Urban Spatial Analysis - Xinlin Huang by Xinlin Huang

Urban Spatial Analysis Work Samples

XINLIN HUANG University of Pennsylvania Master of City Planning ‘13 Landscape Studies ‘13 Phone: 1-215-882-0712 Email: xinlinhuang7@gmail.com LinkedIn (Click)

Measuring TOD Potential of Philadelphia’s Subway Stations

1.CREATE TOD STUDY ZONE Subway Station

TOD Study Zones based on 5 minutes walkshed

Team: Xinlin Huang, Rung-er Jang, Yao Lu

INTRODUCTION Transit Oriented Development (TOD) promotes dense, walkable and mix-used communities around the transit nodes. Compared to the low-density suburban sprawls in US cities, TOD provides a life style choice that involves more walking, biking and human contact, with higher energy efficiency and embraces the diverse and vibrant nature of urban living. Under the pressing global warming issue and ever- increasing petrol price, TOD has been included by many American cities as the solution towards those problems. And because of the recently- increased popularity of urban life styles among young Americans, TOD has been adopted by many realestate developers as the market’s demand. Our study aims at evaluating the TOD potential for Philadelphia’s major transit nodes areas, and identifying the most eligible stations for future TOD. Philadelphia is a city with a successful public transit system. The city has a relatively stable source of public transit riders and sound transit infrastructures. The current public transit system of the city consists of subway, regional rails, light rails, buses, inter-urban high speed lines. Among the city’s many transit nodes, we select subway stations as the subject of our study. We believe the subway stations have the ability to generate passenger traffic that enables condensed economic and social activities and support a successful TOD. The city’s subway line has high capacity, high speed and a large service area. According the SPETA ( South-eastern Pennsylvania Transportation Authority)’s Operating Facts Report Fiscal Year 2011, the ridership of the city’s subway system is 63,177,000 passenger miles, which is the second largest running after bus and light rail system’s 106,516,600 passenger miles. However, the city has 52 subway stations but 8,074 bus/ light rail stations. Therefore the actual passenger attraction of subway stations is stronger than bus/light rail stations. Besides, the subway station areas very often are where the buses and light rail stations clustered, which means subway stations areas actually generate more passenger than subway stations.

2. SELECT CRITERIA

Demographic and Economic

Land Use

High density indicates possible intensive economic and social activities. High value of employment and family income indicates high level of economic prosperity. High house value and gross rent indicates the desirability of land and future demand for land in the study zone. The combination of the features mentioned above is the indicator of business opportunities. TOD supportive-zoning are critical for TOD. Mixed use is desirable because it indicates existing TOD foundation and eliminates the potential cost of zoning change. Our general standard of scoring the land use favours residential-commercial mixed land use with a more than 25% residential proportion inside the study zone.

Urban Form

Existing physical features greatly affect the potential of TOD. An ideal TOD site should be walking-friendly. We assume that block size determines the level of walkability. By measuring the total length of streets in each study zone, we look for those with the highest value of aggregated street length which indicates smallest block size and a high level of walkability.

Residents‘ Journey to work

Public transit ridership of a study zone indicates the amount of steady traffic flow and source of transit ridership. In our study we calculate the “means of travel” and “car ownership” to tell which study zones have steadier and higher public transit ridership. We also calculating travel time to work by car. The idea is that people who spend longer time to commute by car might be more willing to use public transit.

