An introduction to spatial data science with GeoDa: Volume 1: Exploring spatial data
Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- List of Figures -- Preface -- Acknowledgments -- About the Author -- 1. Introduction -- 1.1. Overview of Volume 1 -- 1.2. A Quick Tour of GeoDa -- 1.2.1. Data entry -- 1.2.2. Data manipulation -- 1.2.3. GIS operations -...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Francis Group
2024
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- List of Figures -- Preface -- Acknowledgments -- About the Author -- 1. Introduction -- 1.1. Overview of Volume 1 -- 1.2. A Quick Tour of GeoDa -- 1.2.1. Data entry -- 1.2.2. Data manipulation -- 1.2.3. GIS operations -- 1.2.4. Weights manager -- 1.2.5. Mapping and geovisualization -- 1.2.6. Exploratory data analysis -- 1.2.7. Space-time analysis -- 1.2.8. Spatial autocorrelation analysis -- 1.2.9. Cluster analysis -- 1.3. Sample Data Sets -- I. Spatial Data Wrangling -- 2. Basic Data Operations -- 2.1. Topics Covered -- 2.2. Spatial Data -- 2.2.1. GIS files -- 2.2.2. Tabular files -- 2.2.3. Other spatial data input -- 2.3. Creating Spatial Layers -- 2.3.1. Point layers from coordinates -- 2.3.2. Grid -- 2.4. Table Manipulations -- 2.4.1. Variable properties -- 2.4.2. Calculator -- 2.4.3. Merging tables -- 2.5. Queries -- 2.5.1. Selection Tool -- 2.5.2. Indicator variable -- 2.5.3. Save selected observations -- 2.5.4. Spatial selection -- 3. GIS Operations -- 3.1. Topics Covered -- 3.2. Projections -- 3.2.1. Coordinate reference system -- 3.2.2. Selecting a projection -- 3.2.3. Reprojection -- 3.3. Converting Between Points and Polygons -- 3.3.1. Mean centers and centroids -- 3.3.2. Tessellations -- 3.4. Minimum Spanning Tree -- 3.4.1. Concept -- 3.4.2. Minimum Spanning Tree options -- 3.5. Aggregation -- 3.5.1. Dissolve -- 3.5.2. Aggregation in table -- 3.6. Multi-Layer Support -- 3.6.1. Loading multiple layers -- 3.6.2. Automatic reprojection -- 3.6.3. Selection in multiple layers -- 3.7. Spatial Join -- 3.7.1. Spatial assign -- 3.7.2. Spatial count -- 3.8. Linked Multi-Layers -- 3.8.1. Specifying an inter-layer linkage -- 3.8.2. Visualizing linked selections -- II. EDA and ESDA -- 4. Geovisualization -- 4.1. Topics Covered -- 4.2. From EDA to ESDA. "This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration, to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods, by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa"-- "This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning. The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user's guide for these methods as implemented in the GeoDa open source software for spatial analysis. It is organized into three major parts, dealing with dimension reduction (principal components, multi-dimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods"-- |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (xxvii, 424 Seiten) Illustrationen |
| ISBN: | 9781040010907 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV050041795 | ||
| 003 | DE-604 | ||
| 007 | cr|uuu---uuuuu | ||
| 008 | 241125s2024 xx a||| o|||| 00||| eng d | ||
| 020 | |a 9781040010907 |9 978-1-040-01090-7 | ||
| 035 | |a (DE-599)KEP102458987 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-188 | ||
| 082 | 0 | |a 519.5/3 |2 23 | |
| 084 | |a RB 10104 |0 (DE-625)142220:12617 |2 rvk | ||
| 100 | 1 | |a Anselin, Luc |d 1953- |0 (DE-588)170116972 |4 aut | |
| 245 | 1 | 0 | |a An introduction to spatial data science with GeoDa |b Volume 1: Exploring spatial data |c Luc Anselin |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Francis Group |c 2024 | |
| 300 | |a 1 Online-Ressource (xxvii, 424 Seiten) |b Illustrationen | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b c |2 rdamedia | ||
| 338 | |b cr |2 rdacarrier | ||
