Evaluation of Accessibility to Urban Green Space in Beijing

Abstract

Availability and accessibility of green space are key indices of a rational and equal urban layout.

Knowing underserved areas lacking opportunities of accessing green space is critical for future urban

management. This study aims to evaluate the spatial distribution of urban green space in Beijing and

quantify its potential accessibility. Two approaches are used, network service area analysis and a

Gaussian-based two-step floating catchment area method. Result suggests that the spatial accessibility to

green space is not evenly distributed. Findings also provide consultative data for optimizing urban’s green

layout in the future.

1. Problem Statement

Urban green space, which connects human being and nature, is fundamental to livability of cities (Wolch,

Wilson, & Fehrenbach, 2005). Access to green spaces is important to physical activities and public health.

However, green space is often unequally distributed. It is therefore important to examine the equity in

access to green spaces and learn where to intervene. This study is inclined to offer analysis of spatial

distribution of green space in the urban area of Beijing, specifically, the six central districts, which are

Docheng, Xicheng, Chaoyang, Haidian, Fengtai, and Shijingshan, respectively.

The distribution and functionality of urban green space in Beijing has its own characteristics. On one

hand, rather than being under one unified plan, the existing urban green system is composed of hundreds

of parks built in different historical periods, such as the “three mountains and five imperial gardens” in

Qing Dynasty, and Beijing Olympic forest park recently built in 2008. Such historical factors may result

in uneven green distribution. On the other hand, Beijing is experiencing a complicated urban spatial

structure evolution due to population growth and redistribution. Therefore, evaluating green space’s

service efficiency is of great value for Beijing’s future urban management.

Since 1980s, planners and policy-makers widely took green area per capita, ratio of green space area,

and green space cover ratio, etc. as key indices to guide planning and management in China. Such indices

could reflect general characteristics of urban green space but fail to reveal the actual resource allocation

situation. This study will use GIS-based approaches for green space accessibility assessment. It aims to

offer an in-depth knowledge of green space distribution of Beijing by quantifying its accessibility.

2. Literature Review

There is a variety of GIS-based methods to estimate green space accessibility. One common approach is

to calculate the amount of green space within a predefined region (e.g., Potestio et al., 2009; Richardson

et al., 2010). Another approach is to measure access to green space using Euclidean distance or distance

along a road network (e.g., Comber et al., 2008; Coombes et al., 2010; Kessel et al., 2009). The third

approach is based on the gravity model (Hillsdon, Panter, Foster, & Jones, 2006). And the last is to

measure accessibility through considering supplies and demands as well as their interactions (Luo &

Wang, 2003; Wang, 2006; Dai, 2011).

LA221 Quantitative Method in Environmental Planning

Research paper for final project

Min Yuan

Urban population density is commonly considered positively related to the intensity of human

modification of the Earth’s surface. Thus remote sensing imagery has been widely adopted for population

estimation. Major techniques for population estimation by remote sensing include dasymetric mapping,

regression models and geostatistical models (Joseph et al., 2012).

Moreover, there are a couple of Chinese studies on green space accessibility. Yin and Xu (2009)

analyzed the accessibility of parks in Shanghai using Euclidean distance. Yuan and Xu (2015) analyzed

the accessibility of urban green space in Beijing based on GIS network analysis. However, the population

data in this study is at a census level and insufficient at adequate geospatial scales.

3. Solution

3.1 Concept model and study approach

As shown in ChartS1, a general concept model includes the processes of problem statement, identifying

input dataset, problem analysis, verifying analysis result, as well as implementing the result, serving as

the basis for building models for this study.

Chart 1. Concept model

“Chart 2. General flow chart

Green space access falls into two major categories: actual accessibility and potential accessibility, of

which the former emphasizes the actual use of green space and the latter highlights the availability of

green space in an area. This research measures the potential accessibility to green spaces. As shown in

Chart 2, the evaluation of the study is based on two processes, the network service area analysis and the

two-step floating catchment area analysis (2SFCA).

3.1.1 Network service area analysis

This analysis is aimed to calculate how many of the total population have access to green space, serving

as the first index for evaluation. The analysis is composed of three steps.

First, a Landsat image is classified to land cover using semi-automatic classification on the Quantum

GIS platform, for further disaggregating population data. The only population layer is a 2010 census tract

map. Such aggregate data doesn't reflect the actual distribution, and its accuracy cannot meet the higher

resolution analysis. To match the population data with physical elements, Landsat imagery is used. The

Semi-Automatic Classification Plugin of QGIS provides an interactive way to search, display and

download Landsat 8 images. Moreover, it allows semi-automatic supervised classification of remote

sensing images, providing tools to expedite the creation of ROIs, the pre-processing phases (image

clipping, Landsat conversion to reflectance), the classification process, and the post processing phases

(accuracy assessment, land cover change). Using this plugin, the image is classified into four land cover

classes (built-up, water, vegetation, and soil).

