It is possible to obtain vast amounts of spatiotemporal data related to human activities to support the study of human behavior and social evolution. In this context, geography, with the human–nature relationship as its core, is undergoing a transition from strictly earth observations to the observation of human activities. Geocomputation for social science is one manifestation thereof. Geocomputation for social science is an interdisciplinary approach combining remote sensing techniques, social science, and big data computation. Driven by the availability of spatially and temporally expansive big data, geocomputation for social science uses spatiotemporal statistical analyses to detect and analyze the interactions between human behavior, the natural environment, and social activities; Remote sensing (RS) observations are used as primary data. Geocomputation for social science can be used to investigate major social issues and to assess the impact of major natural and societal events, and will surely be an area of focused development in geography in the near future. We briefly review the background of geocomputation in the social sciences, discuss its definition and disciplinary characteristics, and highlight the main research foci. Several key technologies and applications are also illustrated with relevant case studies of the Syrian Civil War, typhoon transits, and traffic patterns.

结合土地利用类型数据和容积率数据,本文提出一种基于逻辑回归模型的社交网络定位数据识别居民职住地的方法,并与人口普查数据、交通调查数据进行多角度多指标的对比验证,分析该数据源在城市居民职住地研究中的可靠性.该研究成果为大数据时代下的城市居民职住地研究提供了一种新型大规模数据源的可行性探讨,对城市规划和交通规划等问题具有重要参考意义.

With the rapid development of communication technology, a large amount of spatiotemporal trajectory data has been produced. One of the critical applications of trajectory data is location prediction that is important for urban traffic planning and location-based services. Although many methods for personal location prediction have been proposed, to the best of our knowledge, some users always have sparse historical trajectory data in practical applications, resulting in poor location prediction precision. Targeting on this challenge, we propose an Individual Trajectory-Group Trajectory (ITGT) location prediction model by utilizing the pattern of group travels. First, the model performs the stay point extraction and conducts the spatial clustering to construct the clustering link. Second, Fano's inequality and clustering link are used to evaluate the predictability of location. Third, two variable order Markov models, named prediction by partial match (PPM) and probabilistic suffix tree (PST), are adopted to predict the clustering link. Finally, our approach is evaluated by using 608 712 points from 5000 volunteers at Shenzhen, China. The results show that: 1) when using individual trajectory, the PPM individual model is superior to the conventional N-order Markov model and PST individual model; 2) when all group trajectories are used, the PPM group model is not as accurate as the PPM individual model; and 3) when it classifies the group trajectory into different traffic zones, the PPM zone model is better than the PPM group model and the PPM individual model. The prediction precision of 1-3 order PPM zone model is 83.21%, 86.75%, and 87.35%, respectively, which introduces approximately 3% performance gains by utilizing the characters of traffic zone groups.

The rapid development of information and communication technology and the popularization of mobile devices have generated a large number of spatiotemporal trajectory data. Trajectory data can be applied to location prediction, which is significant for urban traffic planning and location-based service. Although various methods for personal location prediction have been proposed, the historical trajectory data of some users is always sparse in practical applications, resulting in poor prediction precision of prediction models based on personal historical data for those sparse users. Targeting on this challenge, we propose an "Individual trajectory-Group trajectory assist Individual trajectory" location prediction model (ITGTAIT) by utilizing the group travel patterns to assist in predicting personal locations. First, the model conducts a spatial clustering algorithm on trajectory points to construct the clustering link. Second, the clustering link and Fano's inequality are used to estimate the predictability of the next location. Third, a Variable Order Markov Model that named Prediction by Partial Match (PPM) was adopted to predict the clustering link based on the individual trajectory for users with sufficient data. For users with sparse samples, the PPM utilizes the pattern of group travels, which using the group trajectory to assist individual trajectory. Finally, our method was evaluated by using 608,712 trajectory points from 5000 volunteers at Shenzhen city, China. The result shows that a) with the increase of training data, the precision of the ITGTAIT is gradually stable, b) for users with over four days of data, the highest precision is 87.11%, stable at about 82%, c) for users with only 1-3 days of data, the prediction precision is 55.15%, 67.04%, and 76.86% respectively, which introduces approximately 10.76%, 10.98% and 6.83% performance gains on location predictions respectively by utilizing the group characters.

