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Python Across Disciplines
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Table of Contents

1.1.   Introduction 1.2.   About the Author & Contact Info 1.3.   Book Conventions 1.4.   What (Who) is a Programmer? 1.5.   Programming Across Disciplines 1.6.   Foundational Computing Concepts 1.7.   About Python 1.8.   First Steps 1.8.1 Computer Setup 1.8.2 Python print() Function 1.8.3 Comments
2.1. About Data 2.2. Data Types 2.3. Variables 2.4. User Input 2.5. Data Structures (DS)         2.5.1. DS Concepts         2.5.2. Lists         2.5.3. Dictionaries         2.5.4. Others 2.6. Files         2.6.1. Files & File Systems         2.6.2. Python File Object         2.6.3. Data Files 2.7. Databases
3.1. About Processing 3.2. Decisions         3.2.1 Decision Concepts         3.2.2 Conditions & Booleans         3.2.3 if Statements         3.2.4 if-else Statements         3.2.5 if-elif-else Statements         3.2.6 In-Line if Statements 3.3. Repetition (a.k.a. Loops)         3.3.1  Repetition Concepts         3.3.2  while Loops         3.3.3  for Loops         3.3.4  Nested Loops         3.3.5  Validating User Input 3.4. Functions         3.4.1  Function Concepts         3.4.2  Built-In Functions         3.4.3  Programmer Defined Functions 3.5. Libraries         3.5.1  Library Concepts         3.5.2  Standard Library         3.5.3  External Libraries 3.6. Processing Case Studies         3.6.1  Case Studies         3.6.2  Parsing Data
4.1. About Output 4.2. Advanced Printing 4.3. Data Visualization   4.4  Sound
  4.5  Graphics
  4.6  Video
  4.7  Web Output
  4.8  PDFs & Documents
  4.9  Dashboards
  4.10  Animation & Games
  4.11  Text to Speech

5.1 About Disciplines 5.2 Accounting 5.3 Architecture 5.4 Art 5.5 Artificial Intelligence (AI) 5.6 Autonomous Vehicles 5.7 Bioinformatics 5.8 Biology 5.9 Bitcoin 5.10 Blockchain 5.11 Business 5.12 Business Analytics 5.13 Chemistry 5.14 Communication 5.15 Computational Photography 5.16 Computer Science 5.17 Creative Writing 5.18 Cryptocurrency 5.19 Cultural Studies 5.20 Data Analytics 5.21 Data Engineering 5.22 Data Science 5.23 Data Visualization 5.24 Drone Piloting 5.25 Economics 5.26 Education 5.27 Engineering 5.28 English 5.29 Entrepreneurship 5.30 Environmental Studies 5.31 Exercise Science 5.32 Film 5.33 Finance 5.34 Gaming 5.35 Gender Studies 5.36 Genetics 5.37 Geography 5.38 Geology 5.39 Geospatial Analysis ☯ 5.40 History 5.41 Humanities 5.42 Information Systems 5.43 Languages 5.44 Law 5.45 Linguistics 5.46 Literature 5.47 Machine Learning 5.48 Management 5.49 Marketing 5.50 Mathematics 5.51 Medicine 5.52 Military 5.53 Model Railroading 5.54 Music 5.55 Natural Language Processing (NLP) 5.56 Network Analysis 5.57 Neural Networks 5.58 Neurology 5.59 Nursing 5.60 Pharmacology 5.61 Philosophy 5.62 Physiology 5.63 Politics 5.64 Psychiatry 5.65 Psychology 5.66 Real Estate 5.67 Recreation 5.68 Remote Control (RC) Vehicles 5.69 Rhetoric 5.70 Science 5.71 Sociology 5.72 Sports 5.73 Stock Trading 5.74 Text Mining 5.75 Weather 5.76 Writing
6.1. Databases         6.1.1 Overview of Databases         6.1.2 SQLite Databases         6.1.3 Querying a SQLite Database         6.1.4 CRUD Operations with SQLite         6.1.5 Connecting to Other Databases
Built-In Functions Conceptss Data Types Date & Time Format Codes Dictionary Methods Escape Sequences File Access Modes File Object Methods Python Keywords List Methods Operators Set Methods String Methods Tuple Methods Glossary Index Appendices   Software Install & Setup
  Coding Tools:
  A.  Python ↗   B.  Google CoLaboratory ↗   C.  Visual Studio Code ↗   D.  PyCharm IDE ↗   E.  Git ↗   F.  GitHub ↗
  Database Tools:
  G.  SQLite Database ↗   H.  MySQL Database ↗


