As a second-order analysis method of spatial point patterns, Ripley's K function (K function for short) uses distance as an independent variable to detect the distribution patterns of points under different spatial scales, which has been widely used in distinct fields such as ecology, economics, and geography. However, the applications of K function are limited due to the sharply increased computational cost of nested traversals on the point- pair distance measurements in both estimation and simulation phases when the point size getting larger. Therefore, the optimization of algorithm workflow and parallel acceleration have become the key technologies for tackling the performance bottleneck and computability problem of K function. Among these solutions, hashbased partitioning has been widely adopted in parallel computing frameworks for enabling data decomposition, while R-tree indexes have been proposed to reduce the computational cost of point-pair distance measurements by using spatial query instead. However, default hash-based partitioning methods ignore the spatial proximity of data distributions, while R-tree indexes fail to save query time of neighboring points under large spatial distance threshold comparing with pointwise distance calculation. In order to address these issues, this paper proposes an acceleration method for K function based on the space filling curves. Specifically, the Hilbert curve is adopted to achieve spatial partitioning, which reduces the data tilt and communication cost between partitions by better considering the spatial proximity. Upon the partition result, local indexing based on Geohash code is further developed to improve the spatial indexing strategy, which embeds spatial information in codes for achieving quick distance filtering, in turn accelerates the pointwise distance measurements. To verify the effectiveness of the proposed method, it is compared with two optimization methods adopted in previous studies, i.e., default partition without indexing, and KDB-tree partition with R-tree indexing, by analyzing the calculation time of K function for Point of Interests (POIs) of enterprise registration data in Hubei province, China under different data sizes, spatial distance thresholds, and computing nodes in a computing cluster. Experimental results indicate that the time cost of the proposed method is about 1/4 of that for default partition without indexing under the data scale of 300 000 points. Besides, the speedup ratio is larger than 3.6 times under 9 nodes. Therefore, the proposed method can improve performance of K function effectively in a distributed environment and has a promising scalability and could provide a reference for accelerating other spatial patterns analysis algorithms as well.

Information empowerment to the territorial spatial planning has become a hot research field in the new era. However, research on territorial utilization evaluation using big data integration remains to be explored. The purpose of this paper is to carry out an empirical study on the efficiency assessment of residential land use in new urban area employing Tencent location-based big data. Assessment index of residential land use efficiency in each residential area have been proposed, supported by integration of multi-source geospatial data, to reveal the differences in land use efficiency among different residential areas in Changzhou city. The results show that, firstly, population size of hourly particle statistics within the residential area fluctuates periodically, reaching peak value at 21:00 generally, which is in line with the routine of daily going out and returning home for urban residents. There are also expected differences in population agglomeration degree and population size among residential buildings with different capacity rates. Secondly, the 29 residential areas are divided into five groups by year of construction, 1980s, 1990s, 2000s, 2010—2015, and post-2015. The average population size of efficiency index of group 1980s, 1990s, 2000s, 2010—2015, and post-2015 are 1.74, 2.45, 2.31, 0.95, and 0.91 per 100 m2, respectively. Index values of residential areas built before 2010 are significantly higher than those built after 2010. Furthermore, residential areas built after 2010 are lower than the average level (population size of 2.06 per 100 m2 in year 2018) of the entire urban residential areas. Thirdly, it is suggested that lower results of efficiency index is not fully equal to poor level of intensive land use. The main reasons of diverse land use efficiency of residential areas constructed in different periods include the growth periodicity of new urban area development in Changzhou city, and urban residents' desire for better living environment to enhance their quality of habitation. Research shows that location-based big data, as a source of population data with high solution, could reflect the temporal and spatial characteristics of resident aggregations objectively. Index constructed to assess urban residential land efficiency using location-based big data is both innovative and scientific, which could provide a new way for the analysis of high-quality land space utilization. In conclusion, regularity recognition of behavior characteristics from urban residents can provide support for spatial policy formulation during the urbanization process based on "putting people first" policy in China. What's more, new data sources, represented by location-based big data in this paper, will play an important role in decision-making mechanism of territorial spatial planning.

