Sensing Mixed Urban Land-Use Patterns Using Municipal Water Consumption Time Series

Guan, Qingfeng; Cheng, Sijing; Pan, Yongting; Yao, Yao; Zeng, Wen

doi:10.1080/24694452.2020.1769463

Cited by 6 publications

(2 citation statements)

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“…Topographic features such as elevation, slope and aspect can be used in urban land use classification (Chen et al, 2017b). Given the advantage of more complete population coverage and longer temporal spans, municipal data such as water consumption data has been used to identify the socioeconomic functions of urban lands (Pan et al, 2020) and analyze mixed patterns of land use (Guan, Cheng, Pan, Yao, & Zeng, 2021), because the land use types and composition can be estimated by the classification of spatiotemporal patterns in water consumption from individual end-users (Guan et al, 2021).…”

Section: Auxiliary Data Sourcesmentioning

confidence: 99%

“…Dovey and Pafka (2017) proposed a live/work/visit triangle framework focusing on the interconnections between land use functions to measure land-use mix. Considering the biased spatiotemporal coverage of social sensing data, Guan et al (2021) proposed to identify individual socioeconomic functions by the water consumption patterns of municipal services and measure the mixed land-use patterns using the information entropy index. However, the data availability of municipal services will barricade large-scale implementations and practices of this type of approach.…”

Section: Mixed Land-use Problemmentioning

confidence: 99%

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Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities

Чэн

Gong

2021

Big Earth Data

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Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape design, environmental management, health promotion, and biodiversity conservation. Land-use maps outlining the distribution, pattern, and composition of essential urban land use categories (EULUC) have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies. New and improved Earth observations, algorithms, and advanced products for extracting thematic urban information, in association with emerging social sensing big data and auxiliary crowdsourcing datasets, all together offer great potentials to mapping fine-resolution EULUC from regional to global scales. Here we review the advances of EULUC mapping research and practices in terms of their data, methods, and applications. Based on the historical retrospect, we summarize the challenges and limitations of current EULUC studies regarding sample collection, mixed land use problem, data and model generalization, and large-scale mapping efforts. Finally, we propose and discuss future opportunities, including cross-scale mapping, optimal integration of multi-source features, global sample libraries from crowdsourcing approaches, advanced machine learning and ensembled classification strategy, open portals for data visualization and sharing, multi-temporal mapping of EULUC change, and implications in urban environmental studies, to facilitate multi-scale fine-resolution EULUC mapping research.

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Section: Auxiliary Data Sourcesmentioning

confidence: 99%

Section: Mixed Land-use Problemmentioning

confidence: 99%