Zhao,Jianting_EE

How Topography Has Affected Beijing on Its Urban Sprawl | LARP 743 Midterm Project | Jianting Zhao | Oct. 24th 2016
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How Topography Has Affected Beijing on Its Urban Sprawl
Interest in This Topic
Growing up in Beijing, it is always been the most
important city in my mind. Its civic development
matters to millions citizens.
In recent years, Beijing is growing exponentially.
But the growth in uneven. Where has grown the
fastest, and where the slowest? We will find out in
this project.
Topography of Beijing
From the Digital Elevation Model (DEM) we can see that the northeast, north, northwest, west and southwest
parts of Beijing are higher in elevation and the central, southern and eastern parts are flat. The urban core of
Beijing is developed in the flat area.
Developed
area
Medium
Developed
area
Less
Developed
area
Under
Developed
area

Urban Development Over 2 Decades
The shapefiles of the urban sprawl from 1993 to 2012 and the Night Lighting from the same time span are displayed. There is a close relationship between urban
sprawl and stable lights. However, the shapefiles are able to show urban development beyond Beijing as well, whereas their counterparts in lighting are clipped.
Annual Urban Sprawl Annual Stable Lighting
1993 1994 1995 1996 1993 1994 1995 1996
1997 1998 1999 2000 1997 1998 1999 2000
2001 2002 2003 2004 2001 2002 2003 2004
2005 2006 2007 2008 2005 2006 2007 2008
2009 2010 2011 2012 2009 2010 2011 2012

Overlay Comparison
Urban Sprawl Overlay from 1993-2012
It is apparent that the flatter area are occupied by people and development.
More importantly, the urban sprawl also goes beyond Beijing’s municipal
boundary towards east, southeast and south.
Stable Lights Overlay from 1993-2012
The lighting overlay almost fills up the low elevation area in Beijing. The
pattern is very similar to that of urban sprawl. It is easy to see a correlation
between these two properties.

Rate of Urbanization
After confirming the close relationship between
urban sprawl shapefiles and stable lights images,
I used the image collection to conduct further
analysis, because there are more methods available
to work with image collection in Google Earth
Engine.
Rate of Urbanization is calculated by adding the
pixel values of each year and reducing to an image
with the sum of all pixel values over time span (as
shown below). The darker green indicates higher
pixel value, and hence longer time of stable lights.
Afterward, I reprojected this sum image to
EPSG:3857 -- WGS84 Web Mercator (Auxiliary
Sphere) projection. By doing this projection, I was
able to use ee.Terrain.slope to calculate the slope
of that sum image, such that I can get the rate of
urbanization over 2 decades (Right Image). The
slope is calculated such that the steeper slope
(darker blue) represents less sprawl over unit time,
versus flatter slope (lighter blue) indicates more
sprawl over unit time.
More Stable Light
Faster Sprawl
Less Stable Light
Slower Sprawl
The area in the mountain region has darker colors, indicating slow urban sprawl. In contrast, the area on plain has
bigger urban sprawl.

Other Approaches of Analysis on Urbanization
Summarizing Urban Sprawl by Standard Deviation
Over 2 decades, the lighting has changed a lot, and it is in general, from low
light intensity to high intensity. Therefore, one way to measure the urban sprawl
is to measure how unstable the light intensity is over the years. If it is big, that
means it’s been developing and vice versa. The central urban core has small
number, because it is lit up throughout the time span, whereas the mountain
area has small number because there isn’t much development.
Summarizing Urban Sprawl by Mean
This method produces similar result to the sum image. In this mean image,
each pixel represents the mean value of stable light intensity over the time
span. The urban core, where is always lit up, has the highest mean value, and
the less development the less pixel value in the rest of the area.
More Change In
Light Intensity More Stable Light
Less Change In
Light Intensity
Less Stable Light

