Where to Live in China?

Course: LA221 Quantitative Methods for Environmental Planning Professor: John Radke Yang Liu April 27, 2017

Where to Live in China?

A Livability Analysis of Environmental and Social Factors

Table of Content 1.

Problem + Goal 2.

Relevant Research and Literature 3.

Conceptual Model 4.

Data Collection 5.

Spatial Model 6.

Mathematical Model 7. Processes 8.

Result + Conclusion 9. References

1.

Problem + Goal

Today, livability has become a heated topic in China.

With the economic growth, urban sprawl, and worsening air quality, Chinese people are looking for cities- cities with strong economy, less pollution, high mobility, good educational opportunities, rich natural resources, and to start with, cities that are not overcrowded with people – to relocate to. These are only parts of what are feeding into the perception of whether a city is livable for the current and future generations of Chinese people, however, some of them can be major concerns.

Wuxi

The intent of the paper is to experiment with a model that can help evaluate livability across China with some of the aspects mentioned above. The data I can acquire is very limited within China, especially on demographics. Therefore, the goal is not to provide an accurate or usable evaluation of livability within this paper, but to test the methods and the parameters that can be used for various data I have, and a way to stitch them together into on suitability analysis.

This particular project is for personal learning process only.

However, the clients this type of model can potentially serve to include: government, research institute, individual researchers, educators, students, and the public.

2.

Relevant Research and Literature

Livability index is a subjective matter. The index factors applied in this paper are drawn from the related researches and related concepts, such as: AARP Livability Index(AARPLI), Urban Environmentally Livable Index for China(UELI), Sustainable Cities Index 2015 & 2016(SCI), Green City Index(GCI), Global Power City Index 2016(GPCI), Quality of Life Indicators(QLI), Happy Planet Index(HPI), and Canadian Index of Wellbeing(CIW).

Beijing

Some of the factors mentioned in these indexes are: AARPLI- Housing, Neighborhood, Transportation, Environment, Health, Engagement, And Opportunity UELI- Air, Soil, Water, Acoustic, Ecosystem, Natural Resources, GDP, Income, Tax Revenue, Health, Housing, Poverty, Education, Social Security and Safety SCI- Energy & Climate Action, Water, Waste, Materials & Resources, Transport & Accessibility, Biodiversity, Buildings,

Community & Culture, Local Economy, Health & WellBeing GCI- CO2, Energy, Buildings, Transport, Waste & Land Use, Water, Air Quality, Environmental Governance GPCI- Economy, Research and Development, Cultural Interaction, Livability, Environment and Accessibility HPI- Wellbeing, Life expectancy, Inequality of outcomes, Ecological Footprint; CIW- Community Vitality, Democratic Engagement, Education, Environment, Healthy Populations, Leisure and Culture, Living Standards, Time Use

3.

Conceptual Model

4.

Data Collection

For all the data, the datum is D_Xian_1980, the projected coordinate system is Xi_An_1980, the projection is Transverse Mercator, the linear unit is meter, and the angular unit is degree.

Layer

Type Source

Date of Data

GDP by Province

Polygon

ChinaMap

2010

Population Density

by Province

Polygon

ChinaMap

2010

Urban Areas

Polygon

ChinaMap

2011

Highways

Polyline

ChinaMap

2009

High Speed Railway Stations

Point

ChinaMap

2016

Airports

Point

ChinaMap

2010

Water Quality

Point

ChinaMap

2005

Notes GCP by Province (2010). Derived from Annual Provincial statistics at China Data Online: http://chinadataonline. org/member/macroyr/ (c) China Data Center This data is licensed for use by Harvard University, and is available for browsing only on ChinaMap service under the terms of an MOU between Harvard and CDC. For access and permission to use this data, Harvard Users may consult the Harvard Map Collection, non-Harvard users may refer to CDC. World Urban Areas (1:10 million) Source: Natural Earth Description: Urban areas worldwide as vector polygons.

Projection: WGS 84 Distribution point: http://koordinates.com/layer/1285world-urban-areas-110-million/ 2009 National Roads and Highways of China (2009) Src: 国家基础地理信息 系统 (2007) 测绘局, 中国地图公路交 通版 (2008). Edited by Lex Berman.

China High Speed Railway Stations, updated 2016. Compiled by Yifan LI, edited by Xuan Zhang and Lex Berman.

