Urban Vitality Study on Chengdu Downtown with Spatial Data Analysis Abstract: People not only define the city but observe and participate in the city life. Because of the complex and ever-changing essence of the urban space, the relationship between the urban vitality and human activities has become an essential part of city planning. Recently, due to the development of the Internet, a large number of data are provided for the spatiotemporal analysis. Applying the POI data (point of interest data) and heatmap data in GIS, in this paper, I try to introduce certain factors such as the density of restaurants, entertainment and sports centers, companies, etc. to measure the urban vitality as the variations of human dynamics in Chengdu. Additionally, I try to figure out the characteristics of specific vibrant urban spaces. Keywords: urban vitality, human behavior patterns, data analysis
1. Introduction: With the urban sprawl and development, many kinds of research have been done to deal with urban issues, which makes city planners better understand people and their behavior. However, surveys and spatial information used in most studies were based on empirical observation or personal experience, causing detailed but limited data sets. The importance of quantitative measurements and analysis in studying the relationship between people and cities might be neglected within this context. However, recently, the increasing number of urban data against the backdrop of big data times has made more kinds of research possible. For instance, open data from public transit cards, business API and social media stimulate the quantitative analysis of urban design, transportation, and infrastructure planning. Thus, to integrate these data into the analysis helps to introduce an additional layer of information to the complex representation of what urban space is. This paper is mainly focuses on the relationship between human behavior and the vitality in the domain of urban development. Chengdu, one of the busiest and the most prosperous commercial hubs in China, is studied as a case. Applying the POI data (point of interest data) and heat map data to GIS, I try to introduce certain factors such as the density of restaurants, entertainment and sports centers, companies, etc. to measure the urban vitality as the variations of human dynamics in Chengdu. Additionally, I have made attempts to figure out the characteristics of specific vibrant urban spaces.
2. Concepts: 2.1 Urban vitality According to Towards A Theory of Urban Vitality, PR Maas, urban vitality is the synergy arising from a "variety" of somewhat "unique" commercial and entertainment opportunities, and a dense socially heterogeneous pedestrian population. In urbanism, vitality is a necessary factor for a bustling city, and it is primarily associated with safety, sustainability, and walkability in streets and other public spaces. In The Death and Life of Great American City, Jane Jacobs strongly connected the concept of vitality to that of diversity, where she was mainly concerned about the morphological diversity related to the built form of the city and the distribution of activities. She identified four measurements of diversity (mixed used, number of street intersections, and urban density) for evaluating vitality. They are essential and complementary to each other, altogether contributing to defining the degree of vitality of a certain venue. 1
Based on the analysis of the previous studies in academic fields and the status quo of Chinese cities, especially Chengdu, in this paper, the distribution of activities and specific traits of built form of the town, such as the public transportation and the land use would be utilized to evaluate the spatiotemporal continuity of human presence and activities. 2.2 Spatial data Spatial data refers to all types of data objects or elements that exist in a geographical space or horizon. Spatial planning relates to the methods and approaches used by the public and private sectors to influence the distribution of people and activities in various scales. It takes place in the land use, regional planning, transportation systems, and community planning, etc. Most data that city planners utilize are collected by automatically-generated data from sensors and other technological devices while people interact within the urban space. Numerous data sets have been employed in recent research, such as the public transit cards to calculate commuting flows, the mobile phone data to detect public activities and the social media data to figure out people’s comments and preferences of different locations. Considering the strengths and limitations of various data applications, I propose a quantitative method by using open data sets from Baidu POI (point of interest), Baidu Heatmap and primary resources of existing condition in Chengdu to carry out this research.
3. Methodology: The approaches and steps of this research are as follows. 3.1 To Select the scope of the research and obtain basic information through investigation; 3.2 To Collect related Baidu POI data and Baidu Heatmap; 3.3 Data cleansing to detect and correct (or remove) corrupt or inaccurate records from the data set; 3.4 Degree of aggregation of different POI: to process results of POI density analysis separately in ArcGIS; 3.5 Overlay analysis of all activities: to calculate all selected POI density (with a weighted average) in ArcGIS; 3.6 The relationship between commercial and business sites (POI density) and urban vitality: to compare the results of POI analysis with the Baidu Heatmap which displays the vitality of people in the city In this paper, POI data are used to simulate the mixing and density of business forms in the urban built environment, and Baidu Heatmap is used to show the spatial and temporal activities of the urban population.