Python Across Disciplines
by John Gordon © 2023
Table of Contents
1.1.   Introduction 1.2.   About the Author & Contact Info 1.3.   Book Conventions 1.4.   What (Who) is a Programmer? 1.5.   Programming Across Disciplines 1.6.   Foundational Computing Concepts 1.7.   About Python 1.8.   First Steps         1.8.1. Computer Setup         1.8.2. Python print() Function         1.8.3. Comments
2.1. About Data 2.2. Data Types         2.2.1. Variables         2.2.2. Strings         2.2.3. Integers         2.2.4. Floating Points         2.2.5. Booleans 2.3. User Input 2.5. Data Structures (DS)         2.5.1. Introduction to DS         2.5.2. Lists         2.5.3. Dictionaries         2.5.4. Others     2.6. Files         2.6.1. Files & File Systems         2.6.2. Python File Object         2.6.3. Data Files 2.7. Databases
3.1. About Processing     3.2. Decisions         3.2.1 Decision Concepts         3.2.2 Conditions & Booleans         3.2.3 if Statements         3.2.4 if-else Statements         3.2.5 if-elif-else Statements         3.2.6 In-Line if Statements     3.3. Repetition (a.k.a. Loops)         3.3.1  Repetition Concepts         3.3.2  while Loops         3.3.3  for Loops         3.3.4  Nested Loops         3.3.5  Validating User Input     3.4. Functions         3.4.1  Function Concepts         3.4.2  Built-In Functions         3.4.3  Programmer Defined Functions     3.5. Libraries         3.5.1  Library Concepts         3.5.2  Standard Library         3.5.3  External Libraries     3.6. Processing Case Studies         3.6.1  Case Studies         3.6.2  Parsing Data             3.6.3  eCommerce Simulation         3.6.4  Datasets         3.6.5  Web Scraping             3.6.6  Named Entity Reconition         3.6.7  Automation
4.1. About Output 4.2. Advanced Printing 4.3. Data Visualization     4.4  Sound
    4.5  Graphics
    4.6  Video
    4.7  Web Output
    4.8  PDFs & Documents
    4.9  Dashboards
    4.10  Animation & Games
    4.11  Text to Speech

