Mobasshir Bhuiya Shagor
ABSTRACT
Humans are affective by nature. They have the tendency to response affects during events. They comprehend and evaluate their places, environments, and surroundings when they ramble. Our work illustrates how these responses to different places, environments, and surroundings can be used to classify places into different feature labels according to different problem categories. These affective responses will also give us a leg up to visualize and better understand human interaction with the places, environments, and surroundings and also empower smart geospatial applications.
In this paper, we proposed a map called Categorized Affect Map (CAM) and a model named category-affect-space model where we collect affective response data as subjective geographic information and evaluate and categorize places based on them using categorized problem features. We also redesigned the self-report, popular crowdsourcing approach for collecting and validating affective responses. The findings of this study will rebound to the benefits of better understanding the human spatial experience, daily behavior, and decision making in different places based on the different problem category features and also expose a new window to research for GIScience.
A multi-label classification algorithm will be used to categorize the places based on the affective responses. Our goal is to visualize and demonstrate how people affect the different categorized problems in different places in the form of smart geospatial applications. We also figured out and filled up the gaps to the important challenges like collecting data and validating data quality for crowdsourcing affective responses which previous research failed to accomplish.
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Index Terms
- CAM - Categorized Affect Map: Implementation of a Smart Geographic Information System for Categorizing Places Based on People's Affective Responses
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