Georeferenced from public sources (primarily Google Earth basemap) and Airport Codes on Wikipedia (“List of airports by ICAO code: Z”). [Edited by Lex Berman, original locations compiled by Yifan Li.] Water Quality Monitoring Stations (2004). Source: Chinese Academy of Sciences, Earth System Science Sharing Network

Air Quality

Raw Data ChinaMap

2016

http://aqi.cga.harvard.edu/china/ ENIPEDIA Wiki, China Power Generation Sites. (note: some 1,982 locations had no Emission Intensity values... this is a rough snapshot of current information, for which more research is needed.

You can help by joining the Enipedia Project!). Citation: C.B.Davis, A. Chmieliauskas, G.P.J. Dijkema, I. Nikolic (2014), Enipedia, http://enipedia.tudelft. nl, Energy & Industry group, Faculty of Technology, Policy and Management, TU Delft, Delft, The Netherlands.

Edited from Chinese Ministry of Education, List of Chinese Higher Education Institutions (2010). Geocoding by Albert Wang. Edited by Lex Berman.

N/A

Power Plants CO2 Emission Intensity

Point

ChinaMap

2014

Higher Education

Point

ChinaMap

2010

Slope

Raster

N/A

N/A

Beijing

Source of Data

5.

Spatial Model

This livability evaluation model is essentially a suitability analysis synthesizing the environmental and social factors. The idea is to look at livability in a holistic way, instead of solely focusing on one aspect.

However, for individual users, if this model is converted into a interface where the different factors can be parameterized and customized by the user, then the person can select factors that he or she would like to take into account, and give some of the factors greater importance.

Creating this model starts with a typical data collection process. Most of the data is collected from the website ChinaMap, created by Harvard University. In ArcMap, the data is compiled, environments are set, and factors are defined as follows: Social opportunities are Higher Education Facilities (social welfare), Highways (mobility & accessibility: local and regional), High-Speed Railway Stations (mobility & accessibility: regional and national), Airports (mobility & accessibility: national and international), Urban Areas (amenity), Population Density (community & resources).

No environmental opportunity is used in this model, but potential ones can be: Natural Preserves, Lakes & Rivers, Vegetation Coverage, etc.

Social constraints is GDP (amenity and income). Other potential factors can be: Crime, Poverty, Dialects, etc.

Environmental constraints are Slope (safety), Power Plants (safety & health), and Air Quality (health). Other constraints may also include: Water Quality, Urban Heat Island, etc.

6.

Mathematical Model

The buffer distance are measured from the sample cities (Beijing, Tianjin, Nanjing) in Google earth. By studying the locations of the existing universities, railway stations, airports, and highways to determine the appropriate buffer distances. This is largely dependent on the real life experience living in these cities by the author. Living within the smallest buffer zone is considered most optimal for traveling to these facilities.

For power plants as a constraint factor, safety, noise, and emission are considered when setting an appropriate buffer distance. The number is taken from a related research.

For population density, the data is from 2010. An average of 500 people per square kilometer is used as a criteria. This number is calculated by averaging the population density in urban areas of China. If a city’s density is too high or too low compared to this number, both will be considered as constraints.

For GDP, similar method is applied. 45 per capita is the criteria number, a national average in 2010.

For a city, the lower the number goes, means the place can suffer from poverty and therefore lack of necessary amenities. If the number is exceedingly high, it means the living expenses can be beyond affordable. Both situation are deemed as constraints.

For Air pollution, the national standard criteria to rank air quality by PM 2.5 is used here for assigning proper weight to each range. Finally, for slope angle, a “universal standard� for evaluating suitability for building houses is used as the criteria.

Weight

4

3

2

1

0

Higher Education Facilities

50 km

150 km

250 km

350 km

350km+

Highways

4 km

8 km

12 km

16 km

16km+

High-Speed Railway Stations

10 km

18 km

26 km

34 km

34km+

Airports

25 km

35 km

45 km

55 km

55km+

Urban Areas

5 km

10 km

15 km

20 km

20km+

Population Density

400-600

300-399 & 601-700

200-299 & 701-800

100-199 & 801-900

100& 900+

Weight

1

0

-1

-2

-3

-4

-5

GDP

-

-

30-39

20-29

10-19

1,000+

10,000+

Slope

-

0-10%

10-15%

15-20%

20-25%

25%+

-

Power Plants

-

5km+

-

3-5 km

-

0-3 km

-

Air Quality PM 2.5

0-50

300-500

500+

50-100 100-150 150-200 200-300

7. Processes Except for the slope angle, which is already a raster data once acquired, all the other data were processed as feature classes.

For population and GDP, the data are already separated by provinces, so all the calculation is completed within the data table when the weights are assigned.

