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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Introduction to GIS01:28

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Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Why geographic data science is not a science.

Simon Scheider1, Enkhbold Nyamsuren1, Han Kruiger1

  • 1Department of Human Geography and Spatial Planning Utrecht University The Netherlands.

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|December 31, 2020
PubMed
Summary
This summary is machine-generated.

Data Science and Geographic Data Science (GDS) are currently viewed as interdisciplinary communities of practice, not distinct scientific disciplines. Further development is needed for them to achieve genuine disciplinary status.

Keywords:
GIScienceGeographycommunity of practicegeographic data sciencemeta‐sciencescientific conceptsscientific questions

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Area of Science:

  • Meta-science
  • Data Science
  • Geographic Data Science

Background:

  • Data Science is increasingly influencing scientific fields, leading to new research methods and questions.
  • Specialized branches like Geographic Data Science (GDS) have emerged, prompting debate about their disciplinary status.

Purpose of the Study:

  • To analyze Data Science and GDS from a meta-scientific perspective.
  • To critically evaluate claims of Data Science and GDS as autonomous scientific disciplines.

Main Methods:

  • Meta-scientific inquiry into the nature of Data Science and GDS.
  • Analysis of current research practices and academic structures.

Main Results:

  • Data Science and GDS are currently best characterized as interdisciplinary communities of practice.
  • They involve data-driven scientists collaborating across traditional boundaries.

Conclusions:

  • Data Science and GDS are not yet autonomous scientific disciplines.
  • Key elements are missing for their formal recognition as distinct fields of study.