Proceedings of the Fábos Conference on Landscape and Greenway Planning Volume 6 Adapting to Expanding and Contracting Cities Article 58 2019 Study on Greenway Network Planning Based on Big Data Social Behavior and Urban Potential Space Framework Xixi Chen Beijing Forestry University, 841311055@qq.com Liang Li Beijing Forestry University, 67430565@qq.com Follow this and additional works at: https://scholarworks.umass.edu/fabos Part of the Environmental Design Commons, Geographic Information Sciences Commons, Historic Preservation and Conservation Commons, Landscape Architecture Commons, Nature and Society Relations Commons, Remote Sensing Commons, Urban, Community and Regional Planning Commons, and the Urban Studies and Planning Commons Recommended Citation Chen, Xixi and Li, Liang (2019) "Study on Greenway Network Planning Based on Big Data Social Behavior and Urban Potential Space Framework," Proceedings of the Fábos Conference on Landscape and Greenway Planning: Vol. 6, Article 58. Available at: https://scholarworks.umass.edu/fabos/vol6/iss1/58 This Article is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Proceedings of the Fábos Conference on Landscape and Greenway Planning by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact scholarworks@library.umass.edu.
Chen and Li: Study on Greenway Network Planning Based on Big Data Social Behavior Study on Greenway Network Planning Based on Big Data Social Behavior and Urban Potential Space Framework Xixi Chen 1, Liang Li 1 1 Beijing Forestry University, (No.) 35 E(ast) Qinghua Road, Beijing and 100083, China Abstract Chinese cities are facing urban transformations, and urban greenways should be highly-ranked in future urban construction. While the selection of urban greenways is influenced by complicated factors, such as the existing urban land use layout, the network structure and the usage demand. At present, Chinese greenways route selection is mainly based on suitability analysis of the value and potential of urban land use, but rarely related to the daily use demand of citizens. In particular, there is a lack of consideration of the two key factors: the conversion potential of urban land and the usage demand of urban residents. Under the new big data environment, this paper uses big data to explore a greenway network planning model, which consists of GIS spatial analysis (evaluation of potential space of urban greenway) and social behavior analysis of big data (evaluation of use demand of urban residents). From the perspective of urban greenway potential space evaluation, including: 1) high frequency use space, evaluated through urban public space vitality and urban green space landscape vitality; 2) Unutilized space that can be transformed, namely river channel, railway and protective greenbelt. From the perspective of social behavior analysis of big data, including: 1) usage demand analysis of existing park and built greenway based on big data of Web review. 2) bicycle's usage demand analysis of greenway node space and linear space by shared bicycle OD data and trajectory data. Linear space and point space can be drawn from the above analysis. The linear space is used to establish the cost grid of greenway suitability, while the point space is used to establish an evaluation index system and select greenway nodes. Then use minimum cost path algorithm to generate the preliminary scheme of greenway selection by connecting nodes and cost grid in ArcGIS. Finally, combined with big data analysis of greenway usage demand, the structure and function of the greenway planning is optimized to form an overall urban greenway network system. Under the background of new technology development, a controllable and quantitative data model of urban greenway selection is explored. Key words: urban greenways, big data of social behavior, spatial potential, route selection, GIS suitability evaluation, construction mode Introduction With the rapid development of modern cities, Chinese cities have experienced unprecedented rapid development and expansion, with an ever-increasing area and population, rapid development of various types of urban land, rapid reduction of internal open space, serious encroachment on surrounding land, and increasing fragmentation of land..the Greenway provides a new green infrastructure that creates a network of open spaces and links people and venues, and provides a new development model for future urban renewal and sustainable land use [1]. The importance of urban greenways in urban areas, building Published by ScholarWorks@UMass Amherst, 2019 1
