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Objectives: The spatial delimitation of urban area is the foundation for monitoring the urbanization level and determining city scale from the perspective of geographical space.Methods: Based on the requirements of urban area defined in Document No. 51 of the State Council, this paper clarifies the connotations of urban area with further understandings and proposes a process for delimiting urban area in geographical space by connecting map polygons of governmental residents and neighborhood committee, which can reduce the subjectivity and fuzziness in the spatialization of process of urban area. Results: This paper develops the main idea and spatial delimitation about urban area and takes Wuhan as an example to illustrate the details about spatial delimitation of urban areas.Conclusions: This paper mainly discusses how to unify urban delineation under the current technical conditions, and does not mention how to use other auxiliary data, such as the application of multi⁃source big data in urban delineation, because of the unbalanced development of big data infrastructure in different cities. But it does not mean that the application of population big data and urban big data is not important.On the contrary, the application of big data in the work of urban area⁃ delimitation is the next research focus and the future development trend. [ABSTRACT FROM AUTHOR]

Objectives: Existing population spatialization methods mainly use administrative-unit-level data to train regression model, and transfer it to grid cell-level to achieve population allocation. However, the significant scale difference between the analytical units in training and estimation leads to the issues of crossscale model transfer. Meanwhile, only the attributes of current cell are considered in cell-level feature modeling, which causes the innate spatial association between cells to be eliminated and cells to be isolated. Methods: This paper proposes a novel population spatialization based on random forest by considering pixellevel attribute grading and spatial association (PAG-SA). In the cell-level feature modeling, we firstly construct the night light grading features embedded with building category constraints based on natural breaks, and count the grid proportion of each grading level at the administrative-unit-level as the training input to reduce the cross scale error; secondly, the influence and distance attenuation of neighborhood point of interests (POIs) upon the current cell is modelled by using kernel density estimation; thirdly, based on overlay analysis, the numbers of POIs in the contours of different building types are counted to improve the precision of feature modeling. Results: To verify the effectiveness of the proposed method, we selected Wuhan city as the experimental area and compared its spatialization accuracy with the datasets of WorldPop, GPW and PopulationGrid_China at street scale. The results show that the mean absolute error of PAG⁃SA is only 1/6-1/3 of the comparison datasets. In addition, the influence of feature composition, grid size and kernel density bandwidth on the accuracy is also discussed. Conclusions: By fusing multi source data and considering pixel⁃level attribute grading and spatial association, the proposed method PAG⁃SA is effective for achieving population spatialization in urban areas with finer grid sizes and higher accuracy. It can also provide references for spatialization applications of other geographic attributes that also face with scale mismatch issue in spatial regression modeling. [ABSTRACT FROM AUTHOR]