/////////// Load Data /////////////////
var BJdem1 = ee.Image(“users/zhaojianting/MidTerm/ASTGTM2_N39E116_dem”),
BJdem2 = ee.Image(“users/zhaojianting/MidTerm/ASTGTM2_N40E116_dem”),
BJ = ee.FeatureCollection(“ft:1y1yODcot22kvYzeKKuZllMro1fkzSQf2ozfF0Qnw”),
BJ1993 = ee.FeatureCollection(“ft:1Gr-KhI32ffFCDLgBKIIF9y2rkWQIMDp-zDqp8At4”),
BJ1994 = ee.FeatureCollection(“ft:1oQeiBQIRoNgPEKeDZ1u8qCQP5I657fsOMecSZ0l-”),
BJ1995 = ee.FeatureCollection(“ft:1Wf6L2ZbhZ1c3UQM95RVZzMsr8-ZHk1GmpSmN5OMd”),
BJ1996 = ee.FeatureCollection(“ft:1us8xnLuzbc71qHphrL1lz-9_mSq_FSFLpxMNrKJz”),
BJ1997 = ee.FeatureCollection(“ft:1sbnsmOV5Kb2fA5fXdB8Vq-Jr2NJRa0tCkNtdnPR4”),
BJ1998 = ee.FeatureCollection(“ft:1r3DcuFbra-HMUOT8KzJgMfViYDmDC7St1PzFt-A8”),
BJ1999 = ee.FeatureCollection(“ft:1DFfQ51IyxddakAXHQBg4uNsZobwRUDl-6G8sSuj_”),
BJ2000 = ee.FeatureCollection(“ft:1LJIlkDNa3Xq4gH9ivO_8kWNVpKHZgsU5WLCfaI2u”),
BJ2001 = ee.FeatureCollection(“ft:1fdungkkXjF4Qr-MQhAIfAMOME39-5GizAQeHqVZT”),
BJ2002 = ee.FeatureCollection(“ft:1m2Qk089MtdxU3v6zk7fNQCvv0WpYstFKqapvrTEW”),
BJ2003 = ee.FeatureCollection(“ft:1VnZiXbcHAeIu_aY1awpGCDIJWGD1-mgo8Op_gfjr”),
BJ2004 = ee.FeatureCollection(“ft:17hXweCxsqYgqLt65MLWAzRMphpg61PBRrMPrljZy”),
BJ2005 = ee.FeatureCollection(“ft:1dSrFSQNFnXZptJ8ilOepJ3yRDFEKO9gj9UFK1fcW”),
BJ2006 = ee.FeatureCollection(“ft:1D7ALNyY8c6PIDGy_a15Tc6wb2y-77ZMArmI8EUuy”),
BJ2007 = ee.FeatureCollection(“ft:15CCzRn4XCa0dRUgXlyvPMGwylF02DaCaw54ydRvr”),
BJ2008 = ee.FeatureCollection(“ft:1D9OxIv6-nXzJ2idPHPX2lBuBZP8ZhyE-Y_6ekff8”),
BJ2009 = ee.FeatureCollection(“ft:15Nfkm2ADzgduaPsE3iwMdiZZ6Zjuq9avoQerXgz0”),
BJ2010 = ee.FeatureCollection(“ft:1lyLAs2x4JiyQCThmqbpbBq6whfuAAF0Jt17ogQpW”),
BJ2011 = ee.FeatureCollection(“ft:13MPSOxUqk9hcm12kLa3sTHoJTapF2a4hrXqQdjb-”),
BJ2012 = ee.FeatureCollection(“ft:1bXJ0xyWwGbM48KAQm6HdtDAPD5lzCN2CIVtg5I0n”),
Lt1993 = ee.Image(“NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F101993”),
Lt2012 = ee.Image(“NOAA/DMSP-OLS/NIGHTTIME_LIGHTS/F182012”);
Google Earth Engine Code --->
Conclusion
From the comparisons of topography and urban
sprawl images, we can see a relationship between
them. More mountains hinders urban sprawl. This
makes sense because uneven topography makes
urban development harder. People tend to gather
on flat topography.
For Beijing specifically, it is growing eastard
and connecting with other larger cities such as
Tangshan and Tianjin. If the sprawl keeps its pace,
Beijing will become a giant metropolitan area
expanding all the way to the Bohai Sea.