With the advent of Information and Communication technology (ICT) in modern age, the statement of “death of distance” has received numerous discussions. This article contributes a new empirical study to the debate of “death of distance” by considering the effect of spatial autocorrelation in the estimation of distance decay effect with the incorporation of network autocorrelation in spatial econometric modeling. This work is based on a city-level dataset from China's largest social networking site called Weibo. The findings are shown as following. First, the coefficient value of network autocorrelation term (0.007, significant at 0.01 level) suggests that the city-level online social links are spatially dependent. In other words, these social connections are not randomly distributed across space but tend to form spatial clusters where neighboring links are more similar. Second, controlling spatial autocorrelation in the data, a distance decay effect on the formation of online social links is unveiled with a much smaller scaling exponent of the distances (i.e., 0.276) as compared to those (e.g., 2.0, 1.8, 1.45, 1.06, 1.03, 0.4, and 0.5) in existing studies. This research provides a useful modeling framework to analyze the real-world driving forces that characterize the patterns of social interactions in virtual space and thus advance our understanding in the connection of virtual and real spaces.

Understanding urban functions and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we present a novel approach to uncovering urban functions by aggregating human activities inferred from mobile phone positioning and social media data. First, the homes and workplaces (of travelers) are estimated from mobile phone positioning data to annotate the activities conducted at these locations. The remaining activities (such as shopping, schooling, transportation, recreation and entertainment) are labeled using a hidden Markov model with social knowledge learned from social media check-in data over a lengthy period. By aggregating identified human activities, hourly urban functions are inferred, and the diurnal dynamics of those functions are revealed. An empirical analysis was conducted for the case of Shenzhen, China. The results indicate that the proposed approach can capture citywide dynamics of both human activities and urban functions. It also suggests that although many urban areas have been officially labeled with a single land-use type, they may provide different functions over time depending on the types and range of human activities. The study demonstrates that combining different data on human activities could yield an improved understanding of urban functions, which would benefit short-term urban decision-making and long-term urban policy making.

Mp-matching floating car data is a fundamental task in traffic surveillance, traffic anomaly detection, and urban dynamic analysis. This study proposes a parallel map-matching approach to process streaming large volume floating car data. Considering the connectivity of a transportation network, the matching candidates are limited with a coarse spatial grid. A distance filter and a direction filter are combined to reduce the number of matching candidates. The trajectory between consecutive nodes is recovered with a shortest path list. The shortest path list in memory was developed to reduce the computation and speed up the matching process. A non-relational distributed database parallelizes the map-matching procedure. The performance of the presented approach was tested with large volume floating car data in Wuhan, China. It demonstrates that this method achieves 90.62% correct map-matching results. This efficiency could fulfill the needs of real-time traffic monitoring, and will benefit trajectory analysis.

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial-temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial-temporal demand coverage approach for optimizing the placement of ET charging stations in the space-time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting. (C) 2015 Elsevier Ltd. All rights reserved.

Location-based service information, provided by social networks, provides new data sources and perspectives to research tourism activities, especially in highly populated mega-cities. Based on three years (2012–2014) of approximately 340,000 check-in records collected from Sina micro-blog at 86 tourist attractions in Shenzhen, a first-tier city in southern China, we conducted a comprehensive study of the attraction features involving different aspects, such as tourist source, duration of stay, check-in activity index, and attraction correlation degree. The results showed that (1) theme parks established in the early 1990s were the most popular tourist attractions in Shenzhen, but a negative trend was detected in the check-in population; (2) compared with check-in times from surrounding activities and the kernel density of tourists, most destinations in Shenzhen showed a lack of attraction, failing to make the most of their geographic accessibility; and (3) the homogeneity and inconvenient traffic conditions of major tourist destinations leading to the construction of a tourism tour chain has become a challenge. The results of this study demonstrate the potential of big-data mining and provide valuable insights into tourism market design and management in mega-cities.