4. Data description: 4.1 Data Sources Baidu Map POI data and the Baidu Heatmap data used in the research were provided by Professor Xu who was the instructor of this paper and shared with other students who carried out relevant studies in our senior year. 1. Baidu Map POI data: The number of POI data is about 165,800 pieces from 13 categories, including restaurants, scenic spots, civic facilities, shopping centers, education facilities, financial centers, commercial office buildings, sports centers and medical care, etc. in Chengdu downtown area, each of which contains many classes and subclasses. Each piece of POI data includes ID, category, address, latitude and longitude, and other information. 2. Baidu Heatmap data: Based on the geographic location of mobile phone users on the LBS platform, the Baidu Heatmap 2
data of Chengdu used by this research show different degrees of population distribution in time and space dimensions. The total number of heat map data spans for 111 hours (time intervals) from March 8, 2017, to March 12, 2017, which thoroughly covers the working days, holidays, days and nights of Chengdu downtown area. Baidu Heatmap offers urban studies a new perspective. The degree of crowds and population distribution reflects the land use and urban vitality to some extent. A series of heat map charts with time sequence also reflect the temporal and spatial changes of the people’s activities in a specific area. These characteristics and advantages mentioned above provide new possibilities for the study. 4.2 Data Cleansing and Coordinate Conversion For Baidu Map POI data, POIs labeled as “others” are removed from the land use mix analysis as this type of POIs with mixed information is not well organized and classified according to our review. I also employ manual checking of randomly sampled POIs to ensure the data quality. In ArcGIS analysis, the most recently developed and widely used datum is WGS1984. However, because of the restrictions on geographic data in China, the data I obtained are GCJ-02 (colloquially Mars Coordinates) which are not adequate for further calculation in the ArcGIS platform. The offsets of GCJ-02 can result in a 100 to 700 meters error from the actual location if a WGS-84 marker is placed on a GCJ-02 map, and vice versa. Therefore, coordinate conversion is needed to convert POI from GCJ-02 to WGS1984. The second step is to export the Excel table which covers attribute data(WGS1984) as a DBF file and to make a clean DBF file that ArcGIS can read. The third step is to convert the geodatabase exported in the second step to shapefiles and delete the data which are outside the Chengdu downtown area (research boundary). For Baidu Heatmap, first, the TIF map is projected into the Chengdu coordinate system by georeferencing. Second, the TIF data remote sensing image wave is selected and exported in ArcGIS to make the heatmap data available in ArcGIS analysis.
5. Analysis: 5.1 OpenStreetMap basemap OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. In this research, I use the transport OMS basemap of Chengdu downtown area in southwest China. This map style transport lanes like railways, buses and subway lines, which illustrates the urban structure and lays a foundation for the following analysis.
Figure 1. OSM Basemap of Chengdu Downtown
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5.2 Each POI and the aggregation of commercial and business sites In this study, from the 13 categories of POI data obtained, the data of 8 types of commercial service facilities closely related to the behavioral vitality in the city were selected, namely restaurants, scenic spots, municipal facilities, shopping centers, education facilities, financial centers, commercial office buildings, and sports centers. Kernel density analysis was performed on each type of POI to obtain corresponding raster data and distribution density maps of commercial and business sites. The POI density maps are shown below.
Figure 2. POI Density of 8 categories
In order to simplify the information and to group various categories of POI for the next step, I reclassified the values of the set of rasters to a common scale and grouped the values into ten specified intervals. The result of reclassification is shown below.
Figure 3. Reclassification_ POI Density of Different Categories
As seen from the above maps, Graph 1 shows that the distribution of restaurants decreases from the center of Chengdu. 4
Graph 2 indicates that the commercial offices are mainly distributed along the main roads and subway lines of the city, with the north-south direction being obvious. Graph 3: Shopping centers (including convenience stores, supermarkets, etc.) are densely distributed in the city center. The distribution of other is consistent with residential communities. Graph 4: The distribution of financial centers is similar to commercial offices. Graph 5: Scenic spots and other attractions are mainly concentrated in the city center, eg. Wuhou Memorial Temple and Kuanzhai Ancient Street of Qing Dynasty. Graph 6: Sports facilities (sports halls, gymnasiums, etc.) are mainly concentrated in the city center and local communities. Graph 7: The distribution of educational facilities is decreasing from the center of Chengdu. Graph 8: The spatial distribution of living municipal facilities are mainly in line with large local communities. 5.3 Overlay analysis of all POI sites Next, each type of POI is weighted and analyzed to combine the characteristics of several datasets into one. Different types of facilities have different shapes and influences on urban vitality. This study explains the degree of importance of different types of facilities through research on relevant reference papers and review checklist in city planning field. By comparing and simulating the degree of importance with Yaahp (a software that provides model construction, calculation, and analysis for decision-making processes), I determine weights of POIs by fuzzy quantization with the overlay analysis. The results are as follows:
Figure 4. Weights of POIs by Yaahp
Then, I assign values to different POI facilities in the overlay analysis according to different weights. (The calculation formula of the weighted superposition is as follows). P denotes the POI score of the superposed each facility; i denotes a certain type of POI facility; n denotes the number of facilities (eight in total); ai denotes the density of the POI facility, and mi denotes the weight of the POI facility.