Proceedings of the Fábos Conference on Landscape and Greenway Planning, Vol. 6, No. 1 [2019], Art. 58 urban-type greenway networks that are closely related to residents' lives can effectively improve urban traffic conditions, improve residents' quality of life [2], and promote urban lifestyles dominated by slow behavior. As the main way to optimize the spatial structure of urban stocks [3-5]. This study mainly divides the influencing factors of greenway route selection into two parts: demand and carrying space potential. Try to combine spatial potential and social behavior big data analysis to explore new ways of urban bicycle greenway route selection planning. Background and Literature Review Since the 1990s, China has entered the stage of rapid development of greenway research [6-8]. Today, with the continuous advancement of China's urbanization process, urban development is shifting from highspeed expansion to refined renewal [8], and the green road planning and construction approach will also produce new changes. According to the spatial distribution type, the greenways are mainly divided into three types: area-type, city-type and community-type. [9]. Compared with other types of greenways, the urban greenway system needs more consideration of the dual structure of physics and function. It is more constrained by space and more closely related to social life. How to choose the right space for building greenways in a complex urban land environment becomes a problem that needs to be considered and studied. Under the background of new data, the actual situation of social life can be reflected through a large number of personal mobile devices and Internet data through the four-dimensional scale study of space-time behavior, can effectively reflect the current operating mode of society [12-13].And with the increasing effectiveness of relevant data sources, accuracy, and coverage of people, the application prospects are huge. Urban greenway route selection technology framework based on spatial potential and social behavior big data Under the new big data environment, this paper uses big data to explore a greenway network planning model, which consists of GIS spatial analysis (evaluation of potential space of urban greenway) and social behavior analysis of big data (evaluation of use demand of urban residents). From the perspective of urban greenway potential space evaluation, including: 1) high frequency use space, evaluated through urban public space vitality and urban green space landscape vitality.2) Unutilized space that can be transformed, namely river channel, railway and protective greenbelt. From the perspective of social behavior analysis of big data, including: 1) usage demand analysis of existing park and built greenway based on big data of Web review. 2) bicycle's usage demand analysis of greenway node space and linear space by shared bicycle OD data and trajectory data. Linear space and point space can be drawn from the above analysis. The linear space is used to establish the cost grid of greenway suitability, while the point space is used to establish an evaluation index system and select greenway nodes. Then use lowest-cost path algorithm to generate the preliminary scheme of greenway selection by connecting nodes and cost grid in ArcGIS. The lowest-cost path model is based on the cost-weighted distance function, combined with the connection node and the cost grid to obtain the minimum cumulative cost distance and cost distance direction from each grid unit to its nearest node, thereby obtaining the corresponding connection nodes. https://scholarworks.umass.edu/fabos/vol6/iss1/58 2
Chen and Li: Study on Greenway Network Planning Based on Big Data Social Behavior Finally, combined with big data analysis of greenway usage demand, the structure and function of the greenway planning is optimized to form an overall urban greenway network system. Under the background of new technology development, a controllable and quantitative data model of urban greenway selection is explored. (Figure 1) Figure 1: Technical framework for urban greenway routeselection based on spatial potential and social behavior big data 3. Urban greenway network identification 3.1 Spatial potential analysis of urban greenway route selection In the urban areas that have been built, it is actually very difficult to find space to build a coherent, highquality urban green slow-moving network. The most coherent cyberspace in modern urban space is the modern urban infrastructure network, such as existing roads, railways and canals, and various types of protective green spaces. Because of the emphasis on a single functional utilization model, its space has a huge composite. With the potential for retrofitting [14], these spaces can be used as potential space for urban greenway construction. With the change of the concept of road rights development, chronic priority has gradually become a consensus. For example, Barcelona urban planning focuses on combining green space networks with slow- Published by ScholarWorks@UMass Amherst, 2019 3
Proceedings of the Fábos Conference on Landscape and Greenway Planning, Vol. 6, No. 1 [2019], Art. 58 moving systems, using existing urban infrastructure corridors, and compounding existing urban spaces. Utilize, rebuilt the city's car dealership and slow-moving "dual network layout" mode. The construction of the Barcelona city's slow-moving network mainly utilizes the existing linear infrastructure space such as roads, railways and canals, and through creative design, these spaces are transformed into multifunctional green connection axes to realize its transition from motor vehicle priority to slow priority. It enhances the comfort of slow travel and promotes the reconstruction of the urban green space network. The research focused on the use of infrastructure line space in the greenway of Haidian District, Beijing, including road space, river channels and surrounding green space, railways and surrounding green space. These spaces are faced with problems such as single function, low landscape quality, and lack of slow function. The