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 520 | 3 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- List of Figures -- Preface -- Acknowledgments -- About the Author -- 1. Introduction -- 1.1. Overview of Volume 1 -- 1.2. A Quick Tour of GeoDa -- 1.2.1. Data entry -- 1.2.2. Data manipulation -- 1.2.3. GIS operations -- 1.2.4. Weights manager -- 1.2.5. Mapping and geovisualization -- 1.2.6. Exploratory data analysis -- 1.2.7. Space-time analysis -- 1.2.8. Spatial autocorrelation analysis -- 1.2.9. Cluster analysis -- 1.3. Sample Data Sets -- I. Spatial Data Wrangling -- 2. Basic Data Operations -- 2.1. Topics Covered -- 2.2. Spatial Data -- 2.2.1. GIS files -- 2.2.2. Tabular files -- 2.2.3. Other spatial data input -- 2.3. Creating Spatial Layers -- 2.3.1. Point layers from coordinates -- 2.3.2. Grid -- 2.4. Table Manipulations -- 2.4.1. Variable properties -- 2.4.2. Calculator -- 2.4.3. Merging tables -- 2.5. Queries -- 2.5.1. Selection Tool -- 2.5.2. Indicator variable -- 2.5.3. Save selected observations -- 2.5.4. Spatial selection -- 3. GIS Operations -- 3.1. Topics Covered -- 3.2. Projections -- 3.2.1. Coordinate reference system -- 3.2.2. Selecting a projection -- 3.2.3. Reprojection -- 3.3. Converting Between Points and Polygons -- 3.3.1. Mean centers and centroids -- 3.3.2. Tessellations -- 3.4. Minimum Spanning Tree -- 3.4.1. Concept -- 3.4.2. Minimum Spanning Tree options -- 3.5. Aggregation -- 3.5.1. Dissolve -- 3.5.2. Aggregation in table -- 3.6. Multi-Layer Support -- 3.6.1. Loading multiple layers -- 3.6.2. Automatic reprojection -- 3.6.3. Selection in multiple layers -- 3.7. Spatial Join -- 3.7.1. Spatial assign -- 3.7.2. Spatial count -- 3.8. Linked Multi-Layers -- 3.8.1. Specifying an inter-layer linkage -- 3.8.2. Visualizing linked selections -- II. EDA and ESDA -- 4. Geovisualization -- 4.1. Topics Covered -- 4.2. From EDA to ESDA. | |
| 520 | 3 | |a "This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration, to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods, by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa"-- | |
| 520 | 3 | |a "This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning. The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user's guide for these methods as implemented in the GeoDa open source software for spatial analysis. It is organized into three major parts, dealing with dimension reduction (principal components, multi-dimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods"-- | |
| 650 | 0 | 7 | |a Räumliches Datenbanksystem |0 (DE-588)4232580-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Raumdaten |0 (DE-588)4206012-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Geoinformationssystem |0 (DE-588)4261642-6 |2 gnd |9 rswk-swf |
| 653 | |a GeoDa (Computer file) | ||
| 653 | 0 | |a Spatial analysis (Statistics) | |
| 653 | 0 | |a Spatial analysis (Statistics) / Data processing | |
| 689 | 0 | 0 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | 1 | |a Geoinformationssystem |0 (DE-588)4261642-6 |D s |
| 689 | 0 | 2 | |a Raumdaten |0 (DE-588)4206012-6 |D s |
| 689 | 0 | 3 | |a Räumliches Datenbanksystem |0 (DE-588)4232580-8 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781032229188 |c (hbk.) |
| 776 | 0 | |z 9781032229621 |c (pbk.) | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781032229188 |
| 856 | 4 | 0 | |u https://lanselin.github.io/introbook_vol1/ |x Archivierung |z kostenfrei |3 Volltext |
| 912 | |a ZDB-30-PQE | ||
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035379516 | |
Datensatz im Suchindex
| _version_ | 1820889671048101888 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Anselin, Luc 1953- |
| author_GND | (DE-588)170116972 |
| author_facet | Anselin, Luc 1953- |
| author_role | aut |
| author_sort | Anselin, Luc 1953- |
| author_variant | l a la |
| building | Verbundindex |
| bvnumber | BV050041795 |
| classification_rvk | RB 10104 |
| collection | ZDB-30-PQE |
| ctrlnum | (DE-599)KEP102458987 |
| dewey-full | 519.5/3 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/3 |
| dewey-search | 519.5/3 |
| dewey-sort | 3519.5 13 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Geographie |
