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Books on the topic 'Geospatial maps'

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Author: Grafiati
Published: 7 July 2024

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1

Ohio Geographically Referenced Information Program., ed. Contacts for geospatial data sets of Ohio, 1997. Columbus: Ohio Geographically Referenced Information Program, 1997.

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2

Baker, Nancy T. National Stream Quality Accounting Network and National Monitoring Network Basin Boundary Geospatial Dataset, 2008-13. Reston, Va: U.S. Dept. of the Interior, U.S. Geological Survey, 2011.

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3

Stefanakis, Emmanuel. An introduction to web mapping & geospatial web services. North Charleston: CreateSpace Independent Publishing Platform, 2015.

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4

Rogerson, Peter. Statistical detection and surveillance of geographic clusters. Boca Raton: Chapman & Hall/CRC, 2009.

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5

Workshop National Geospatial Data Infrastructure: Towards a Road Map for India (2001 New Delhi, India). Workshop National Geospatial Data Infrastructure: Towards a Road Map for India, 5-6 February 2001, New Delhi India. Noida: Centre for Spatial Database Management and Solutions, 2001.

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6

Foody, Giles, Peter Mooney, Cidália Costa Fonte, Ana Maria Olteanu Raimond, Steffen Fritz, and Linda See, eds. Mapping and the Citizen Sensor. London, United Kingdom: Ubiquity Press, 2017.

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7

Anthamatten, Peter. How to Make Maps. Taylor & Francis Group, 2020.

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8

Anthamatten, Peter. How to Make Maps. Taylor & Francis Group, 2020.

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9

Anthamatten, Peter. How to Make Maps: An Introduction to Theory and Practice of Cartography. Taylor & Francis Group, 2020.

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10

Anthamatten, Peter. How to Make Maps: An Introduction to Theory and Practice of Cartography. Taylor & Francis Group, 2020.

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11

Anthamatten, Peter. How to Make Maps: An Introduction to Theory and Practice of Cartography. Taylor & Francis Group, 2020.

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12

Rischpater, Ray, and Carmen Au. Microsoft Mapping: Geospatial Development with Bing Maps and C#. Apress L. P., 2013.

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13

Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, and Board on Mathematical Sciences and Their Applications. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. National Academies Press, 2016.

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14

Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, and Board on Mathematical Sciences and Their Applications. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. National Academies Press, 2016.

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15

Board on Atmospheric Sciences and Climate, Division on Earth and Life Studies, National Academies of Sciences, Engineering, and Medicine, Division on Engineering and Physical Sciences, and Board on Mathematical Sciences and Their Applications. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. National Academies Press, 2016.

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16

Moraga, Paula. Geospatial Health Data. Taylor & Francis Group, 2019.

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17

Rischpater, Ray. Microsoft Mapping: Geospatial Development with Bing Maps and C# (Expert's Voice in Microsoft). Apress, 2013.

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18

Nackoney, Janet, Jena Hickey, David Williams, Charly Facheux, Takeshi Furuichi, and Jef Dupain. Geospatial information informs bonobo conservation efforts. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198728511.003.0017.

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Abstract:
The endangered bonobo (Pan paniscus), endemic to the Democratic Republic of Congo (DRC), is threatened by hunting and habitat loss. Two recent wars and ongoing conflicts in the DRC greatly challenge conservation efforts. This chapter demonstrates how spatial data and maps are used for monitoring threats and prioritizing locations to safeguard bonobo habitat, including identifying areas of highest conservation value to bonobos and collaboratively mapping community-based natural resource management (CBNRM) zones for reducing deforestation in key corridor areas. We also highlight the development of a range-wide model that analysed a variety of biotic and abiotic variables in conjunction with bonobo nest data to map suitable habitat. Approximately 28 per cent of the range was predicted suitable; of that, about 27.5 per cent was located in official protected areas. These examples highlight the importance of employing spatial data and models to support the development of dynamic conservation strategies that will help strengthen bonobo protection. Le bonobo en voie de disparition (Pan paniscus), endémique à la République Démocratique du Congo (DRC), est menacé par la chasse et la perte de l’habitat. Deux guerres récentes et les conflits en cours dans le DRC menacent les efforts de conservation. Ici, nous montrons comment les données spatiales et les cartes sont utilisées pour surveiller les menaces et prioriser les espaces pour protéger l’habitat bonobo, inclut identifier les zones de plus haute valeur de conservation aux bonobos. En plus, la déforestation est réduite par une cartographie collaborative communale de gestion de ressources dans les zones de couloirs essentiels. Nous soulignons le développement d’un modèle de toute la gamme qui a analysé un variété de variables biotiques et abiotiques en conjonction avec les données de nid bonobo pour tracer la carte d’un habitat adéquat. Environ 28 per cent de la gamme est prédit adéquat; de cela, environ 27.5 per cent est dans une zone officiellement protégée. Ces exemples soulignent l’importance d’utiliser les données spatiales et les modèles pour soutenir le développement de stratégies de conservations dynamiques qui aideront à renforcer la protection des bonobos.
19

Russian military mapping: A guide to using the most comprehensive source of global geospatial intelligence. Minneapolis, Minn: East View Cartographic, 2005.

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20

Rischpater, Ray, and Carmen Au. Microsoft Mapping Second Edition: Geospatial Development in Windows 10 with Bing Maps and C#. Apress, 2015.

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21

Graser, Anita. Learning QGIS: Use QGIS to create great maps and perform all the geoprocessing tasks you need. 2014.

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22

Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Taylor & Francis Group, 2019.

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23

Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Taylor & Francis Group, 2019.

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24

Moraga, Paula. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny. Taylor & Francis Group, 2019.

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25

Corbin, Tripp. Learning ArcGIS Pro 2: A Beginner's Guide to Creating 2D and 3D Maps and Editing Geospatial Data with ArcGIS Pro, 2nd Edition. Packt Publishing, Limited, 2020.

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26

Rogerson, Peter, and Ikuho Yamada. Statistical Detection and Surveillance of Geographic Clusters. Taylor & Francis Group, 2009.

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27

Rogerson, Peter, and Ikuho Yamada. Statistical Detection and Monitoring of Geographic Clusters. Chapman & Hall/CRC, 2008.

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28

Rogerson, Peter, and Ikuho Yamada. Statistical Detection and Surveillance of Geographic Clusters. Taylor & Francis Group, 2008.

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29

Rogerson, Peter, and Ikuho Yamada. Statistical Detection and Surveillance of Geographic Clusters. Taylor & Francis Group, 2008.

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30

Kitchin, Rob, Matthew W. Wilson, and Tracey P. Lauriault. Understanding Spatial Media. SAGE Publications, Limited, 2017.

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31

Mooney, Peter, Giles Foody, and Linda See. Mapping and the Citizen Sensor. Saint Philip Street Press, 2020.

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