5.1 Disciplinarity 5.2 Accounting 5.3 Architecture 5.4 Art 5.5 Artificial Intelligence (AI) 5.6 Autonomous Vehicles 5.7 Bioinformatics 5.8 Biology 5.9 Bitcoin 5.10 Blockchain 5.11 Business 5.12 Business Analytics 5.13 Chemistry 5.14 Communication 5.15 Computational Photography 5.16 Computer Science 5.17 Creative Writing 5.18 Cryptocurrency 5.19 Cultural Studies 5.20 Data Analytics 5.21 Data Engineering 5.22 Data Science 5.23 Data Visualization 5.24 Drone Piloting 5.25 Economics 5.26 Education 5.27 Engineering 5.28 English 5.29 Entrepreneurship 5.30 Environmental Studies 5.31 Exercise Science 5.32 Film 5.33 Finance 5.34 Gaming 5.35 Gender Studies 5.36 Genetics 5.37 Geography 5.38 Geology 5.39 Geospatial Analysis       5.39.1 Geospatial Concepts       5.39.2 Geospatial in the Humanities       5.39.3 Geospatial Project Ideas       5.39.4 Geospatial Data Sources 5.40 History 5.41 Humanities 5.42 Information Systems 5.43 Languages 5.44 Law 5.45 Linguistics 5.46 Literature 5.47 Machine Learning 5.48 Management 5.49 Marketing 5.50 Mathematics 5.51 Medicine 5.52 Military 5.53 Model Railroading 5.54 Music 5.55 Natural Language Processing 5.56 Network Analysis 5.57 Neural Networks 5.58 Neurology 5.59 Nursing 5.60 Pharmacology 5.61 Philosophy 5.62 Physiology 5.63 Politics 5.64 Psychiatry 5.65 Psychology 5.66 Real Estate 5.67 Recreation 5.68 Remote Control (RC) Vehicles 5.69 Rhetoric 5.70 Science 5.71 Sociology 5.72 Sports 5.73 Stock Trading 5.74 Text Mining 5.75 Weather 5.76 Writing
6.0 Chapter Overview     6.1. Databases
        6.1.1 Overview of Databases         6.1.2 SQLite Databases         6.1.3 Querying a SQLite Database         6.1.4 CRUD Operations w/SQLite         6.1.5 Connecting to Other Databases     6.2. Object Oriented Programming
            6.2.1. Introduction to OOP             6.2.2. Unified Modeling Language             6.2.3. Classes & Objects             6.2.4. Abstraction             6.2.5. Encapsulation             6.2.6. Inheritance             6.2.7. Polymorphism             6.2.8. Exception Handling             6.2.9. Codealong: Employees             6.2.10. Codealong: Cars     6.3. Graphical User Interfaces
            6.3.1. Introduction to GUIs
            6.3.2. Tkinter
            6.3.3. Codealong: Login Form
            6.3.4. Codealong: Data Entry Form
    6.4. Algorithms & Data Structures
            6.4.1. Introduction to Algorithms
            6.4.2. Searching
            6.4.3. Sorting
            6.4.4. Metrics
            6.4.5. Nodes
            6.4.6. Linked Lists 1
            6.4.7. Linked Lists 2
            6.4.8. Stacks
            6.4.9. Queues
            6.4.10. Heaps
            6.4.11. Trees
            6.4.12. Graphs
            6.4.13. Maps
            6.4.14. Hashing
            6.4.15. Cryptography
    6.5. Additional Advanced Topics
            6.5.1. APIs
            6.5.2. Automation
            6.5.3. Recursion
            6.5.4. More About Datasets
            6.5.5. Large Language Models
            6.5.6. Text Analysis
            6.5.7. Web Scraping
            6.5.8. Data Science
            6.5.9. Machline Learning
            6.5.10. Natural Lang Processing
            6.5.11. Artificial Intelligence
            6.5.12. Cython
            6.5.13. PyPy
            6.5.14. Multi-Threading
            6.5.15. Parallel Processing

Built-In Functions ↗ Concepts: Alphabetical ↗ Concepts: By Chapter ↗ Data Types ↗ Date & Time Format Codes ↗ Dictionary Methods ↗ Escape Sequences ↗ File Access Modes ↗ File Object Methods ↗ Python Keywords ↗ List Methods ↗ Operators ↗ Set Methods ↗ String Formatting Types ↗ String Methods ↗ Tuple Methods ↗
  Software Install & Setup

  Coding Tools:
  A.  Python ↗   B.  Google CoLaboratory ↗   C.  Visual Studio Code ↗   D.  PyCharm IDE ↗   E.  Git ↗   F.  GitHub ↗
  Database Tools:
  G.  SQLite Database ↗   H.  MySQL Database ↗



Table of Contents  »  Chapter 5 : Disciplines : Geospatial Analysis : Data Sources

Geospatial Data Sources

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Contents

Overview

Accessing geospatial data is a fundamental step in conducting spatial analysis and geographic research. Various organizations and government entities provide free geospatial data, catering to a wide range of disciplines from environmental monitoring to urban planning. Locating the specific data you need for a project can be time consuming, but well worth it once it is located. I have constructed the following source catalog to provide a starting place for exploring and locating relevant data sources.