| edition | First edition |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV050041795</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">241125s2024 xx a||| o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781040010907</subfield><subfield code="9">978-1-040-01090-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP102458987</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-188</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">519.5/3</subfield><subfield code="2">23</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">RB 10104</subfield><subfield code="0">(DE-625)142220:12617</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Anselin, Luc</subfield><subfield code="d">1953-</subfield><subfield code="0">(DE-588)170116972</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">An introduction to spatial data science with GeoDa</subfield><subfield code="b">Volume 1: Exploring spatial data</subfield><subfield code="c">Luc Anselin</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton ; London ; New York</subfield><subfield code="b">CRC Press, Taylor & Francis Group</subfield><subfield code="c">2024</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xxvii, 424 Seiten)</subfield><subfield code="b">Illustrationen</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- List of Figures -- Preface -- Acknowledgments -- About the Author -- 1. Introduction -- 1.1. Overview of Volume 1 -- 1.2. A Quick Tour of GeoDa -- 1.2.1. Data entry -- 1.2.2. Data manipulation -- 1.2.3. GIS operations -- 1.2.4. Weights manager -- 1.2.5. Mapping and geovisualization -- 1.2.6. Exploratory data analysis -- 1.2.7. Space-time analysis -- 1.2.8. Spatial autocorrelation analysis -- 1.2.9. Cluster analysis -- 1.3. Sample Data Sets -- I. Spatial Data Wrangling -- 2. Basic Data Operations -- 2.1. Topics Covered -- 2.2. Spatial Data -- 2.2.1. GIS files -- 2.2.2. Tabular files -- 2.2.3. Other spatial data input -- 2.3. Creating Spatial Layers -- 2.3.1. Point layers from coordinates -- 2.3.2. Grid -- 2.4. Table Manipulations -- 2.4.1. Variable properties -- 2.4.2. Calculator -- 2.4.3. Merging tables -- 2.5. Queries -- 2.5.1. Selection Tool -- 2.5.2. Indicator variable -- 2.5.3. Save selected observations -- 2.5.4. Spatial selection -- 3. GIS Operations -- 3.1. Topics Covered -- 3.2. Projections -- 3.2.1. Coordinate reference system -- 3.2.2. Selecting a projection -- 3.2.3. Reprojection -- 3.3. Converting Between Points and Polygons -- 3.3.1. Mean centers and centroids -- 3.3.2. Tessellations -- 3.4. Minimum Spanning Tree -- 3.4.1. Concept -- 3.4.2. Minimum Spanning Tree options -- 3.5. Aggregation -- 3.5.1. Dissolve -- 3.5.2. Aggregation in table -- 3.6. Multi-Layer Support -- 3.6.1. Loading multiple layers -- 3.6.2. Automatic reprojection -- 3.6.3. Selection in multiple layers -- 3.7. Spatial Join -- 3.7.1. Spatial assign -- 3.7.2. Spatial count -- 3.8. Linked Multi-Layers -- 3.8.1. Specifying an inter-layer linkage -- 3.8.2. Visualizing linked selections -- II. EDA and ESDA -- 4. Geovisualization -- 4.1. Topics Covered -- 4.2. From EDA to ESDA.</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">"This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration, to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods, by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa"--</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">"This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning. The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user's guide for these methods as implemented in the GeoDa open source software for spatial analysis. It is organized into three major parts, dealing with dimension reduction (principal components, multi-dimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods"--</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Räumliches Datenbanksystem</subfield><subfield code="0">(DE-588)4232580-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Raumdaten</subfield><subfield code="0">(DE-588)4206012-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Geoinformationssystem</subfield><subfield code="0">(DE-588)4261642-6</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">GeoDa (Computer file)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Spatial analysis (Statistics)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Spatial analysis (Statistics) / Data processing</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Geoinformationssystem</subfield><subfield code="0">(DE-588)4261642-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Raumdaten</subfield><subfield code="0">(DE-588)4206012-6</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="3"><subfield code="a">Räumliches Datenbanksystem</subfield><subfield code="0">(DE-588)4232580-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781032229188</subfield><subfield code="c">(hbk.)