Employment Rate

Population Density

3. ANALYSIS AND MAPPING

Fern Rock Trans. Center

Olney

Wyoming

North Philadelphia

Girard

40th

34th 30th

Race-Vine

Allegheny

Tioga

North Philadelphia Susquehanna-Dauphin

Berks

Girard

Fairmount 69th St. Terminal

Spring Garden

Chinatown

Millbourne

63rd 60th

56th 52nd

Spring Garden 46th

Population Density Criteria

15th 13th 8th 5th 2nd 11th City Hall Walnut-LocustLombard-South

40th

34th 30th

Employment Criteria Subway Station Buffer 1

Ellsworth-Federal

Tasker-Morris

Spring Garden

Race-Vine Chinatown 15th 13th 8th 5th 2nd Walnut-Locust11th

Lombard-SouthCity Hall

Subway Station Buffer

Ellsworth-Federal

Allegheny

Somerset Huntngdon� York-Dauphin

Cecil B. Moore

Berks

Fairmount Spring Garden 46th

Erie/Torresdale

Alleghney

Somerset Huntngdon� York-Dauphin

Cecil B. Moore

56th 52nd

Church

Erie

Tioga

Susquehanna-Dauphin

63rd 60th

Frankford Terminal Margaret/Orthodox

Huntng Par k�

Church Erie/Torresdale

Alleghney

Millbourne

Wyoming

Frankford Terminal Margaret/Orthodox

Huntng Par k� Erie

69th St. Terminal

4.WEIGHT CRITERIA

Fern Rock Trans. Center

Olney Logan

Logan

Tasker-Morris

Snyder

Oregon

Median House Value

Fern Rock Trans. Center

Olney

Margaret/Orthodox

Huntng Par k�

Alleghney

Berks

Cecil B. Moore Girard

69th St. Terminal

56th 52nd

Spring Garden 46th

40th

34th 30th

Race-Vine

69th St. Terminal

63rd 60th

Income Criteria

Chinatown

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall

56th 52nd

Spring Garden 46th

40th

34th 30th

Ellsworth-Federal

69th St. Terminal

63rd 60th

House Value Criteria

Chinatown

56th 52nd

Spring Garden 46th

40th

Girard

34th 30th

Rent Criteria

Chinatown

Subway Station Buffer 1

Ellsworth-Federal

Tasker-Morris

Race-Vine

Spring Garden

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall

Subway Station Buffer

Ellsworth-Federal

Tasker-Morris

Race-Vine

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall

Subway Station Buffer

Berks

Cecil B. Moore

Fairmount Millbourne

Allegheny

Somerset Huntngdon� York-Dauphin

Girard

Girard Spring Garden

Tasker-Morris

Snyder

Oregon

Patson��

Existing Land Use

Patson��

Land Use - Calculated

Olney

Walkability

Fern Rock Trans. Center

Olney

Church

Alleghney

Girard

56th 52nd

Spring Garden 46th

40th

34th 30th

Race-Vine

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall

Commercial District Mixed Use C+R

Girard

69th St. Terminal

40th

34th 30th

Alleghney

Somerset

Subway Station Buffer

Susquehanna-Dauphin

1 2

Snyder

Oregon

Cecil B. Moore

Patson��

Olney

Fern Rock Trans. Center

Olney

Logan

Margaret/Orthodox

Huntng Par k�

North Philadelphia

Cecil B. Moore

Allegheny

56th 52nd

46th

40th

34th 30th

Race-Vine

Chinatown

Tasker-Morris

Allegheny

69th St. Terminal

Travel by Subway Criteria Subway Station Buffer 1 2

Millbourne

63rd 60th

56th 52nd

Spring Garden 46th

40th

34th 30th

Race-Vine

Chinatown

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall Ellsworth-Federal Tasker-Morris

Cecil B. Moore Girard

69th St. Terminal

TimeTravel toWork Criteria Subway Station Buffer 1 2

Millbourne

63rd 60th

56th 52nd

Spring Garden 46th

40th

34th 30th

Race-Vine

Chinatown

Tasker-Morris

Berks

Subway Station Buffer 1 2

Snyder

Oregon

Patson��

5 Patson��

46th

40th

34th 30th

Spring Garden

Race-Vine Chinatown 15th 13th 8th 5th 2nd Walnut-Locust11th

Lombard-SouthCity Hall

Final Criteria Score: Score TOD Potential From Low to Subway StationHigh Buffer 1.11538462 - 2.03846154