Secondly, a population distribution map is created using dasymetric mapping technique. Dasymetric

mapping means using ancillary data to disaggregate coarse resolution population data to a finer resolution

(Eicher and Brewer 2001). This study take the land cover map derived from Landsat imagery to

disaggregate the population. At the meantime, by converting the census map to a 30m×30m cell raster, it

achieves spatial down-scaling population simulation.

The third step is to identify the ratio of service population based on ArcGIS network analysis. A

network service area is a region that encompasses all accessible streets. Service areas created by network

analysis are converted to a raster and overlay with the disaggregated population distribution raster to

identify how many people are within the service area, and figure out the areas short of accessibility.

3.1.2 Two-step floating catchment analysis

In addition to distance, the amount of green space available and population demands are also critical

factors that influence access to green space. While network analysis may identify the neighborhoods short

of accessibility, the method encounters two issues. On one hand, people may not go to the closest green

space. On the other hand, the population pressure from different neighborhoods on the same green space

is not considered. Tackling the two issues requires consideration of two interactions-people from the same

neighborhood may visit multiple green spaces and a green space may have visitors from different

neighborhoods. Therefore, an accessibility measurement considering supplies and demands as well as

their interactions is desired. The two-step floating catchment area method (2SFCA), proposed in prior

research (Luo & Wang, 2003; Wang, 2006), is suitable. Using a catchment, it explicitly takes into account

resource suppliers and population demands and their interactions. However, the 2SFCA assumes uniform

accessibility within each catchment. Then Gaussian function was introduced in prior research (Dai, 2010)

into the 2SFCA to continuously discount the access within a catchment.

3.2 Data Resources and Projection

A Landsat image is obtained from the U.S. Geological Survey (USGS) (http://earthexplorer.usgs.gov/).

Road, park, city boundary shapefiles of Beijing are from National Geomatics Center of China.

Population data of 6th Census (2010) and census shapfiles are from China National Bureau of Statistics

and National Geomatics Center of China. Additional data for reference is obtained from Open Street Map.

According to National Standard for Basic Terminology of Urban Planning GB/T 50280—98 by

Ministry of Construction of the People's Republic of China, green space is defined as the area which

provides environmental, ecological, recreational and landscape functions. Public urban green space

system includes parks, green belts, specified green space and green buffers. In this study, the green spaces

used are the ones with size lager than 1 hectare. Green space dataset is revised according to the urban

green space data released from Beijing Municipal Bureau of Landscape and Forestry in 2014.

Figure1. Data: LC81230322015266LGN00, Beijing 2010 census data, green space, streets

3.3 Data Processing

3.3.1 Data Processing for network analysis

1) Landsat image classification for a land cover map

Figure 2. Land cover

2) Dasymetric mapping Chart 3. Flow chart

for Landsat image processing

P is the cell’s population. RA is the relative density by landcover type. PA is the proportion of cells within the census unit. N is

the actual population of the census unit. E is the expected population of census unit calculated using the relative densities. E

equals the sum of the products of relative density and the proportion of each landcover type in each census unit. AT is the total

number of cells that fall within each census unit.

Chart 4. Flow chart for dasymetric mapping

Figure3. Aggregate census map

Formula1 Dasymetric mapping

Figure4. Population distribution

3) Network service area analysis

Chart 5. Flow chart for network analysis

Figure5. Build a network dataset Figure6. Network service area analysis Figure7. Overlay with population map

3.3.2 Data processing for 2SFCA

Figure 8. 2SFCA

1st step

2nd step

At the first step, for each green space location, search all population locations that are within a threshold

distance, thus formulating the catchment for this green space. Populations are weighted using a Gaussian

function. Sum up the weighted populations within the catchment as the potential users for the green space.

The ratio of the green space to the populations is written as:

where Pk is the population at location k whose centroid falls into the catchment of green space location j; dkj is the distance

between population location k and green space location j; Sj is the capacity (size) of green space at j; G is the friction-of-distance

listed below:

At the second step, for each population location (census tract), search all green spaces within a

threshold distance, thus formulating the catchment for the census tract. Discount each R using the

Gaussian function. Sum up discounted R within the catchment to obtain the spatial accessibility at

population location. The accessibility of green space is written as:

where l is all green spaces within the catchment of population location i, and all other notations are the same as in Formula 2.