</subfield></datafield><datafield tag="776" ind1="0" ind2=" "><subfield code="z">9781032229621</subfield><subfield code="c">(pbk.)</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781032229188</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://lanselin.github.io/introbook_vol1/</subfield><subfield code="x">Archivierung</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035379516</subfield></datafield></record></collection> |
| id | DE-604.BV050041795 |
| illustrated | Illustrated |
| indexdate | 2025-01-10T19:01:52Z |
| institution | BVB |
| isbn | 9781040010907 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035379516 |
| open_access_boolean | 1 |
| owner | DE-188 |
| owner_facet | DE-188 |
| physical | 1 Online-Ressource (xxvii, 424 Seiten) Illustrationen |
| psigel | ZDB-30-PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| spelling | Anselin, Luc 1953- (DE-588)170116972 aut An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data Luc Anselin First edition Boca Raton ; London ; New York CRC Press, Taylor & Francis Group 2024 1 Online-Ressource (xxvii, 424 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- List of Figures -- Preface -- Acknowledgments -- About the Author -- 1. Introduction -- 1.1. Overview of Volume 1 -- 1.2. A Quick Tour of GeoDa -- 1.2.1. Data entry -- 1.2.2. Data manipulation -- 1.2.3. GIS operations -- 1.2.4. Weights manager -- 1.2.5. Mapping and geovisualization -- 1.2.6. Exploratory data analysis -- 1.2.7. Space-time analysis -- 1.2.8. Spatial autocorrelation analysis -- 1.2.9. Cluster analysis -- 1.3. Sample Data Sets -- I. Spatial Data Wrangling -- 2. Basic Data Operations -- 2.1. Topics Covered -- 2.2. Spatial Data -- 2.2.1. GIS files -- 2.2.2. Tabular files -- 2.2.3. Other spatial data input -- 2.3. Creating Spatial Layers -- 2.3.1. Point layers from coordinates -- 2.3.2. Grid -- 2.4. Table Manipulations -- 2.4.1. Variable properties -- 2.4.2. Calculator -- 2.4.3. Merging tables -- 2.5. Queries -- 2.5.1. Selection Tool -- 2.5.2. Indicator variable -- 2.5.3. Save selected observations -- 2.5.4. Spatial selection -- 3. GIS Operations -- 3.1. Topics Covered -- 3.2. Projections -- 3.2.1. Coordinate reference system -- 3.2.2. Selecting a projection -- 3.2.3. Reprojection -- 3.3. Converting Between Points and Polygons -- 3.3.1. Mean centers and centroids -- 3.3.2. Tessellations -- 3.4. Minimum Spanning Tree -- 3.4.1. Concept -- 3.4.2. Minimum Spanning Tree options -- 3.5. Aggregation -- 3.5.1. Dissolve -- 3.5.2. Aggregation in table -- 3.6. Multi-Layer Support -- 3.6.1. Loading multiple layers -- 3.6.2. Automatic reprojection -- 3.6.3. Selection in multiple layers -- 3.7. Spatial Join -- 3.7.1. Spatial assign -- 3.7.2. Spatial count -- 3.8. Linked Multi-Layers -- 3.8.1. Specifying an inter-layer linkage -- 3.8.2. Visualizing linked selections -- II. EDA and ESDA -- 4. Geovisualization -- 4.1. Topics Covered -- 4.2. From EDA to ESDA. "This book is the first in a two-volume series that introduces the field of spatial data science. It offers an accessible overview of the methodology of exploratory spatial data analysis. It also constitutes the definitive user's guide for the widely adopted GeoDa open source software for spatial analysis. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques, using dynamic graphics for thematic mapping, statistical graphing, and, most centrally, the analysis of spatial autocorrelation. Key to this analysis is the concept of local indicators of spatial association, pioneered by the author and recently extended to the analysis of multivariate data. The focus of the book is on intuitive methods to discover interesting patterns in spatial data. It offers a progression from basic data manipulation through description and exploration, to the identification of clusters and outliers by means of local spatial autocorrelation analysis. A