Rapid growing urbanization and explosive e-business expect effective logistics distribution service in the metropolitan area. Because of traffic control, commuting peak and unpredictable traffic accidents, traffic states in the metropolitan area fluctuate sharply, leading to the unacceptable logistics service delay in our daily life. To overcome this problem, a spatio-temporal decision support (STDS) framework is developed to facilitate large scale logistics distribution in the metropolitan area. It consists of a traffic information database, a spatio-temporal heuristic algorithm module, many intelligent mobile apps and a cloud geographical information science (GIS) based logistics server. The spatio-temporal heuristics algorithm is to optimize logistics vehicle routing with the historical traffic information. The mobile apps guide the deliverymen in the real-time logistics. The cloud GIS based logistics server integrates traffic information, client demands, vehicle information, the optimizationOptimization of vehicle routing and the monitoring of logistics processes. The STDS framework has been implemented in a GIS environment. Its performance is evaluated with large scale logistics cases in Guangzhou, China. Results demonstrates the effectiveness and the efficiency of the developed STDS framework. The STDS framework could be widely used in the logistics distribution in metropolitan area, such as the express delivery, e-business, and so on.

Macroscopically monitoring the status of urbanization and fast acquiring the land covers or land use in urban areas is essential for urban planning, management and scientific policy-making. The rapidly developing remote sensing technologies have been recognized as an essential approach to carry out this work because of their vital ability to capture the physical features of different land use, such as the spectral and textural properties. However, these technologies could not reveal the heterogeneity of urban development and differentiate the vitality in and among cities with the similar physical properties interpreted from remote sensing images. Human-activity based sensing technologies nowadays have been recognized as a promising alternative to resolve these problems. Spatio-temporal distribution of human activities could be derived from mobile phone records and smart card records stored in the public transportation systems, social media or social networking services (SNSs), and etc. They are good indicators for the social function of land use and urban vitality. We proposed types of indices to bridge the relationships between the intensities of human activities and land covers. Similar to the spectral bands of remote sensing images, more than thirty social bands were generated in this paper to describe the social characteristics of ground objects by aggregating and gridding human activities into pixels. According to the spectral profiles of eight land covers, a supervised classification approach was then applied to infer the land covers of the research area. Validation experiments were conducted in Shenzhen, China using a large-scale of people's historical login information on Tencent QQ, which is the most popular SNS, during 2013. Results showed that the land cover of Shenzhen could be determined with a detection rate of 72%according to an urban planning map of Shenzhen. Compared to the classification results from remote sensing images, the human-activity based sensing technologies can obtain more detailed insight into the urban form, city skeleton, and the heterogeneity of development and vitality in different urban areas.

Physical location is an important characteristic for digital individuals, as it is widely used in location based services, such as navigation, advertisements, and recommendations. This paper focuses on the problem of inferring individual physical locations from their friendships in a social network. We represent individual locations with a few high frequency places to eliminate the noise influence. By using of interactions between users, a spatial based inferring model is developed to directly estimate individual physical locations. The spatial weighted clustering method is used by considering the structure of interactions between friends. Data from Tencent, the biggest social network service provider in China, is used to conduct an experiment to validate the performance of the proposed inferring framework. Results indicate the framework can predict individual locations within 15 km in distance error with the accuracy of 68%.

Understanding the spatial distribution of human social-economic activities helps marketing, policy making, planning, and government management. With rapid growing of internet, communication technology and the high penetration of mobile phones, massive activity data is collected from people daily life, giving us an opportunity to estimate the national distribution of human activities. This paper proposed a human-sensing approach to estimate the spatial distribution of economic activity derived from the mass of human mobility data. Comparisons with the economic activity estimation method based on a remote-sensing technology (i.e., night-time light imagery) are analyzed. The results indicated that human mobility and activity data is a good indicator to model the high-resolution economic activity.

