Through Spillovers in SSEM Local Moran’s I Visualizing Local Moran’s I Code Application Local Moran’s I Polygon Data Code Application Local Moran’s I One-Mode Point Data Code Application Local Moran’s I Two-Mode Point Data To Retain or Exclude Neighborless Units Code Application to Exclude Neighborless Units Social Outcome Dependence or Autocorrelation: SODA 2.0 Relationships in SODA 2.0 Application of SODA 2.0 Next Steps Discussion Questions Replication Exercises References 8 279 280 280 282 284 286 290 296 297 297 298 299 305 306 312 313 315 316 319 323 327 328 332 332 337 346 347 348 349 SSEM Regression Based Analyses 353 Residual SODA and the Importance of Spatial Regression Modeling SODA
Mechanisms in Regression Residuals Testing for RSODA 354 355 356
xxiv CONTENTS Simultaneous Autoregressive (SAR) Modeling Mechanisms and Implications of RSODA SAÆ Application to Polygon Data Assessing Whether RSODA was Handled Building a SAR Model While Addressing Place-based Multicollincarity Application of Feature Selection Via Random Forests Application Simultaneous Autoregressive Models SAR Application to Two-Mode Point Data Data Preparation and Transpormations Outcome Indicators and Feature Selection Rationale Two-mode to One-mode Transformations Feature Selection with Point Data Building SAR Model While Addressing Place-based Multicollinearity Multilevel SAR Models Multilevel Data Statistical Description of Multilevel SAR Multilevel SAR Function Application SAR or Multilevel SAR Testing for Spatial Heterogeneity Via Geographically Weighted Regression How Does SAR differ from GW Approaches? Distance and Travel Time Matrices and Kernel Functions Do we Need GW?: GW Multiscale Summary Statistics Geographically Weighted Regression and Visualization Spatio-Temporal SAR: A Difference in Differences Application Spatio-Temporal Data or Panel Data with SpatialInformation Testing for RSODA in Panel SAR SAR Panel Set Up SAR Panel Data Source and Setting Falsification Test Identification SAR Panel Application SAR Panel Function SAU and Multilevel SAR with Social Data Multilevel SAR Constrains for SODA 2.0 Social Multilevel SAR social_multilevel_SAR(.) Application Closing Thoughts and Next Steps Discussion Questions Replication Exercises References 359 359 361 362 365 366 370 373 373 375 176 380 380 384 385 387 392 401 401 403 404 406 413 420
421 422 422 424 425 426 427 433 434 435 436 440 441 442 444
CONTENTS 9 Visualization, Mining, and Density Analyses of Spatial and Spatio-Temporal Data SSEM Visualizations Polygon Data- Visualization poly_map (.) Function Application Exploratory Spatio-Temporal Data Mining and Visualization spatio_panel_visual (.) Implementation Point Data Visualization point_map (. ■ . ) Function Application Geospatial Point Density Methodological Approach What Questions May We Address with Geospatial Point Density ? Code Application for the Maps Next Steps in Gesopatial Point Density Geographical Network Visualizations Data Sources Preparation Rationale geographical_networks (. ) Application Two-Mode Networks Application One-Mode Geographical Networks Procedures Closing Thoughts Discussion Questions Replication Exercises References 10 Final Words References XXV 447 447 449 449 452 453 456 458 459 460 462 464 468 468 470 470 471 473 478 479 480 482 482 485 489 Glossary 491 Index 497 |
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| id | DE-604.BV049095016 |
| illustrated | Illustrated |
| index_date | 2024-07-03T22:30:54Z |
| indexdate | 2024-07-10T09:55:11Z |
| institution | BVB |
| isbn | 9783031248566 |
| language | English |
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| physical | xli, 503 Seiten Illustrationen, Diagramme 235 mm |
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| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in social sciences |
| spelling | González Canché, Manuel S. Verfasser (DE-588)1301158429 aut Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R Manuel S. González Canché Cham Springer [2023] xli, 503 Seiten Illustrationen, Diagramme 235 mm txt rdacontent n rdamedia nc rdacarrier Springer texts in social sciences bicssc bisacsh Sampling (Statistics) Computer simulation Machine learning Social sciences Sozialwissenschaften (DE-588)4055916-6 gnd rswk-swf Räumliche Statistik (DE-588)4386767-4 gnd rswk-swf Hardcover, Softcover / Sozialwissenschaften allgemein Sozialwissenschaften (DE-588)4055916-6 s Räumliche Statistik (DE-588)4386767-4 s DE-604 Erscheint auch als Online-Ausgabe 978-3-031-24857-3 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034356657&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | González Canché, Manuel S. Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R bicssc bisacsh Sampling (Statistics) Computer simulation Machine learning Social sciences Sozialwissenschaften (DE-588)4055916-6 gnd Räumliche Statistik (DE-588)4386767-4 gnd |
| subject_GND | (DE-588)4055916-6 (DE-588)4386767-4 |
| title | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R |
| title_auth | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R |
| title_exact_search | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R |
| title_exact_search_txtP | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R |
| title_full | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R Manuel S. González Canché |
| title_fullStr | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R Manuel S. González Canché |
| title_full_unstemmed | Spatial socio-econometric modeling (SSEM) a low-code toolkit for spatial data science and interactive visualizations using R Manuel S. González Canché |
| title_short | Spatial socio-econometric modeling (SSEM) |
| title_sort | spatial socio econometric modeling ssem a low code toolkit for spatial data science and interactive visualizations using r |
| title_sub | a low-code toolkit for spatial data science and interactive visualizations using R |
| topic | bicssc bisacsh Sampling (Statistics) Computer simulation Machine learning Social sciences Sozialwissenschaften (DE-588)4055916-6 gnd Räumliche Statistik (DE-588)4386767-4 gnd |
| topic_facet | bicssc bisacsh Sampling (Statistics) Computer simulation Machine learning Social sciences Sozialwissenschaften Räumliche Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034356657&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT gonzalezcanchemanuels spatialsocioeconometricmodelingssemalowcodetoolkitforspatialdatascienceandinteractivevisualizationsusingr |