How to conduct quantitative analysis of urban vitality in a scientific and efficient manner has become a key research issue nowadays. Based on the multi-source geographic big data such as OpenStreetMap, Baidu Map POI, WeChat Travel, Meituan, Gaode building outlines, etc., and from the dual perspectives of people and space, this study selects indicators from four aspects, including crowd vitality, diversity of vitality, activity satisfaction, and spatial interaction potential, to construct a comprehensive vitality evaluation model of Spatial TOPSIS. Using the model, this study evaluates the comprehensive vitality of the downtown area of Nanjing, analyzes the spatial distribution characteristics of the vitality of the neighborhood, and explores the similarities and differences of vitality poles between weekdays and weekends. The evaluation results are compared with that of Entropy TOPSIS, in an effort to explore the impact of spatial interaction on the vitality of blocks. This study aims to help urban planners to understand the current status of urban vitality systematically, and provide a feasible plan for urban planning research. The research shows that, firstly, the spatial distribution characteristics of the comprehensive vitality of the downtown blocks in Nanjing urban center are similar between weekdays and weekends. However, the comprehensive vitality of the blocks on weekdays is higher than that on weekends. From the perspective of block functions, the high-value areas of comprehensive vitality are mainly concentrated in commercial centers, tourist attractions, and transportation hubs, which are closely related to the distribution of transportation stations (e.g. subway stations). Secondly, based on the vitality analysis of weekdays and weekends, it is found that Hunanlu - Xinjiekou - Confucius temple scenic area is the largest and most stable vitality pole. Among the small vitality poles, only Longjiang Metro Station has begun to take shape. Other small vitality poles, including Jiqingmen Street, Olympic Sports Center, Baijiahu Commercial District, and Wanda Commercial District, are unstable. Their vitality is still growing. Thus, they may become bigger vitality poles in the future. Thirdly, both the high vitality blocks in the center of the study area and the low vitality blocks receive less spatial effects. The areas with the greatest vitality change are generally distributed in a ring shape around the periphery of the central city. Combining the block functions, it is found that the comprehensive vitality of block units of other land and industrial land is less affected by the spatial interaction. In comparison, residential, commercial, scientific, educational, and cultural land are greatly affected by spatial interaction.

The MODIS Enhanced Vegetation Index (EVI) time- series data has been widely used in many research fields such as vegetation observation, ecological environment, and global meteorological changes. However, even though the EVI time series data has undergone strict preprocessing, there are still some noises in it. Therefore, this paper develops a simple and effective method to reconstruct EVI time-series data and eliminate the noise in EVI time- series data, especially some noise caused by atmospheric clouds and snow cover. The theory of the new method is derived from graph theory, using the relationship of the Laplacian matrix to assign the weight of the pixel of the selected neighborhood window in EVI to get the fitting of the center pixel. The new method has been applied to MODIS MOD13A1 products from 2016 to 2018 and compared with the S-G filtering method, Harmonic Analysis of Time Series method, Double Logistic function method, and Asymmetric Gaussian model function method. The results show that in the desert, grassland, and woodland, the absolute difference of the leave-one verification test of the new method is the smallest, which is better than other methods; when fitting EVI time-series data of different vegetation types, the graph theory neighbor method presents a better detailed fitting curve; the RMSE values of the new method in the five vegetation types are 200.59, 46.58, 63.48, 165.47, and 40.95 respectively, which are the smallest values among the five methods and are more effective in obtaining high- fidelity and high- quality EVI time- series data. The method research in this article can provide a useful reference for the denoising of vegetation remote sensing time- series data and the study of the ecological environment.

