During the period of rapid urbanization, the urban fringe area is the area where urban expansion occurs first, and land use change is the most active. Studying its evolution laws and characteristics is of great significance to urban planning and urban expansion, and the primary task of fringe area research is the spatial recognition and boundary division of urban fringe area. The previous methods for defining urban fringe areas are mainly divided into qualitative division based on experience and quantitative division based on indicators constructing. This research avoids the construction of index systems and the selection of mathematical models and improves the objectivity of the experiment. Based on the existing methods, this research considers the correlation between the difference of industrial distribution within cities and the urban spatial structure and spatial distribution of urban elements and considers the distance decay law of urban density. The urban fringe area in this research is defined as the distinction region of the service and manufacturing industry extending outward from the inside of the city. First, calculate the POI density of service industry and manufacturing industry. Then look for the inflection point where its density value drops sharply and get the isoline of that point. The range within the isoline is that the industry extends outward from the inner city and has reached the saturation state. Two types of industries can determine two isolines, and the belt region between those isolines is the urban fringe area. We use the urban fringe area identified from the impervious surface data to verify the result. The comparative results show that the identification method of urban fringe area based on POI works effectively, and it can successfully identify the multi-center urban core area. The method mentioned in this paper provides a new idea from the perspective of industrial activities in identifying and defining the belt region of urban fringe area.

Parks are an important component of the green infrastructure in cities. They provide multiple benefits for residents’ life. Due to the uneven spatial distribution of parks, it is necessary to evaluate the spatial disparity about residents’ access to parks. The two-step floating catchment area method is suitable for measuring the accessibility of urban parks. However, few studies have noticed the representative of entrances of parks, especially adding them into the process of accessibility measurements. The floating catchment area is often determined by road speed and empirical travel time. In this article, the impact of multi-entrances of the park will be considered and real-time travel time is used in calculating accessibility. In a case of multi-entrance parks in Beijing, the average travel time of the sub-district is calculated, and with the support of the modified method, the spatial accessibilities of car driving, public transport and car-public transport mixed travel modes are obtained. The results show that the time–space compression effect of car driving is more obvious and the mixed travel mode presents the advantage of high accessibility index. This study has a theoretical and practical value for urban planning, such as advising urban planners on the siting of green park space.

Shopping stores are an important part of retail facilities and indispensable public facilities in a city. They are not only concentrated in shopping malls, but also distributed independently throughout the city, and often agglomerated in space. This paper attempts to measure the rationality of the spatial layout of all shopping stores in the city. Residents will visit multiple shopping stores in one trip to meet their demands. Based on this characteristic, this paper studies shopping store clusters and proposes a cluster two-step floating catchment area (C-2SFCA) method to analyze the accessibility differences of shopping stores in urban areas. Using the case of Beijing within the Fifth Ring Road, this paper implements the C-2SFCA method in a study unit of traffic analysis zones (TAZ) considering three transport modes (car, public transport, walking) with the support of real-time travel time collected from an internet map. The results show that spatial accessibility differed greatly under different transport modes and also had an uneven distribution pattern. Among these three results, the spatial variation of public transport accessibility was the highest. The results can provide references for urban planners in facility configuration and decision-making.

With the development of mobile positioning technology, a large number of Origin-Destination (OD) flow data with spatial and temporal details have been produced. These OD flow could give us a great opportunity to research geographical phenomena such as spatial interaction and mobility patterns. The OD flow clustering approach is an effective way to explore the main mobility patterns of the objects. At the same time, similarity measurement plays a key role in OD flow clustering. However, most of the previous OD flow similarity measurement methods failed to make full use of the spatial information of the flow including spatial proximity and geometric similarity. In this paper, we considered both position information and geometric properties of OD flow and propose a new method to measure the spatial similarity between OD flows. Specifically, the proposed method sets the neighbor threshold with the length of OD flows and the parameter α dynamically. Based on the constraint of the OD points’ location, the directions of the flows are implicitly restricted. The sole-parameter α has a practical value as it determines the maximum length difference and the maximum directional difference that can be tolerated between similar flows. The proposed method passed a simulation experiment with synthetic flows and a case study with 283,008 taxi trips in Beijing in one day. The results show that the proposed method can discover the dominant mobility pattern from a large number of flow data effectively. In the case study, the dominant flow clusters reveal the taxi mobility patterns of residents at different distances in Beijing.

Exploring urban travelling hotspots has become a popular trend in geographic research in recent years. Their identification involved the idea of spatial autocorrelation and spatial clustering based on density in the previous research. However, there are some limitations to them, including the unremarkable results and the determination of various parameters. At the same time, none of them reflect the influences of their neighbors. Therefore, we used the concept of the data field and improved it with the impact of spatial interaction to solve those problems in this study. First of all, an interaction-based spatio-temporal data field identification for urban hotspots has been built. Then, the urban travelling hotspots of Beijing on weekdays and weekends are identified in six different periods. The detected hotspots are passed through qualitative and quantitative evaluations and compared with the other two methods. The results show that our method could discover more accurate hotspots than the other two methods. The spatio-temporal distributions of hotspots fit commuting activities, business activities, and nightlife activities on weekdays, and the hotspots discovered at weekends depict the entertainment activities of residents. Finally, we further discuss the spatial structures of urban hotspots in a particular period (09:00–12:00) as an example. It reflects the strong regularity of human travelling on weekdays, while human activities are more varied on weekends. Overall, this work has a certain theoretical and practical value for urban planning and traffic management.