Accessibility score (Ai) suggests the amount of green space for every resident in this catchment.

Chart 6. Flow chart for 1st step of 2SFCA

Formula 2

Formula 3

Formula 4

Chart 7. Flow chart for 1st step of 2SFCA

Choosing the catchment size (d0) is important because it determines whether a green space is

accessible. In this study, the size is set to 2.5 kilometers, for the reason that the maximum walking time

human can afford is normally 30 minutes, and the average human walking speed is about 5 kilometers per

hour.

2SFCA method is unable to create an accessibility surface because the result is expressed as values

at the center points of census and these values are discrete. Kriging spatial interpolation can be used to

address this issue. Kriging assumes that the distance or direction between sample points reflects a spatial

correlation which explains variation in the surface. Thus, it is suitable to be used. The final accessibility

of the 136 census tracts is classified to 5 classes using Jenks Natural Breaks Classification method. This

data clustering method is used for this study because it is designed to determine the best arrangement of

values into different classes by reducing the variance within classes and maximizing the variance between

classes.

4. Results

4.1 Result of network analysis

To the threshold distance of 2500m, the service area accounts for 27.9% of the study area; the service

population accounts for 48.6%. Ratios of service area of Dongcheng, Xicheng, Chaoyang, Haidian,

Fengtai and Shijingshan are 92.9%, 78.1%, 28.2%, 27.0%, 51.8% and 4.8%, respectively. Ratios of

service population are 93.4%, 76.4%, 40.4%, 54.2%, 28.4% and 24.3%, respectively.

Table 1. Network analysis result

Figure9. Service area with 6 districts and 136 census tracts

As shown in figure 9, results indicate that green space is unequal in different districts in Beijng. That

is the green distribution of central urban area is better than that of fringe area. And Dongcheng District is

the best area among all the six districts. Spatial differences of all the census tracts are also remarkable.

Green space is excessive in a few census tracts while insufficient in most census tracts. On the whole,

urban green space of Beijing can only meet the need for not a half of all the dwellers.

4.2 Result of 2SFCA

As shown in figure 10, the study result suggests that the overall layout of the green space in Beijing is

relatively irrational and unreasonable. There is a spatial mismatch between green space and population.

Low accessibility is associated with most urban area, while higher access is present in socioeconomically

advantaged areas. The two high-value clusters are actually areas where relatively low density

communities with high housing price concentrate. It suggests that neighborhoods with a higher

concentration of wealth have significantly greater access to green spaces.

As shown in Table 2, it is also found that the spatial pattern of green space accessibility displays

strong polarization characteristics. More than 70% of the census tracts have lower green space

accessibility than the average level around the city, while only a few exhibits higher accessibility.

Table 2. Accessibility from 2SFCA analysis

Figure 10. Accessibility map

5. Discussions and Conclusions

Opportunities to access green spaces have critical implications to active physical activities, public health,

and environmental justice (Boone et al., 2009; Coombes et al., 2010; Coutts, Horner & Chapin, 2010).

Findings of this research are of significance for city administrators to regulate and adjust the spatial

distribution of existing green space. Moreover, the GIS-based approaches used in this research have a

number of advantages and merits. First, the network service area analysis reveals the travel friction better

than the Euclidean distance in previous study on green space of Beijing. Secondly, the two-step floating

catchment area method considers not only the surrounding green space availability, but also the

population demands from the surrounding neighborhoods. Thirdly, the models built in model builder for

dasymetric mapping and two-step floating catchment area analysis can be extended to target more

research processes on this study in the future.

This study has a number of limitations which could be addressed in the future studies. First, the users

of green space are generalized without considering the differences in needs, preference, and behavior

among population groups. Green space accessibility for different age, ethnic, income and occupation

groups should be assessed separately in future studies. Secondly, in this study size is used as the single

indicator for the service ability of each green space. In fact, factors determining the attractiveness of

individual green space are more complex. Thus more indicators to describe green space should be

included. Thirdly, the accessibility is based on walking distance. Other travel modes such as private

vehicle, biking, and public transit are necessary in order to provide a complete understanding of green

space access. At last, the data limitation leads to less accuracy in the dasymetric mapping process which

mainly uses a land cover map only consisting of four classes. Rather than the only “built-up” class for

clustering population, which actually has merged buildings, roads and hard-pave public spaces, more

classes such as high intensity and low intensity should be obtained in the future studies.

In summary, this study uses GIS-based approaches to quantify spatial accessibility to urban green

spaces of Beijing. The findings can assists in delineating shortage areas to target and evaluating the

justice of green space improvement so as to ensure the equity in accessibility within the whole cities.

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