For buffer zones, each dataset has a distinct model built for it to generate buffer zones, but a larger buffer zone will have overlapping areas with each smaller buffer zones. Another interactive model is created to use to clip all the buffer zones for each one of the dataset, this interactive model will also union the clipped buffer zones and the extent of China to become one feature class with weights assigned to different areas. During this process, one layer: highway, however, cannot be completed because the feature class becomes too complex at one point and the computer can no longer handle the data (it failed to generate graphic during the step of clipping buffer zones).

Therefore in the final results, this layer is not included.

Finally, for the air quality layer, first the raw data is used to generate points, then an IDW interpolation with 50 sample points and cell size of 150 meters is used to generate the raster layer. The raster layer then is simplified in order to create a weight map by reclassifying.

Once all the feature classes are ready, then they are converted to raster layers with the same cell size of 150 (150 x 150) for calculation. Finally, a model with raster calculator will generate one opportunity map and one constraint map, then calculate again to generate the suitability map.

In the following pages are the maps created from each of the models, with an indication of the necessary models, code, parameters, and algorithms used to generate the maps.

Higher Education Facilities [Vector] In this model, an iterator is embed in the model to repeat the process of creating all the buffer zones. For each of the buffer zone, a field “Buffer_Distance“is created and the number entered in the field is the same with the buffer distance.

High Speed Railway Stations [Vector] In this model, an iterator is embed in the model to repeat the process of creating all the buffer zones. For each of the buffer zone, a field “Buffer_Distance“is created and the number entered in the field is the same with the buffer distance.

Airports [Vector] In this model, an iterator is embed in the model to repeat the process of creating all the buffer zones. For each of the buffer zone, a field “Buffer_Distance“is created and the number entered in the field is the same with the buffer distance.

Urban Areas [Vector] In this model, an iterator is embed in the model to repeat the process of creating all the buffer zones. For each of the buffer zone, a field “Buffer_Distance“is created and the number entered in the field is the same with the buffer distance.

Population Density [Vector] In this model, first a new field of population density is created and calculated by dividing the total population with the total area.

With each range of density, the areas that fall into this range is selected, and a weight is assigned to these areas. This process is repeated until all the areas are assigned with a weight number.

GDP (Per Capita) [Vector] In this model, the overall logic is the same with the “Population Density� layer. However, in order to make the model simpler, a python code is used to complete all the steps at once instead of actually building every step with the model.

Slope Angle [Raster] In this model, the weights are assigned by reclassifying the layer. Then the cell size is also reset to 150 meters in order to use this layer for calculation in the suitability analysis.

Power Plants [Vector] In this model, an iterator is embed in the model to repeat the process of creating all the buffer zones. For each of the buffer zone, a field “Buffer_Distance“is created and the number entered in the field is the same with the buffer distance.

Air Quality PM 2.5 [Vector - Raster] In this model, first the PM 2.5 hourly raw data from 2016 is displayed as points: Air_Quality. Then IDW interpolation is applied on these points to generate a raster surface showing the intensity of PM 2.5. Then the values are reclassified using the air quality classification standard by Chinese government (ranging from good to hazardous).

Highways [Vector] The iterator to create buffer zones takes too long to run for this layer because the layer is too complex. A regular model is created instead. However, the buffer zones cannot be futher processed due to the complexity. This layer isn’t used for the final analysis.

An Interactive Model...

This is a prototype to envision how the suitability analysis can be designed as a designer-friendly interface. For the purpose of preparing weighted layers for users to view and use, this model can be applied to any of the dataset made in this geodatabase to process the buffer zones: first to erase the overlapping areas by selecting the corresponding feature classes as indicated, then to union the buffers and the analysis boundaries, and finally a unique python code(Reclass) can be input manually to assign desired weights to each of the dataset. All the processing files including the final result can be saved to desired locations.

When all the weighted layers are ready to be use for analysis, they are going to be the layers displayed on the website. The designer can also parameterize the suitability model similar to this model, so that the users can choose the layers(factor) they would like to include for their livability evaluation, and also to give a second layer of heavier or lighter weight to different factors. For the purpose of this class, the suitability analysis model is not parameterized.

Higher Education Facilities Weight Map [Raster]

High-Speed Railway Stations Weight Map [Raster]

Airports Weight Map [Raster]

Urban Areas Weight Map [Raster]

Population Density Weight Map [Raster]

Composite Opportunity Map [Raster]

GDP Per Capita Weight Map [Raster]

Slope Angle Weight Map [Raster]

Power Plants Weight Map [Raster]

Air Pollution (PM 2.5) Weight Map [Raster]

Composite Constraint Map [Raster]

Suitability Map [Raster]

Location Map Recommendation Provinces with overall higher livability: 1. Guangdong Province 2. Zhejiang Province 3. Jiangsu Province 4. Shandong Province 5. Liaoning Province

8.