Humanitarian Data Exchange

NASA Earth Data

WorldPop

Sentinel Hub



Resource Watch

African Biodiversity Network

Open Street Map

Natural Earth

Geospatial Data Source Catalog

  • African Biodiversity Network (ABN): Website: ABN Description: Offers data relevant to the conservation and use of Africa’s biodiversity.
  • Atlas of Canada: Website: Atlas of Canada Description: Provides various maps and related information about Canada.
  • Australian Government Geoscience Portal: Website: Geoscience Australia Description: Offers a wide range of geoscientific data and information about Australia.
  • CGIAR-CSI: Website: CGIAR Consortium for Spatial Information Description: Provides a variety of agricultural, environmental, and climate-related geospatial datasets.
  • DIVA-GIS: Website: DIVA-GIS Description: DIVA-GIS specializes in providing free geographic data, particularly useful for biodiversity research, conservation, and mapping of species distributions.
  • European Environment Agency (EEA): Website: EEA Description: Offers data sets related to environmental monitoring and assessment in Europe.
  • European Space Agency (ESA) - Copernicus Open Access Hub: Website: Copernicus Open Access Hub Description: This portal provides access to data from the Copernicus satellites and missions, offering a range of environmental and earth observation datasets.
  • FAO GeoNetwork: Website: Food and Agriculture Organization of the United Nations Description: Provides a variety of datasets related to food and agriculture globally.
  • Geofabrik: Website: Geofabrik Description: Regularly updated extracts from the OpenStreetMap database, available for free download.
  • GeoGratis (Canada): Website: GeoGratis Description: A portal provided by the Canadian government offering a variety of geospatial datasets.
  • Global Administrative Areas (GADM): Website: GADM Description: GADM provides datasets of boundaries for all countries worldwide, down to their lowest administrative level (e.g., states, provinces, departments).
  • Global Land Cover Facility: Website: GLCF Description: Provides earth science data and products, focusing on satellite imagery and land cover.
  • Humanitarian Data Exchange (HDX): Website: Humanitarian Data Exchange Description: Managed by the United Nations Office for the Coordination of Humanitarian Affairs, HDX contains datasets relevant to humanitarian crises worldwide, including geographic data.
  • INPE (Brazilian National Institute for Space Research): Website: INPE Description: Offers satellite imagery and datasets related to the Brazilian territory, focusing on deforestation and environmental changes.
  • LandScan: Website: LandScan Global Population Database Description: Hosted by the Oak Ridge National Laboratory, this database offers global population distribution data.
  • LINZ Data Service: Website: Land Information New Zealand (LINZ) Description: Provides New Zealand's geospatial data, including land and sea maps.
  • NASA Earth Data: Website: NASA Earth Observing System Data and Information System (EOSDIS) Description: NASA provides a wide variety of earth science data through this portal, covering atmospheric science, oceanography, land vegetation, and more.
  • National Oceanic and Atmospheric Administration (NOAA): Website: NOAA Data Tools Description: NOAA provides datasets related to meteorology, oceanography, marine habitats, and much more, essential for environmental and climate research.
  • Natural Earth: Website: Natural Earth Description: Natural Earth offers free vector and raster dataset downloads, ideal for making cartographic visualizations. The data is at different scales and includes both cultural and physical features.
  • OpenStreetMap (OSM): Website: OpenStreetMap Description: OpenStreetMap is a collaborative project that creates a free editable map of the world. The data from OSM is freely available for download and represents various geographic data contributed by people worldwide.
  • OpenTopography: Website: OpenTopography Description: A platform providing access to high-resolution topographic data and tools.
  • Protectedplanet: Website: Protectedplanet Description: Offers data on protected areas worldwide, including national parks, nature reserves, and conservation regions.
  • Sentinel Hub: Website: Sentinel Hub Description: Offers data from the European Union's Copernicus Programme, including environmental and earth observation data.
  • UNEP Environmental Data Explorer: Website: UNEP Description: The United Nations Environment Programme offers data on themes like climate change, water, and policy.
  • US Census Bureau: Website: US Census Bureau GIS Data Description: The Census Bureau provides spatial data related to demographic and economic statistics, including boundaries, census tracts, and more.
  • US Geological Survey (USGS): Website: USGS EarthExplorer Description: The USGS EarthExplorer provides a wealth of data from the US Geological Survey, including satellite imagery, maps, and various datasets related to geology, climate, geography, and more.
  • USDA National Agricultural Statistics Service: Website: USDA NASS Description: Offers agricultural data from the United States, including crop boundaries, agricultural practices, and production.
  • World Bank - Data Catalog: Website: The World Bank Description: Includes a wide variety of datasets, with many relevant to economic and social geospatial analysis.
  • World Pop: Website: WorldPop Description: Provides population data for low and middle-income countries.
  • World Resources Institute (WRI): Website: WRI Resource Watch Description: Provides various global datasets, focusing primarily on sustainability and the environment.


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© 2023 John Gordon
Cascade Street Publishing, LLC