Railway passenger flows reflect economic and social interactions in urban agglomerations. Urban agglomeration is the basic regional unit for a country to participate in global competition and international division of labor. It is also the main form of China's new urbanization and an important carrier of modernization. The integrated development of urban agglomerations is one of the major strategies for the development of urban agglomerations in China. An important sign of urban agglomeration integration is the structural characteristics of population, goods, information, and other flows within the urban agglomeration. In this context, the theory of flow space is applied in this study. Based on China's railway schedule OD data in 2018, this study uses complex network approach to analyze the structure patterns of urban agglomerations at the national scale, and investigates the variations of the spatial structure, scale structure, and network node structure in the five major urban agglomerations. The results of analysis show that there is a certain spatial dislocation in the scope between the urban agglomeration area based on the railway schedule and the urban agglomeration in the planning scheme. The planning scope of urban agglomerations is often larger than that of railway passenger transport service supply network. This shows that the supply of railway passenger service may lag behind the urban agglomeration planning. The five major urban agglomerations, Yangtze River Delta, Pearl River Delta, Jingjinji, Middle reach of the Yangtze River, and Chengdu-Chongqing, have different local patterns. Yangtze River Delta has a networkshaped spatial form and centralized scale distribution, the main problem of which is the mismatch between hub function and scale of local nodes. Pearl River Delta has a radial spatial form and centralized scale distribution, the main problem of which is the relative weak connection with other urban agglomerations. Jingjinji has a radial spatial form and centralized scale distribution, the main problem of which is the existence of marginalized nodes. The middle reach of the Yangtze River has a cluster- like spatial form and decentralized scale distribution, the main problem of which is the loose internal structure. Chengdu-Chongqing has a dumbbell-shaped spatial form and centralized scale distribution, the main problem of which is the weak connection with other urban agglomerations. With the process of regional integration, it is necessary to make up for the weak links within and among urban agglomerations according to the differences in the development stages of the network structure, so as to promote the coordination with the scale structure. It is necessary to promote the construction of passenger transport service network with distinct levels, cooperative hubs, and balanced spatial form to support the integrated development of urban agglomeration.

Many cities in China have adopted a series of Non-Pharmaceutical Interventions (NPIs) and rapidly suppressed the 1^st wave of COVID-19 epidemic in 2020. It is critical to evaluate the effectiveness of these NPIs for future epidemic control. However, as a variety of NPIs were applied together in practice, it is difficult to evaluate the effectiveness of a single type of intervention by epidemiological observation. Taking Shenzhen city as an example, this study used a spatially explicit agent-based model by integrating mobile phone location data, travel survey data, building survey data and other multi- source spatiotemporal big data to evaluate the effectiveness of different types of NPIs in the suppression of the 1^st wave of COVID-19 epidemic in Shenzhen. The simulation results show that the peak of the epidemic would have appeared on the 127^th day since Jan 1^st of 2020, resulting in an average of 72.26% of the population to be infected without any interventions. In the 1^st wave of Shenzhen epidemic, except for the hospitalization of confirmed cases and intercity traffic restrictions, the stay- at- home order was the most effective one, followed by comprehensive isolation and quarantine measures (for close contacts, imported population and suspected cases), mask wearing, and orderly resumption of work. The stay- at-home order and comprehensive isolation and quarantine measures can effectively control the large-scale outbreak of the COVID-19, which are identified as the core measures; Mask wearing and orderly resumption of work can only reduce the overall infection size and delay the epidemic peak, which are identified as secondary measures. Considering the socioeconomic costs and the receding compliance to interventions in the post- epidemic period, this study suggests that the core measures and secondary measures should be combined to control the sporadic cases. Specifically, the local government can give the highest priority to isolation and quarantine measures for confirmed cases and high- risk individuals, complemented by mask wearing. In addition, our model can reveal the high- risk infection areas at a community level, which can help deploy control measures within an urban environment. In summary, this study demonstrated the advantages of integrating spatiotemporal big data and agent- based models to simulate the spread processes of infectious diseases in an urban environment: it can not only simulate the evolving processes of an epidemic at a finegrained scale, but also evaluate the effectiveness of the NPIs at an individual level and for activity- travel behaviors, which can be useful for precise intervention.