Tasker-Morris

2.03846155 - 2.57692308

Snyder

2.57692309 - 2.88461538

Oregon

2.88461539 - 3.38461538 3.38461539 - 4.73076923

Vehicle Non-availability Criteria

Oregon

Spring Garden

Ellsworth-Federal

Spring Garden

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall Ellsworth-Federal

56th 52nd

Girard

Fairmount

Spring Garden

63rd 60th

Allegheny

Somerset Huntngdon� Susquehanna-Dauphin York-Dauphin

Berks

Millbourne

Church Erie/Torresdale Tioga

North Philadelphia

Girard

Fairmount

Spring Garden

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall Ellsworth-Federal

Cecil B. Moore Girard

Margaret/Orthodox

Huntng Par k� Erie

Somerset Huntngdon� Susquehanna-Dauphin York-Dauphin

Berks

Frankford Terminal

Alleghney

Tioga

North Philadelphia

Girard

Fairmount Spring Garden

Margaret/Orthodox Church Erie/Torresdale

Alleghney

Somerset Huntngdon� Susquehanna-Dauphin York-Dauphin

Girard

Logan

Erie

Erie/Torresdale Tioga

Fern Rock Trans. Center

Wyoming

Frankford Terminal

Huntng Par k�

Church

Erie

63rd 60th

Olney

Wyoming

Frankford Terminal

Alleghney

Millbourne

Fern Rock Trans. Center

Logan

Wyoming

69th St. Terminal

Percentage of Households without car

Travel Time to Work

69th St. Terminal

Berks

Girard

Fairmount

Sports Stadium District

Percentage Travel By Subway

Huntngdon� York-Dauphin

Girard

Tioga Allegheny

North Philadelphia Walkability Criteria

Ellsworth-Federal

Erie/Torresdale

Girard Spring Garden

Chinatown

Tasker-Morris

Patson��

Race-Vine

15th 13th 8th 5th 2nd Walnut-Locust11th Lombard-SouthCity Hall

Snyder

Recreational District

Spring Garden 46th

Oregon

Residential District

56th 52nd

Land Use Criteria

Tasker-Morris

Institutional District

63rd 60th

Subway Station Buffer

Ellsworth-Federal

Industrial District

Millbourne

Erie

Berks

Fairmount

Spring Garden

Chinatown

Church

Allegheny

Somerset Huntngdon� York-Dauphin

Cecil B. Moore

Girard

Fairmount 63rd 60th

Tioga

North Philadelphia Susquehanna-Dauphin

Berks

Frankford Terminal Margaret/Orthodox

Huntng Par k�

Erie/Torresdale

Alleghney

Allegheny

Somerset Huntngdon� York-Dauphin

Cecil B. Moore

Church

Erie

Tioga

North Philadelphia

Wyoming

Frankford Terminal Margaret/Orthodox

Huntng Par k�

Erie/Torresdale

Susquehanna-Dauphin

Millbourne

Logan

Logan Wyoming

Frankford Terminal Margaret/Orthodox

Erie

69th St. Terminal

Fern Rock Trans. Center

Olney

Logan Wyoming Huntng Par k�

Land Use

The overall formula of calculating the final score is: (Population Density*5)+ (Land Use*5) + (Walkability*5) + (Travel by Subway*5) + Income + Employment + Rent + House Value + Time Travel to Work + Average Percentage of Household with no Vehicle Available) / 26

Tioga

North Philadelphia Susquehanna-Dauphin

Berks

Cecil B. Moore

Fairmount Millbourne

Allegheny

Somerset Huntngdon� York-Dauphin

Girard

Girard Spring Garden

Church Erie/Torresdale

Alleghney

Tioga

North Philadelphia Susquehanna-Dauphin

Frankford Terminal Margaret/Orthodox

Huntng Par k� Erie

Erie/Torresdale

Alleghney

Allegheny

Somerset Huntngdon� York-Dauphin

Fairmount 63rd 60th

Margaret/Orthodox Church

Erie

Tioga

North Philadelphia

Wyoming

Frankford Terminal

Huntng Par k�

Church Erie/Torresdale

Susquehanna-Dauphin

Logan

Wyoming

Frankford Terminal

Fern Rock Trans. Center

Olney

Logan

Erie

Millbourne

Median Rent

Fern Rock Trans. Center

Olney

Logan Wyoming

Patson��

Median Family Income

3 5

After the previous processes in ArcGIS, every study zone now has its own value for each criterion. In this step, those data was exported into Excel spread sheet for our next step: weighing criteria. We do not assume that every criterion we use has the same significant in supporting a TOD zone. Each criterion for a study zone is weighted from 1 to 5.

Patson��

Traffic Impact Assessment for Bridge Closures Individual Assignment

3. CALCULATE COST DISTANCE TO HOSPITAL

1. ISOLATE BRIDGES FROM THE ROADS Roads

Raster Calculator -- Multiply Region Group

NoData = Penn Bridge 0= all the others

Reclassify Hospital layer to isolate Hospital, and use 3 friction layer to generate Cost Distance for each senario: Friction

Cost Distance

Reclassify Each Bridge Lanes

Current Condition

Reclassify NoData = Two Bridges 0= all the others

Water

Penn Bridge Shut-down

2. CREATE FRICTION LAYERS FOR 3 SENARIOS Since the biking speed on road is17.04545...Miles/Hour = 25ft/second, and the non road area the bikes are 10 times slower:

2 Small Bridges Shut-down

The Friction for road grid = 0.04 second/ft = 4 hundredseconds/ft The Friction for non road grid = 0.4 second/ft = 40 hundred seconds/ft Raster Calculator: Road * 0.1 + Water* 20

The Current Senario Friction 2 Small bridges Senario Friction

Reclassify into Water = NoData Roads and bridges =4 Non Roads = 40

Penn Bridge Senario Friction

Use Convertion Tools to turn the Biker Home layer into a new grid layer call “AllBikers”. Biker Pixels has the value of 1 while other grids are NoData

