Object-Core Oriented Data Modelling for Tracking of Behaviors of Urban Heat Islands

Abstract

<p><strong>Abstract.</strong> Modeling thematic and spatial dynamic behaviors of Urban Heat Islands (UHIs) over time is crucial to understand the evolution of this phenomenon and the city micro-climate. Previous studies conceptualized that a UHI can only have a single life period with spatial behaviors (i.e. areal changes and topological transformations). However, a UHI can also appear and disappear periodically several times expressed by thematic and spatial integrated behaviors, which has not been established yet. Thus, this study conceptualizes each UHI as an object which has thematic and spatial behaviors simultaneously and proposes several graphs to depict periodic life-time transitions triggered by the behaviors. The model was implemented in an object-relational database, and air temperatures collected from a number of weather stations were interpolated as temperature images each hour for six weeks. Results indicated that the model could track the spatial and thematic evolution of UHIs through continuous time effectively, and also revealed the periodical patterns and abnormal cases of UHIs over a city.</p>

Machine learning-assisted mapping of city-scale air temperature: Using sparse meteorological data for urban climate modeling and adaptation

“…A dense monitoring network has been built and put into use in Guangzhou, China, with 321 automatic weather stations (AWS) during our study period, which has a mean sample spacing of 5 km (approximately 25 km 2 /sample). Such dense monitoring networks provide higher data quality than crowdsource data and have proved valuable for urban heat island studies [23,24] .…”