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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 costand timeeffective neighborhood conditions assessment in support of community data assessment on both demographics and health issues. 2012 ACM Subject Classification Computing methodologies → Computer vision

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