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Exposing the urban continuum: Implications and cross-comparison from an interdisciplinary perspective.

Johannes H Uhl1, Hamidreza Zoraghein2, Stefan Leyk1

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Comparing geospatial data for human settlement analysis reveals varying accuracy. This study cross-validates remote sensing, census, and cadastral data to model urban, peri-urban, and rural areas, informing future data use.

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Area of Science:

  • Geoinformatics and Geospatial Analysis
  • Urban Studies and Planning
  • Remote Sensing and Earth Observation

Background:

  • Increasing availability of diverse geospatial data (remote sensing, census, cadastral) offers new opportunities for modeling human settlement patterns.
  • Limited research exists on the agreement and comparability of these different data products in measuring human presence.
  • Understanding data discrepancies is crucial for accurate characterization of the urban-rural continuum.

Purpose of the Study:

  • To quantitatively evaluate and cross-compare the ability of different geospatial data types to model the urban continuum.
  • To assess the agreement between remote sensing-based built-up land layers, census-derived population estimates, and cadastral data.
  • To identify data advantages and shortcomings across various geographic settings in the U.S.

Main Methods:

  • Utilized an integrated validation database comprising U.S. census data, cadastral and building footprint data, and three versions of the Global Human Settlement Layer (GHSL).
  • Performed quantitative cross-comparison of data products to assess their performance in modeling human presence and settlement patterns.
  • Analyzed data agreement across different geographic settings within the United States.

Main Results:

  • Identified specific advantages and limitations of remote sensing, census, and cadastral data for modeling human settlement.
  • Quantified the agreement and discrepancies between various data sources when measuring human presence.
  • Highlighted the influence of geographic setting on the accuracy and suitability of different data types.

Conclusions:

  • Findings inform future data users about the implications of data accuracy and suitability for specific applications, even in data-poor regions.
  • The study provides critical insights into selecting appropriate geospatial data for urban continuum modeling.
  • Cross-validation is essential for reliable characterization of human settlements using diverse data sources.