existing roads are all priority for motor vehicles. The urban rivers and rivers are also hard rivers. The main functions are mostly flood discharge. The riverside parks are built in a small number of river sections, but they are relatively independent and lack contact with the surrounding areas. The railway function in the region brought about by the development of the city gradually shifted to the outside of the city. Some railways were transformed into the ground, and some ground space could be withdrawn to carry out the construction of the greenway. (Fig. 2-4) Figure 2: Distribution of protective green space Figure 3: Distribution of water system Figure 4: Distribution of railway 3.2 Urban Greenway Usage Demand and Social Behavior Big Data Analysis Since the 21st century, with the rapid development of ICT (Information and Communication Technology), which is represented by social networks, Weibo, WeChat, and location-based services, the city is generating and transmitting massive amounts of time. Information data [15-16], data size and value [17].The application of big data in multidisciplinary fields has become a hot topic of research today [18-20]. In terms of urban traffic flow, it mainly uses data such as bus credit card, subway, taxi track, mobile phone map, mobile phone mobile signal base station, etc. to study the daily travel mode, commuting route, traffic https://scholarworks.umass.edu/fabos/vol6/iss1/58 4
Chen and Li: Study on Greenway Network Planning Based on Big Data Social Behavior and travel and land use relationship of urban residents [21-25]. Such as the use of network evaluation messages and other big data crawling for word frequency analysis, semantic analysis and so on. 3.2.1 Research on trajectory big data for residents' daily travel and high frequency use space The OD node data and trajectory data of the bicycle can reflect the work of the citizens and the space for shopping travel. It reflects the public's demand space very well. Through the analysis of OD data, the spatial distribution characteristics of shared bicycle travel are obtained, reflecting the main demand area [26] of residents' bicycle travel. Using ArcGIS software, the study area is meshed and divided into 10 meters by 10 meters grid, the number of shared bicycles in each unit grid is counted, and the grid of the number of starting bicycles in the study area is obtained. Through visual data analysis, a daily heat distribution map of shared bicycle use space is generated (Fig. 5). The trajectory data is data information obtained by sampling the motion process of the moving object, and the shared bicycle trajectory data is matched to the road network, and the GPS trajectory points are matched to the actual road network [27].The shared bicycle track data is used to obtain the road use preference selected by the current bicycle travel. The data visualization analysis is performed by Arcgis, and the road space heat distribution map of the shared bicycle is generated (Fig. 6) to obtain the high frequency spatial distribution of the shared bicycle use road. Figure 5: Distribution of Shared bikes origin destination Figure 6: density distributions of shared bicycle using road Published by ScholarWorks@UMass Amherst, 2019 5
Proceedings of the Fábos Conference on Landscape and Greenway Planning, Vol. 6, No. 1 [2019], Art. 58 3.2.1 High frequency use space analysis As an important connecting corridor in the city, Greenway needs to meet the various needs of the city, including the basic commuting and urban life functions of the citizens. It reflects the strong practicality and convenience, and can meet the residents' use of slow traffic. The way to reach the actual needs of a variety of destinations such as work and life. (1) Analysis of public space vitality The study first divided the public space vitality into four types of commercial, office, medical and cultural attractions. Climb four types of urban POI (point of interest) data, use ArcGIS software to visualize POI data distribution, and obtain four types of public space vitality distribution density thermograms, through AHP (Analytic Hierarchy Process analytic hierarchy process) Analysis, combined with 20 gardening industry professionals, scored four types of public space vitality, namely commercial 0.33, office 0.30, medical 0.18, culture 0.19, with the weight of four types of public space dynamic distribution density Superimposed, and finally obtained the thermal map of the public space vitality distribution in Haidian District. (Fig. 7) Figure 7: Thermal map of public space vitality distribution (four types of public spaces are commercial, office, medical, and cultural) (2) Evaluation of urban green space space vitality The high-tech heat map uses the acquisition of mobile phone positioning to reflect the number and density of users in a certain area in real time, and obtain the coordinate information of the data. By analyzing and collecting the high German heat map in the same time period of each green space in Haidian District, the population activity intensity of each green space in the same time period can be obtained, and the activity intensity of each green space is assigned by using gis software. The map of the dynamic level of green space in Haidian District is obtained. The more people use the green space in the same time period, the higher the vitality level. (Fig. 8) https://scholarworks.umass.edu/fabos/vol6/iss1/58 6