P=
=
After the analysis of the weighted superposition results, it can be deduced that in the comprehensive evaluation of POI sites and their facilities, as shown in the following figure, the areas with higher distribution are: 1)
CBD -- Chunxi Road Pedestrian Street: This area is located at the inter-change station between two subway lines with high level of development and high building density. This area has a variety of commercial stores of various scales and sizes, from Isetan to 7-Eleven. Also, various types of cuisine cluster in this area, such as the international food, local snacks and street food. Various entertainment and sports facilities (gyms, cinemas, game rooms and KTV), education training center, financial office buildings are pretty dense as well.
2)
Dayuan International Center: This area is located on the west side of the city's main road. The facilities are mixed and dense. There are numerous kindergartens, primary schools, secondary schools, research institutes, shopping malls, cafes, 5
vegetable markets, convenience stores, banks, pharmacies and hospitals. 3)
CBD -- Hongpailou: Business types include sales of cars, building materials, digital, furniture and household appliances.
4)
Around Sichuan Gymnasium Station: This area is located at the inter-change station between two subway lines surrounded by primary schools, secondary schools, universities, city stadium and comprehensive hospitals.
5)
Residential Area -- Chadianzi Yumiao Road Community: It is an old community where all the accommodation was built in 1990s, but full of life services. It is quite convenient for people to live there, especially the senior citizens.
Figure 5. Overlay Analysis of All POI Sites
5.4 The relationship between commercial and business sites (POI density) and urban vitality (Baidu Heatmap) The Baidu Heatmap describes the real-time spatial distribution of the population to a certain extent, and further reflects the activeness of human behavior. This study employed the Baidu Heatmap data for two days in Chengdu: a weekday on March 8, 2017 (Thursday), and a weekend on March 11, 2017 (Saturday). The selected time is 8:00, 10:00, 12:00, 14:00, 16:00, 18:00, 20:00, 22:00 and 24:00. During the weekday, the population is mainly concentrated in urban commercial centers, financial centers, educational facilities (universities). The population in the high-density area in the morning and evening commuting are highly correlated with Chengdu's main roads and subway stations. At the peak of lunch and dinner, the data gathers at restaurants. Beyond the 6
office hours, most of the population is mainly concentrated in residential areas, research institutes, and universities. During the weekend, the population density is similar to that of the working day. However, due to the sluggish commuting, it is characterized by multi-center dispersion. The population activities are mainly in residential areas, large shopping centers, scenic spots, parks, etc. Additionally, the city is more energetic at the weekend than at the workday.
Figure 6. Baidu Heatmap
Comparing the results of Baidu Heatmap analysis with the results of POI density calculation, it is not difficult to find that the two are closely related. The higher density of POI means the higher population density. That is to say the more diverse the types of commercial and business facilities, the higher the density in space and the more intensive and dynamic the people are. 5.5 Characteristics of vibrant urban spaces After analyzing the city's highly dynamic space, I can initially find that these spaces have the following characteristics: Urban vitality is strongly tied to diversity, mainly associated with the distribution of activities, mixed land use, the age of buildings, reachability to public transportation and urban density. They are essential and complementary to each other, and function together to define the degree of diversity (and therefore vitality) of places. Thus, a mix of different activities works in synergy to attract people in the same urban spaces. In Chengdu, the places full of energy and activities meet the above characteristics. 6. Discussion: The analysis approach above results in some advantages, such as the high-resolution of spatial and temporal information, automatically collected for broader samples and contexts with no subjective bias related to the individual collection. However, it also showcases some relevant bias related to the data representativeness. The first limitation is that some automatically-produced data (in particular social media), can overestimate demographic groups that have access to specific 7
technologies. The second limitation is related to the use of POIs to estimate land use density. Our current approach focuses on the quantity rather than the quality of individual POIs (e.g., a large department store and a small convenience store are treated equally), which inevitably affects the research results. The further studies of urban space should further integrate data on urban land use, population, transportation, and environment for comprehensive analysis. Besides, further studies should take into consideration the boundary, scale, construction age, and usage status of the commercial and business sites.
References: Maas P R. Towards A theory of Urban Vitality[D]. 1961. Chen, Yilun, et al. "Cascaded pyramid network for multi-person pose estimation." arXiv preprint arXiv:1711.07319(2017). Sulis, Patrizia, et al. "Using mobility data as proxy for measuring urban vitality." Spatial Information Science 16 (2018): 137-162. What is Spatial Data? - Definition from Techopedia. https://www.techopedia.com/definition/871/spatial-data Spatial planning - Wikipedia. https://en.wikipedia.org/wiki/Spatial_planning
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