//////////////// Locate the map to Beijing /////////////////////////
Map.setCenter(116.4056, 39.9083,9);
/////////////// Creating an Image Collection for DEM ////////////////
var DEM_collection = ee.ImageCollection([BJdem1,BJdem2,BJdem3,BJdem4,BJdem5,BJdem6,BJdem7]);
/////////////// Clip Global DEM to only Beijing //////////////////
function ShowElv(DemImage){
return DemImage.clip(BJ);
}
var topoBJ = DEM_collection.map(ShowElv);
////////////// Creating Elevation for elevation higher than 150m and within Beijing //////////
function ShowHighElv(DemImage){
var dem_mask = DemImage.gt(150);
var high_elev = DemImage.mask(dem_mask);
var high_elev_bj = high_elev.clip(BJ);
return high_elev_bj;
}
var HIGHDEM_col = DEM_collection.map(ShowHighElv);
//////// Clip night lighting to Beijing and mask out area without stable lighting or intensity less than 15 ///
var NightLight = ee.ImageCollection([Lt1993,Lt1994,Lt1995,Lt1996,Lt1997,Lt1998,Lt1999,Lt2000,
Lt2001,Lt2002,Lt2003,Lt2004,Lt2005,Lt2006,Lt2007,Lt2008,Lt2009,Lt2010,Lt2011,Lt2012]);
print(‘night light’,NightLight);
function LightBJ(LightImage){
var light_mask = LightImage.select([‘stable_lights’]).gt(15);
var lightOn = LightImage.mask(light_mask);
var lightOnBJ = lightOn.clip(BJ);
return lightOnBJ;
}
var light_clip = NightLight.map(LightBJ);
////////////// Calculate Rate of Growth in Urban Area based on Night Lighting ///////////////
function LightBJAll(LightImage){
var lightBJall = LightImage.clip(BJ);
return lightBJall;
}
var lights_BJ = NightLight.map(LightBJAll)
var light_sd = lights_BJ.select(‘stable_lights’).reduce(ee.Reducer.stdDev());
var light_mean = lights_BJ.select(‘stable_lights’).mean();
var light_sum = lights_BJ.select(‘stable_lights’).sum();
var light_sum_proj = light_sum.reproject(‘EPSG:3857’,null,1000);
var urban_rate = ee.Terrain.slope(light_sum_proj);
print(‘urban rate’, urban_rate);
Google Earth Engine Code - continued

//////////// urban area shapefile bounded to BEIJING //////
var BJ1993_bound = BJ1993.filterBounds(BJ);
////////////// Show Beijing Topography ///////////////////////////////////////////
Map.addLayer(topoBJ,{min:0, max: 2200,palette:[‘e6ffe6’,’006622’]},’BJ Topo’)
////////////// Present lighting for each year //////////////////
Map.addLayer(light_clip.filterDate(‘1993-01-01’,’1993-12-31’),
{min:0,max:65,palette:[‘000000’,’ffffb3’], bands:’stable_lights’},’nightlights1993’);

///////////////// Show Annual Urban Development ///////////////////////
Map.addLayer(BJ1993_bound, {color:’1a8cff’}, ‘BJ 1993’);
Map.addLayer(BJ1994_bound, {color:’1a8cff’}, ‘BJ 1994’);
Map.addLayer(BJ1995_bound, {color: ‘1a8cff’}, ‘BJ 1995’);

////////////// Show Urbanization Rate, darker color means slower sprawl, vice versa /////////
Map.addLayer(urban_rate,{min:0,max:20,palette:[‘e6f9ff’,’0099cc’] },’Urbanization Rate’);
///////////// Show Mountains of Beijing //////////////////////////
Map.addLayer(HIGHDEM_col,{min:150, max: 2200,palette:[‘e6ffe6’,’006622’]},’BJ mountain’)
///////////// Show other statistics measuring of lights over 2 decades //////////////
Map.addLayer(light_sd,{min:0,max:20,palette:[‘ecffb3’,’739900’]},’light value sd’);
Map.addLayer(light_mean,{min:0,max:65,palette:[‘ecffb3’,’739900’]},’Light mean value’);
Map.addLayer(light_sum,{min:0,max:1500,palette:[‘ecffb3’,’739900’]},’Light sum value’);
///////////// Show other statistics measuring of lights over 2 decades //////////////
Map.addLayer(light_sd,{min:0,max:20,palette:[‘ecffb3’,’739900’]},’light value sd’);
Map.addLayer(light_mean,{min:0,max:65,palette:[‘ecffb3’,’739900’]},’Light mean value’);
Map.addLayer(light_sum,{min:0,max:1500,palette:[‘ecffb3’,’739900’]},’Light sum value’);

Data Sources:
USGS Earth Explorer: http://earthexplorer.usgs.gov/
Beijing City Lab: http://www.beijingcitylab.com/data-released-1/
NOAA Night Light: https://code.earthengine.google.com/
ArcGIS tutorial: http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/slope.htm
Spatial Reference: http://spatialreference.org/ref/sr-org/7483/
Google Earth Engine Tutorial: https://developers.google.com/earth-engine
Thank to Professor Dana Tomlin and Jill Kelly for technical support.
Reference and Notes

Zhao,Jianting_EE

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