distinctive approach is to spatialize intrinsically non-spatial methods, by means of linking and brushing with a range of map representations, including several that are unique to the GeoDa software. The book also represents the most in-depth treatment of local spatial autocorrelation and its visualization and interpretation by means of GeoDa"-- "This book is the second in a two-volume series that introduces the field of spatial data science. It moves beyond pure data exploration to the organization of observations into meaningful groups, i.e., spatial clustering. This constitutes an important component of so-called unsupervised learning, a major aspect of modern machine learning. The distinctive aspects of the book are both to explore ways to spatialize classic clustering methods through linked maps and graphs, as well as the explicit introduction of spatial contiguity constraints into clustering algorithms. Leveraging a large number of real-world empirical illustrations, readers will gain an understanding of the main concepts and techniques and their relative advantages and disadvantages. The book also constitutes the definitive user's guide for these methods as implemented in the GeoDa open source software for spatial analysis. It is organized into three major parts, dealing with dimension reduction (principal components, multi-dimensional scaling, stochastic network embedding), classic clustering methods (hierarchical clustering, k-means, k-medians, k-medoids and spectral clustering), and spatially constrained clustering methods (both hierarchical and partitioning). It closes with an assessment of spatial and non-spatial cluster properties. The book is intended for readers interested in going beyond simple mapping of geographical data to gain insight into interesting patterns as expressed in spatial clusters of observations. Familiarity with the material in Volume 1 is assumed, especially the analysis of local spatial autocorrelation and the full range of visualization methods"-- Räumliches Datenbanksystem (DE-588)4232580-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Raumdaten (DE-588)4206012-6 gnd rswk-swf Geoinformationssystem (DE-588)4261642-6 gnd rswk-swf GeoDa (Computer file) Spatial analysis (Statistics) Spatial analysis (Statistics) / Data processing Data Mining (DE-588)4428654-5 s Geoinformationssystem (DE-588)4261642-6 s Raumdaten (DE-588)4206012-6 s Räumliches Datenbanksystem (DE-588)4232580-8 s DE-604 Erscheint auch als Druck-Ausgabe 9781032229188 (hbk.) 9781032229621 (pbk.) Erscheint auch als Druck-Ausgabe 9781032229188 https://lanselin.github.io/introbook_vol1/ Archivierung kostenfrei Volltext |
| spellingShingle | Anselin, Luc 1953- An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data Räumliches Datenbanksystem (DE-588)4232580-8 gnd Data Mining (DE-588)4428654-5 gnd Raumdaten (DE-588)4206012-6 gnd Geoinformationssystem (DE-588)4261642-6 gnd |
| subject_GND | (DE-588)4232580-8 (DE-588)4428654-5 (DE-588)4206012-6 (DE-588)4261642-6 |
| title | An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data |
| title_auth | An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data |
| title_exact_search | An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data |
| title_full | An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data Luc Anselin |
| title_fullStr | An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data Luc Anselin |
| title_full_unstemmed | An introduction to spatial data science with GeoDa Volume 1: Exploring spatial data Luc Anselin |
| title_short | An introduction to spatial data science with GeoDa |
| title_sort | an introduction to spatial data science with geoda volume 1 exploring spatial data |
| title_sub | Volume 1: Exploring spatial data |
| topic | Räumliches Datenbanksystem (DE-588)4232580-8 gnd Data Mining (DE-588)4428654-5 gnd Raumdaten (DE-588)4206012-6 gnd Geoinformationssystem (DE-588)4261642-6 gnd |
| topic_facet | Räumliches Datenbanksystem Data Mining Raumdaten Geoinformationssystem |
| url | https://lanselin.github.io/introbook_vol1/ |
| work_keys_str_mv | AT anselinluc anintroductiontospatialdatasciencewithgeodavolume1exploringspatialdata |