This paper presented the spatial database collected in 2013 for mitigating the urban carbon emissions of Jinjiang city, China. The database included the high-resolution CO emissions gridded maps, urban form fragmentation evaluation maps, and city-scale effect related impact factors distribution maps at 30 m and 500 m. We collected the multi-sources data including statistical, vector, and raster data from open-access websites and local governments. We used a general hybrid approach based on global downscaled and bottom-up elements to produce the CO emissions gridded maps. The urban fragmentation was measured by the landscape fragmentation metrics under the feature scale and the accurate identification of the urban functional districts. The percentage of the urban area and the points of interest (POI) density representing the city-scale effect related impact factors were calculated in each grid by the land use and POI data. Our database could be used for the validation of urban CO emissions estimation at the city scale. The landscape metrics and city-scale effect related impact factors maps can also be used for evaluating the socio-economic status in order to solve the other urban spatial planning problems.
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http://dx.doi.org/10.1016/j.dib.2020.105274 | DOI Listing |
J Comput Soc Sci
August 2024
Department of Physics, Cornell University, Ithaca, NY 14850 USA.
Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time.
View Article and Find Full Text PDFCommun Earth Environ
November 2024
Facaulty of Civil and Environmental Engineering, Institute of Technology, Bandung, Indonesia.
Tropical peatland fires generate substantial quantities of airborne fine particulate matter (PM) and in Indonesia are intensified during El Niño-related drought leading to severe air quality impacts affecting local and distant populations. Limited in-situ data often necessitates reliance on air quality models, like that of the Copernicus Atmosphere Monitoring Service, whose accuracy in extreme conditions is not fully understood. Here we demonstrate how a network of low-cost sensors around Palangka Raya, Central Kalimantan during the 2019 fire season, quantified extreme air quality and city-scale variability.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Center for Environmental Solutions & Emergency Response, US Environmental Protection Agency, Cincinnati, OH, USA; Department of Chemical and Biomolecular Engineering, Rice University, Houston, TX, USA. Electronic address:
Sci Total Environ
December 2024
Swiss Federal Institute of Aquatic Science & Technology (Eawag), Überlandstrasse 133, 8600 Dübendorf, ZH, Switzerland.
Problems caused by urban heat have prompted the exploration of urban greenery and blue spaces for heat mitigation. Various numerical models can simulate heat-related processes, but their use as support-tools to urban planners remains underexplored, particularly at the city-scale, due to high computational demand and complexity of such models. This study investigates the spatial relationships between urban heat, urban form and urban green and blue spaces with the fast climate model TARGET (The Air-temperature Response to Green/blue-infrastructure Evaluation Tool), which only requires minimal inputs of standard meteorological data, land cover and building geometry data.
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