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Urban built-up areas, where urbanization process takes place, represent well-developed areas in a city. The accurate and timely extraction of urban built-up areas has a fundamental role in the comprehension and management of urbanization dynamics. Urban built-up areas are not only a reflection of urban expansion but also the main space carrier of social activities. Recent research has attempted to integrate the social factor to improve the extraction accuracy. However, the existing extraction methods based on nighttime light data only focus on the integration of a single factor, such as points of interest or road networks, which leads to weak constraint and low accuracy. To address this issue, a new index-based methodology for urban built-up area extraction that fuses nighttime light data with multisource big data is proposed in this paper. The proposed index, while being conceptually simple and computationally inexpensive, can extract the built-up areas efficiently. First, a new index-based methodology, which integrates nighttime light data with points-of-interest, road networks, and the enhanced vegetation index, was constructed. Then, based on the proposed new index and the reference urban built-up data area, urban built-up area extraction was performed based on the dynamic threshold dichotomy method. Finally, the proposed method was validated based on actual data in a city. The experimental results indicate that the proposed index has high accuracy (recall, precision and F1 score) and applicability for urban built-up area boundary extraction. Moreover, this paper discussed different existing urban area extraction methods, and provides an insight into the appropriate approaches selection for further urban built-up area extraction in cities with different conditions.
Chengming Li; Xiaoyan Wang; Zheng Wu; Zhaoxin Dai; Jie Yin; Chengcheng Zhang. An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data. Sustainability 2021, 13, 5042 .
AMA StyleChengming Li, Xiaoyan Wang, Zheng Wu, Zhaoxin Dai, Jie Yin, Chengcheng Zhang. An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data. Sustainability. 2021; 13 (9):5042.
Chicago/Turabian StyleChengming Li; Xiaoyan Wang; Zheng Wu; Zhaoxin Dai; Jie Yin; Chengcheng Zhang. 2021. "An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data." Sustainability 13, no. 9: 5042.
The COVID-19 pandemic is a major problem facing humanity throughout the world. The rapid and accurate tracking of population flows may therefore be epidemiologically informative. This paper adopts a massive amount of daily population flow data (from January 10 to March 15, 2020) for China obtained from the Baidu Migration platform to analyze the changes of the spatiotemporal patterns and network characteristics in population flow during the pre-outbreak period, outbreak period, and post-peak period. The results show that (1) for temporal characteristics of population flow, the total population flow varies greatly between the three periods, with an overall trend of the pre-outbreak period flow > the post-peak period flow > the outbreak period flow. Impacted by the lockdown measures, the population flow in various provinces plunged drastically and remained low until the post-peak period, at which time it gradually increased. (2) For the spatial pattern, the pattern of population flow is divided by the geographic demarcation line known as the Hu (Heihe-Tengchong) Line, with a high-density interconnected network in the southeast half and a low-density serial-connection network in the northwest half. During the outbreak period, Wuhan city appeared as a hollow region in the population flow network; during the post-peak period, the population flow increased gradually, but it was mainly focused on intra-provincial flow. (3) For the network characteristic changes, during the outbreak period, the gap in the network status between cities at different administrative levels narrowed significantly. Thus, the feasibility of Baidu migration data, comparison with non-epidemic periods, and optimal implications are discussed. This paper mainly described the difference and specific information under non-normal situation compared with existing results under a normal situation, and analyzed the impact mechanism, which can provide a reference for local governments to make policy recommendations for economic recovery in the future under the epidemic period.
Chengming Li; Zheng Wu; Lining Zhu; Li Liu; Chengcheng Zhang. Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic. ISPRS International Journal of Geo-Information 2021, 10, 145 .
AMA StyleChengming Li, Zheng Wu, Lining Zhu, Li Liu, Chengcheng Zhang. Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic. ISPRS International Journal of Geo-Information. 2021; 10 (3):145.
Chicago/Turabian StyleChengming Li; Zheng Wu; Lining Zhu; Li Liu; Chengcheng Zhang. 2021. "Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic." ISPRS International Journal of Geo-Information 10, no. 3: 145.