Chen and Li: Study on Greenway Network Planning Based on Big Data Social Behavior Figure 8: Urban green space spatial vitality distribution map 3.3 Urban Greenway Planning Model Construction Through social behavior analysis and spatial utilization potential analysis, the urban greenway social behavior big data recognition model is constructed. The linear space evaluation is selected to establish the greenway suitability cost grid; the point space is established to evaluate the index system for evaluation. Greenway node; using the lowest cost path algorithm to generate a greenway preliminary route selection scheme in ArcGIS to form an overall urban greenway network planning system. 3.3.1 Selection of important connecting nodes of greenway The ArcGIS software is used to reclassify the shared bicycle node space after the shared bicycle 0D data processing using the heat distribution map and the high frequency use spatial distribution map, and divide it into ten grades, each assigned a value of 1-10.In the route selection model of this study, the influence of high frequency use space and citizen bicycle use demand on the greenway route selection is 0.5, which will share the space heat distribution characteristics of the bicycle destination and the potential space of the high frequency use space node. The hierarchical distribution features are analyzed by equal value superposition analysis to obtain the suitability distribution map of the greenway connecting nodes (Fig. 9). Finally, through the data selection of Arcgis, the area with the suitability level of the greenway connection node is selected, and each connection node is set up, and finally 47 connection nodes are determined (Fig. 10). Published by ScholarWorks@UMass Amherst, 2019 7
Proceedings of the Fábos Conference on Landscape and Greenway Planning, Vol. 6, No. 1 [2019], Art. 58 Figure 9: Suitability distribution map of greenway connection nodes Figure 10: Selection of greenway connection nodes Figure 11: Spatial suitability map of the greenway selection corridor https://scholarworks.umass.edu/fabos/vol6/iss1/58 8
Chen and Li: Study on Greenway Network Planning Based on Big Data Social Behavior 3.3.2 analysis of space utilization potential of green corridors The ArcGIS software is used to reclassify the shared bicycle linear space usage heat distribution map and the unused convertible linear spatial distribution map after the shared bicycle track data processing, and the same applies to the public bicycle bicycle travel road heat level distribution characteristics and unused. The convertible linear spatial distribution map is equally superimposed to obtain the suitability distribution map of the greenway selection route (Fig. 11). The available urban linear public space is selected as the carrier space of the greenway from the complex urban land environment, which satisfies the possibility of urban greenway route selection, and establishes the greenway selection cost grid based on the cost path model. 3.3.3 Establishing the lowest cost path Using the cost path tool in gis to identify the 47 important greenway connecting nodes as the starting point and the ending point, and using the cost-weighted distance function by sharing the cost of the road using the road heat and the green space of the green space, get the lowest cost path between the corresponding connection nodes (Figure 12). Thus, the greenway selection planning is obtained (Figure 13) Figure 12: Calculation of the lowest cost calculation results for urban greenway route selection Figure 13: Urban Greenway Route Selection map 3.4 Optimization of Urban Greenway Planning Model As the Haidian District of the study area is in an excessive area of the urban center of Beijing to the suburbs, the development is uneven, and the development of the central area is faster. The citizens use Published by ScholarWorks@UMass Amherst, 2019 9
Proceedings of the Fábos Conference on Landscape and Greenway Planning, Vol. 6, No. 1 [2019], Art. 58 more, and the more the area is less energetic, but considering the fairness of urban development and making up for big data. The one-sidedness of the study was optimized for the model. In underdeveloped areas, by setting different selection indicators and selecting nodes, the shortcomings of simple computer calculation results are made up. (Figure 14) Figure 14: Selected point map of the greenway connecting node after optimization 3.5 Greenway route selection results The planning and construction of urban greenways should pay more attention to connectivity. Based on the greenstone minimum cost path obtained by big data combined with ArcGIS analysis, consider the objective constraints of the existing road network and building layout of the city, and make reasonable adjustments. Make it a coherent, multi-functional, vibrant green network that maximizes the commute needs of the citizens and guides healthy green travel. The preliminary route selection map for the urban greenway in Haidian District was finalized (Figure 15). Considering the factors such as the current land use conditions, residents' leisure needs, and different levels of the current roads, the overall greenway system is organized and optimized, and divided into two different levels of greenway systems, namely the integrated greenway system and The commuter greenway system eventually formed the overall greenway route selection plan for Haidian District (Figure 16).The two systems, while ensuring the continuity of the greenway, from the different perspectives of the existing natural environment, road system, landscape features, commuting needs, and walking experience, have https://scholarworks.umass.edu/fabos/vol6/iss1/58 10
