Exploring the Correlation between Population Distribution and Urban Heat Island under Urban Data: Taking Shenzhen Urban Heat Island as an Example
Authors: Wang Yang
Shenzhen is a modern city of China's reform and opening-up policy, the development of urban morphology has been established on the administration of the Chinese government. This city`s planning paradigm is primarily affected by the spatial structure and human behavior. The subjective urban agglomeration center is divided into several groups and centers. In comparisons of this effect, the city development law has better to be neglected. With the continuous development of the internet, extensive data technology has been introduced in China. Data mining and data analysis has become important tools in municipal research. Data mining has been utilized to improve data cleaning such as receiving business data, traffic data and population data. Prior to data mining, government data were collected by traditional means, then were analyzed using city-relationship research, delaying the timeliness of urban development, especially for the contemporary city. Data update speed is very fast and based on the Internet. The city's point of interest (POI) in the excavation serves as data source affecting the city design, while satellite remote sensing is used as a reference object, city analysis is conducted in both directions, the administrative paradigm of government is broken and urban research is restored. Therefore, the use of data mining in urban analysis is very important. The satellite remote sensing data of the Shenzhen city in July 2018 were measured by the satellite Modis sensor and can be utilized to perform land surface temperature inversion, and analyze city heat island distribution of Shenzhen. This article acquired and classified the data from Shenzhen by using Data crawler technology. Data of Shenzhen heat island and interest points were simulated and analyzed in the GIS platform to discover the main features of functional equivalent distribution influence. Shenzhen is located in the east-west area of China. The city’s main streets are also determined according to the direction of city development. Therefore, it is determined that the functional area of the city is also distributed in the east-west direction. The urban heat island can express the heat map according to the functional urban area. Regional POI has correspondence. The research result clearly explains that the distribution of the urban heat island and the distribution of urban POIs are one-to-one correspondence. Urban heat island is primarily influenced by the properties of the underlying surface, avoiding the impact of urban climate. Using urban POIs as analysis object, the distribution of municipal POIs and population aggregation are closely connected, so that the distribution of the population corresponded with the distribution of the urban heat island.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3455713Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 684
 Huang, Conghong, Jun Yang, and Peng Jiang. “Assessing Impacts of Urban Form on Landscape Structure of Urban Green Spaces in China Using Landsat Images Based on Google Earth Engine.” 2018.
 Li, Juan, Y. Long, and A. Dang. "Live-Work-Play Centers of Chinese cities: Identification and temporal evolution with emerging data." Computers, Environment and Urban Systems. 2018, p:58-66.
 MLA Yongze, Song, et al. "Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in China." International Journal of Geographical Information Science .2018, pp:1-30.
 Remote Sensing Micro-Classroom] MODIS Surface Temperature Retrieval Based on Windows Model - News - Global IC Trade Starts Here. N.d. http://blog.sina.com.cn/s/blog_764b1e9d0101cefg.html, accessed November 29, 2018.
 Mao, K., Z. Qin, J. Shi, and P. Gong. “The Research of Split-Window Algorithm on the MODIS.” Geomatics and Information Science of Wuhan University 30(8). 2005.pp.703-706.
 Yoo, C., Im, J., Park, S., & Quackenbush, L. J. Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 137, 2018, pp.149–162.
 M. Bakillah, S. Liang, A. Mobasheri et al., “Fine-resolution population mapping using OpenStreetMap points-of-interest,” International Journal of Geographical Information Science, vol. 28, no. 9, 2014, pp. 1940–1963.
 S. Wang, Y. Wang, J. Tang et al., “What Your Images Reveal,” in Proceedings of the 26th International Conference on World Wide Web, R. Barrett, Ed., Association for Computing Machinery, New York, April 2017, pp. 391–400
 L. Li, Y. Tan, S. Ying et al., “Impact of land cover and population density on land surface temperature: case study in Wuhan, China,” Journal of Applied Remote Sensing, vol. 8, no. 1, 2014,pp. 84993.
 2010 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2010): Toronto, Ontario, Canada, 12-14 July 2010, IEEE, Piscataway N.J, 2010.
 Cai, Meng et al. “Investigating the Relationship between Local Climate Zone and Land Surface Temperature Using an Improved WUDAPT Methodology – A Case Study of Yangtze River Delta, China.” Urban Climate, 2018, pp.485–502.
 B. Tardy, V. Rivalland, M. Huc et al., “A Software Tool for Atmospheric Correction and Surface Temperature Estimation of Landsat Infrared Thermal Data,” Remote Sensing, vol. 8, no. 9, p. 696, 2016.