Based on the massive amount of users social relationship and location information data collected from the largest social network website in China-SINA micro-blog,an inter-city geographic social network was built from the aggregated inter-person linkages mapped from virtual space onto the physical space.Considering about the topological characteristics such as the global and local heterogeneity,and the spatial interactions among cities,a hybrid geographic backbone extraction approach is proposed which is drawn from the gravity model and information entropy theories.This research can increase understanding of the structure of urban systems,radiant ability,attractiveness and openness of cities in the virtual web society.

Cost analysis is essential to enterprises developing plans to deal with product obsolescence. Indeed, cost analysis drives the optimization behind obsolescence mitigation planning and the maintenance of long field life sustainnient-dominated systems. There are many different obsolescence mitigation solutions. Determining the optimum plan requires inputs from multiple departments within the enterprise such as maintenance, manufacturing, inventory, marketing, purchasing, etc. Moreover, proper analysis requires system records over a long period. As one might expect, these needs present challenges since proper data comes from different sources across multiple departments. In recent years, ontological models have been shown to be good at relation representation and knowledge management. Ontologies have been used to help with data integration and decision-making. This paper puts forward an ontology-based model and data inquiry method to help locate appropriate departments and related heterogeneous data for current and legacy data sources. The ontology-enabled data inquiry can then more accurately and efficiently improve cost analysis and the planning and management of obsolescence mitigation activities.

In this paper, a bottom-up vehicle emission model is proposed to estimate real-time CO2 emissions using intelligent transportation system (ITS) technologies. In the proposed model, traffic data that were collected by ITS are fully utilized to estimate detailed vehicle technology data (e. g., vehicle type) and driving pattern data (e. g., speed, acceleration, and road slope) in the road network. The road network is divided into a set of small road segments to consider the effects of heterogeneous speeds within a road link. A real-world case study in Beijing, China, is carried out to demonstrate the applicability of the proposed model. The spatiotemporal distributions of CO2 emissions in Beijing are analyzed and discussed. The results of the case study indicate that ITS technologies can be a useful tool for real-time estimations of CO2 emissions with a high spatiotemporal resolution.

基于中国大型社交网络人际社交关系,以新浪微博为例,构建中国城际地理社交关系网络,提出以城市社交通量指数、城际社交通量指数和城市社交集聚指数为主要评价指标,借此评价城市社交活跃度与影响力、城际社交强度以及城市社交偏好性,分析中国城市社交网络的拓扑特征.研究显示,中国城际社交网络具有明显的全局异构性和局部异构性,在地理空间分布上呈现空间分异现象.城市的社交通量与集聚指数呈较强的正相关,深刻影响中国社会文明进程的一二线城市属于社交寡占型城市,与外界交流相比中小城市社交偏好更为集聚.该研究可为探讨线上网络空间与线下现实空间的映射关系,揭示社交网络信息流的地理空间传播路径、预测网络热点事件时空演化趋势及时空影响范围提供理论依据.

In product design, passing undetected errors to the downstream can cause error avalanche, could diminish product acceptance and largely increase the overall cost. Yet, it is difficult for designers to collect all the related potential errors from different departments in the initial design phase. In order to deal with these problems, this paper puts forward an ontology based method to integrate related history error data from different data sources of multiple departments in an enterprise. By using the advantages of ontologies and ontology-based information systems in knowledge management and semantic reasoning, the method enables the investigation of the root cause of the related potential malfunctions in the early product design phase. The framework can provide warnings and root causes of related potential errors in design based on history data and further continuously improve the product design. In this manner, this method is expected to reduce the knowledge limitation of designers in the initial design phase, help designers consider the problems in the whole enterprise and the product life cycle more completely, facilitate design improvement more accurately and efficiently, and further reduce the cost of the overall product life cycle.