Chen and Li: Study on Greenway Network Planning Based on Big Data Social Behavior given the Greenway different functional features in the future design implementation process. It will also be treated differently. Fig. 15: Calculation of the lowest cost calculation result of the urban greenway route selection after optimization; Fig. 16: Optimized urban greenway route selection map; Fig. 17. Map of greenway system consisting of an integrated greenway system and a commuter greenway system (1) The integrated greenway system To build a integrated greenway based on the selection of green lanes that have large flow of bicycles, can connect existing high-quality park green spaces. The integrated greenway meets the needs of daily cycling and jogging. It also integrates existing fragmented green space, connects various important park green areas, and provides ecological service functions. It also provides recreational facilities in the greenway system to meet residents' daily fitness. entertainment, social and other needs. The overall integrated greenway system has formed a multifunctional system that integrates ecological landscape features, historical culture, social economy, and citizen commuting functions. (2) The commuter greenway system We sort out the other lines that have a large flow of existing bicycles, mainly responsible for work and life commuting services, and plan commuter-type greenway systems. The system makes full use of highfrequency use of urban secondary roads and branch roads to meet the current commuting needs, thereby improving residents bicycle travel experience and promoting daily low-carbon travel of urban residents, which alleviating the growing pressure of urban private motor vehicles. Conclusions This article analyzes the spatial distribution of cycling in Haidian District through shared bicycle big data, mapping the spatial characteristics of citizens cycling. At the same time, based on the characteristic that green space corridors are the main space carriers for the construction of urban greenways, the appropriate spatial distribution characteristics of routes plan for urban bicycles greenway are explored. The research uses big data to analyze social behaviors, and implement a scientific and precise greenway route selection Published by ScholarWorks@UMass Amherst, 2019 11
Proceedings of the Fábos Conference on Landscape and Greenway Planning, Vol. 6, No. 1 [2019], Art. 58 plan from the bottom up, which provides all new approach for greenway route selection planning. In the context of urban renewal and smart development, this new method can be enlightening both theoretically and practically. References [1]FABOS J G. Introduction and Overview: The Greenway Movement, Uses and Potentials of Greenways. Landscape and Urban Planning, 1995,33:1-13. [2] zhang tianjie, li ze. Multi-objective greenway network for high-density cities -- Singapore park link system [J]. Urban planning,2013,05:67-73. [3]Qie guangfa, wang cheng, zhan xiaohong. Characteristics and enlightenment of slow greenway construction in Finland [J]. Chinese journal of urban engineering,2012,04:57-59. [4] wang zhaolin, he fang. On urban greenway planning in interaction with cities [J]. Urban planning,2012,10:34-39. [5] CAI ying, he fang, li yingyi, kang kaishan. Thoughts and planning methods of greenway network integrated into urban and rural areas [J]. Planners,2011,09:32-38. [6] jack ahern, zhou xiao. On principles and methods of greenway planning [J]. Landscape architecture, 2011: 104-107. [7] qin xiaoping, wei min. A comparative study of Greenway in China and Greenway in the United States [D]. Chinese garden, 2013. [8] wang zhaolin, he fang. Tentative discussion on urban greenway planning with urban interaction [J]. Urban planning, 2012: 250. [9] lai shouhua, zhu jiang. Community greenway: a new trend of greenway construction in compact cities [J]. Landscape architecture,2012(3):77-82. [10] zhou conghui. Method of greenway layout in central city based on quantitative evaluation of line selection potential [J]. Chinese garden, 2016: 104-109. [11] zhen feng, liu xiaoxia, liu hui. Regional urban network under the influence of information technology: a new direction of urban research [J]. Human geography,2007,22(2):76-80. [12]Chen jun, jiang jie. Spatial data modeling, processing and analysis of multi-dimensional dynamic GIS [J]. Journal of wuhan university of surveying and mapping,2000,03:189-195. [13]Zhou yan. Virtual geographic environment and spatial data visualization [J]. Railway survey,2004,06:11-14. [14] Conine A, Xiang W-N, Young J, et al. Planning for multi-purpose greenways in Concord, North Carolina[J]. Landscape and Urban Planning, 2004, 68(2-3): 271-287. [15] meng xiaofeng, cixiang. Big data management: concept, technology and challenge [J]. Computer research and development,2013,50(1):146-169. https://scholarworks.umass.edu/fabos/vol6/iss1/58 12