Result + Conclusion

In this model, social factors (including infrastructures) are set as important as the environmental factors. Also, due to limited data resources and time, the result of this project should not be considered an accurate evaluation for livability in China. However, a more precise and holistic evaluation can be generated for use if sufficient time and data are invested into a similar project with the modeling methods provided by this project.

With the result generated from this project, some reasonable assumptions can still be drawn on the possible dilemmas Chinese residents may encounter when they look for a livable city: On one hand, when evaluating livability not only on the environmental factors but also the social factors, such as amenities, education, accessibility, mobility, and infrastructure, the cities in the eastern part of China will generally outweigh the western part in almost every measure. This will give eastern Chinese cities a higher overall livability score, just as demonstrated in the suitability map. However, the environmental issues, air and water pollution particularly, is also much more worse along the eastern part of China. This creates a major dilemma when choosing where to live: a city with great amenities, lots of fun, but terrible air, or a city with great air, but not so much more to enjoy.

On the other hand, one may wonder: What does the rapidly worsening air pollution do to the overall vision of livability? As the map indicates, for the major cities such as Beijing and Tianjin, the perception of livability has changed, but maybe the realistic needs for living in such cities hasn’t changed that much. Why? Because with the rapid urban development still underway, major cities like these have more to offer in the social aspects, which will offset its disadvantages on the environment. Some of the more developed areas in the far west or north may gain popularity when accessibility and mobility are improved, such as Kuming, where it remains beautiful, natural, and relatively unpolluted. However, for the vast majority of the western part of China, the livability is still very low for its severe landscapes and climate condition, and there is a lot to catch up with in the social aspects, too.

What the worsening air pollution really affects, may very likely be the livability of the suburban and rural areas around those major cities in the eastern part of China. In the map, most of the amenities, infrastructures, and transit points are located in the major cities. For the areas falling out of the buffer zones of these cities, the livability level drops sharply. This results in a “oasis in the desert“ phenomenon, where a “green dot”(a highly livable city) lands in a yellow-reddish area (a region with low livability). This phenomenon may point to another troublesome dilemma in China: the government wants to develop its suburban and rural areas to bring economy to these less prosperous places, and to alleviate the stress on the urban population. But if the air pollution keeps building up in these areas and they lose the attraction of offering “less urban and healthier” environment for living, then why do people still want to relocate to these places?

Some provinces on the eastern coast are less affected by air pollution. Patches of green areas indicate that these areas can offer a variety of livable conditions, where a city is more urban, convenient, but less clean, or a suburban town that is less convenient but has cleaner air.

9. References 1. Empirical Study of Urban Environmentally Livable Index for China, YU Fang, Peng Fei, Cao Dong, Wang Jinan (Chinese Academy for Environmental Planning, 100012, Beijing, China), Jianglin (Beijing Academy for Environmental Science, 100037, Beijing, China), Ian V. Green (Culpin Planning Limited, Bristol, United Kingdom) 2. AARP Livability Index : https://livabilityindex.aarp.org/how-are-livability-scoresdetermined 3. Caring for Our Common Home: the Challenge, Suzanne H. Crowhurst Lennard, Ph.D.(Arch.)

4. Mobility: https://unhabitat.org/urban-themes/mobility/ 5. Sustainable Cities Index 2015 - Balancing The Economic, Social And Environmental Needs Of The World’s Leading Cities: https://s3.amazonaws.com/arcadis-whitepaper/arcadissustainable-cities-index-report.pdf 6. Sustainable Cities Index 2016 - Putting People At The Heart Of City Sustainability: https://www.arcadis.com/media/0/6/6/%7B06687980-3179-47AD-89FDF6AFA76EBB73%7DSustainable%20Cities%20Index%202016%20Global%20Web.pdf 7. The Green City Index - A Summary Of The Green City Index Research Series: https://www. siemens.com/entry/cc/features/greencityindex_international/all/en/pdf/gci_report_summary. pdf 8. Global Power City Index 2016 - http://mori-m-foundation.or.jp/english/ius2/gpci2/index. shtml 9. Happy Planet Index, http://happyplanetindex.org/ 10. Quality of Life Indicators in Context, Hazel Henderson, http://ethicalmarketsqualityoflife. com/ 11. Canadian Index of Wellbeing: https://en.wikipedia.org/wiki/Canadian_Index_of_Wellbeing