Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Liu, Baihua; Deng, Yingbin; Li, Miao; Yang, Ji; Liu, Tao

doi:10.3390/app11219968

Cited by 37 publications

(23 citation statements)

References 103 publications

(78 reference statements)

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“…This can easily lead to a phenomenon in which much POI is contained in a large area plot and the point density is low. This leads to errors in the subsequent weighted superposition analysis [ 27 ]. Therefore, for this paper, we selected a 1000 m × 1000 m-scale regular fishnet to divide the study area, and there were 6021 grids after sorting.…”

Section: Methodsmentioning

confidence: 99%

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

Ren

Sun

2023

IJERPH

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With the global spread of various human-to-human epidemics, public health issues have become a focus of attention. Therefore, it is of great importance to improve the quantitative risk assessment of the construction of resilient cities in terms of epidemic disasters. Starting with the dimensions of social activities and material space, this paper took Qingdao, China, with a population of 5 million, as an example, and took its seven municipal districts as the research scope. In this paper, five risk factors, including the Population density index, Night light index, Closeness index of roads, Betweenness index of roads and Functional mixed nuclear density index were selected for weighted superposition analysis. We conducted a quantitative assessment of the spatial risk of epidemic disaster so as to obtain the classification and spatial structure of the epidemic disaster risk intensity. The results show that: ① The roads with a large traffic flow are most likely to lead to the risk of urban spatial agglomeration, and the areas with a large population density and large mixture of infrastructure functions are also important factors causing the risk of epidemic agglomeration. ② The analysis results regarding the population, commerce, public services, transportation, residence, industry, green space and other functional places can reflect the high-risk areas for epidemic diseases with different natures of transmission. ③ The risk intensity of epidemic disasters is divided into five risk grade areas. Among them, the spatial structure of epidemic disasters, composed of the first-level risk areas, is characterized by “one main area, four secondary areas, one belt and multiple points” and has the characteristics of spatial diffusion. ④ Catering, shopping, life services, hospitals, schools and transportation functional places are more likely to cause crowd gathering. The management of these places should be focused on prevention and control. At the same time, medical facilities should be established at fixed points in all high-risk areas to ensure the full coverage of services. In general, the quantitative assessment of the spatial risk of major epidemic disasters improves the disaster risk assessment system in the construction of resilient cities. It also focuses on risk assessment for public health events. It is helpful to accurately locate the agglomeration risk areas and epidemic transmission paths that are prone to outbreak or cause epidemic transmission in cities so as to assist the relevant practitioners in containing the epidemic from the initial stage of transmission in a timely manner and prevent the further spread of the epidemic.

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Section: Methodsmentioning

confidence: 99%

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

Ren

Sun

2023

IJERPH

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“…For SSL, the entire image is meshed into 56,576 patches with a size of 256. Considering current researches (Zhang et al, 2020;Liu et al, 2021;Lu et al, 2022) and the "Code for classification of urban and rural land use and planning standards of development land (GB50137)" issued by the Ministry of Housing and Urban-Rural Development of the People's Republic of China, we divide the UFZs into 10 kinds. For model finetuning, we annotate a few patches manually.…”

Section: Study Area and Datamentioning

confidence: 99%

“…However, these data were uploaded by users, so their quality are uncontrollable (Yin et al, 2021a). The advance in earth observation provides high spatiotemporalresolution remote sensing imagery (RSI), which is widely used for UFZ classification research (Bao et al, 2020;Cao et al, 2020;Liu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Urban functional zone classification based on self-supervised learning: A case study in Beijing, China

Feng

2022

Front. Environ. Sci.

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Urban functional zones (UFZs) are the fundamental units for urban management and operation. The advance in earth observation and deep learning technology provides chances for automatically and intelligently classifying UFZs via remote sensing images. However, current methods based on deep learning require numerous high-quality annotations to train a well-performed model, which is time-consuming. Thus, how to train a reliable model using a few annotated data is a problem in UFZ classification. Self-supervised learning (SSL) can optimize models using numerous unannotated data. In this paper, we introduce SSL into UFZ classification to use the instance discrimination pretext task for guiding a model to learn useful features from over 50,000 unannotated remote sensing images and fine tune the model using 700 to 7,000 annotated data. The validation experiment in Beijing, China reveals that 1) using a few annotated data, SSL can achieve a kappa coefficient and an overall accuracy 2.1–11.8% and 2.0–10.0% higher than that of supervised learning (SL), and 2) can also gain results comparable to that got by the SL paradigm using two times annotated data for training. The less the data used for finetuning the more obvious the advantage of SSL to SL. Besides, the comparison experiment between the model pretrained on the research region and that pretrained on the benchmark reveals that the objects with displacement and incompleteness are more difficult for models to classify accurately.

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“…[15][16][17]. POI data is widely used among them [18]. Many researchers have developed methods to identify functional areas based on the data.…”

Section: Introductionmentioning

confidence: 99%

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

Zheng

Zhang

et al. 2022

Land

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Urban agglomeration is a higher stage of urban development. Exploring the spatial correlation of functional areas is important for promoting high-quality urban development. However, recently the research on urban functional areas is mainly focused on how to identify urban functional areas, and they lack some methods to analyze the spatial correlation patterns of urban functional areas. Therefore, firstly, this study uses POI data and a deep learning model to identify the urban functional areas of four typical urban agglomerations in China. Then, we create a new method to mine the spatial correlation patterns of urban functional areas from two levels (city and cities in one urban agglomeration). Moreover, we find that various graphs well express the spatial correlation patterns. Based on the above, we establish a new technical process for mining the spatial correlation of urban functional areas. The main conclusions are as follows: (1) The multilayer detailed division of the functional area is helpful to mine the spatial correlation pattern of the functional area. (2) The rank of each city in the urban agglomeration can be divided according to the urban functional area; there are great differences in richness and scale of the mixed-functional areas in the urban agglomeration, but there is little difference among the urban agglomerations. (3) The spatial correlation patterns of the functional areas in the first-rank cities of each urban agglomeration area are highly similar. (4) There is a certain spatial correlation pattern of functional areas in Chinese urban agglomerations. (5) There are great differences in the similarity of spatial correlation patterns between cities in one urban agglomeration, and the spatial relationship of similarity may not surround the most developed cities. This research will help urban planners to develop functional areas in different cities.

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Classification Schemes and Identification Methods for Urban Functional Zone: A Review of Recent Papers

Cited by 37 publications

References 103 publications

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

Exploring the Quantitative Assessment of Spatial Risk in Response to Major Epidemic Disasters in Megacities: A Case Study of Qingdao

Urban functional zone classification based on self-supervised learning: A case study in Beijing, China

Mining Spatial Correlation Patterns of the Urban Functional Areas in Urban Agglomeration: A Case Study of Four Typical Urban Agglomerations in China

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