License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GISCIENCE.2018.70
URN: urn:nbn:de:0030-drops-93983
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2018/9398/
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Yen, Chi-Feng ; Tsou, Ming-Hsiang ; Allen, Chris

Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper)

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LIPIcs-GISCIENCE-2018-70.pdf (0.4 MB)

Abstract

Traditionally, understanding urban neighborhood conditions heavily relies on time-consuming and labor-intensive surveying. As the growing development of computer vision and GIScience technology, neighborhood conditions assessment can be more cost-effective and time-efficient. This study utilized Google Earth Engine (GEE) to acquire 1m aerial imagery from the National Agriculture Image Program (NAIP). The features within two main categories: (i) aesthetics and (ii) street morphology that have been selected to reflect neighborhood socio-economic (SE) and demographic (DG) conditions were subsequently extracted through geographic object-based image analysis (GEOBIA) routine. Finally, coefficient analysis was performed to validate the relationship between selected SE indicators, generated via spatial analysis, as well as actual SE and DG data within region of interests (ROIs). We hope this pilot study can be leveraged to perform cost- and time- effective neighborhood conditions assessment in support of community data assessment on both demographics and health issues.

BibTeX - Entry

@InProceedings{yen_et_al:LIPIcs:2018:9398,
  author =	{Chi-Feng Yen and Ming-Hsiang Tsou and Chris Allen},
  title =	{{Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper)}},
  booktitle =	{10th International Conference on Geographic Information  Science (GIScience 2018)},
  pages =	{70:1--70:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Stephan Winter and Amy Griffin and Monika Sester},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2018/9398},
  URN =		{urn:nbn:de:0030-drops-93983},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.70},
  annote =	{Keywords: neighborhood conditions assessment, geographic object-based image analysis, spatial analysis}
}

Keywords: neighborhood conditions assessment, geographic object-based image analysis, spatial analysis
Collection: 10th International Conference on Geographic Information Science (GIScience 2018)
Issue Date: 2018
Date of publication: 02.08.2018

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