In Raster Calcula Multiply the “All Bikers” layer with each Cost Distan Layer Above

ator, lh nce

5. MEASURE TRAFFIC INCREASE ON THE SPECIFIED BRIDGE Cost Distance

4. CALCULATE TRAVEL TIME INCREASE

Zonal -Mean

Travel Time

245.83sec

New Layers that records Cost Distance of Bikers on each biker pixel

Zonal -Mean 4.391min

Zonal 4.820min -Mean

Zone

Increase

Penn Bridge Shut-down

17.6Sec

2 Small Bridges Shut-down

Reclassify the Road layer to get a Zone for Zonal Statistic

Flow Accumulation

6. FINAL RESULTS

Result

Zonal 2 -Maximum

Current Condition

N/A

43.4Sec

Flow Direction

N/A

Zonal -Maximum

2 Zonal -Maximum

Increased Time

Traffic flow on the specified bridge

Increased Traffic flow

4.097min

N/A

Penn Bridge Shut-down

4.391min

17.6s

2 Small Bridges Shut-down

4.820min

43.4s

Senario

Average Commuting Time

Current Condition

Best town to encouter a professional Clown

the

Individual Assignment This analysis is based on two assumptions: 1. In order to reduce commute time and cost, professional clowns tend to live closely to their work places. 2. Demands for clown performance are not limited to areas near clowns’ residences. Nevertheless, the demographic pattern of the areas near clowns’ residences indicated what a potential market for clowns might look like. Therefore, the characteristics of known markets can be used as parameters of identifying other potential markets for clown performance.

1. FIND OUT THE DEMOGRAPHIC CHARACTER OF CLOWNS’ MARKETS Mid-income Layer

Mid-income within Market zones

Mid-income Raster Map Interpolation - IDW

Low

income Group 60004-90003 30006-60004 90003-120001 7.589-30006 120001-150000

High

Population Density Layer

Population Density Raster Map

Population Densitywithin Market zones

Low

Interpolation - IDW

Clowns’ AvailabilityLayer Kernel Density Radius: 5miles

Clowns’ Housing - Market Zones

Low

Reclassify

High

5 4 3 2 1

Use the same operation for the Population Density Layer Population DensityGroup

High

Existing Clowns’ Residence

Reclassify into 5 income categories and use attribute table to see the count of each income group. And rank the income group based on the count:

1335-1761 55.2-482 482-908 908-1335 1761-8643

5 4 3 2 1

2. SEARCH FOR POTENTIAL MARKETS FOR CLOWN PERFORMANCE Mid-income heat map

Mid-income Raster Map

3. EVALUATE EACH TOWN’S POTENTIAL OF ATTRACTING CLOWNS Towns

Zonal Statistics - average Conversion- from polygon to raster

Reclassify according to the 5 Ranks from previous step

Population Density heat map

Population Density Raster Map Reclassify according to the 5 Ranks from previous step

High:11.305

Population Density heat map

Clowns’ AvailabilityLayer

Low:6.843

INDEX FOR ENCOUNTERING CLOWN(S) Low:4

Reclassify according to the 5 Ranks from previous step

WINNER !

High:15

Site Suitability Analysis for a New Telephone Tower Team: Xinlin Huang, Xin Ge In order to determine the potential locatioins for a new cellphone tower, we generated 6 criteria for our analysis. Each criteria was assigned different weight when calculating the final result, because they have different degrees of influence to cell -tower site selection: Weight 1. Population Density 3 2. Accessibility to roads 2 3. Ideal elevation 1 4. Distance to the shoreline 1 5. Distance to Current Towers 2 6. Current underserved zones 3 In our final map, areas marked with red represents the most likely locations for a new cellphone tower:

Existing Towers

Potential for new cell tower Low

High

1. SITING THE AREA WITH HIGHER POPULATION DENSITY Population density relates to the demand of cellphone signals. Altough we do not have the population data for this region, we believe that the density of urban roads is a good indicator for population concentration. Note: We did not distinguish the difference among roads, instead, we reclassify all the roads into one class. Then we use “Focal statistic” tool to calculate each grid’s road density environment. The Neighborhood we use is 50 * 50, which is a 500 * 500 meters square. Reclassify the result into two categories with median value(which is 104, on a range of 1-758). 1 means higher then 104, 0 means the opposite.

2. EVALUATING ACCESS TO MAINTENANCE Towers are more likely to be located in places that have good connection to roads, which provides access for cell tower maintenance Note: We use “ Euclidean Distance” to measure each grid’s distance to roads.

Reclassify: pixels from road within 100m - 500m get values of 1s, the other pixels get 0s:

3. ESTIMATING THE MINIMUM ELEVATION FOR A CELL TOWER The transmission of cellphone signal will be easily blocked by hills, which sets the demand for putting the tower at a relatively high location. We estimate the elevation of the 5 current towers, and base on our estimation we set the minimum height for our new tower. Note: Convert point to raster -> get a grid layer for the 6 towers. With this layer and elevation layer, use zonal statistics to calculate the elevation for each tower: All the towers are located at an elevation higher than 170 meters, except for one tower that locates at the elevation of 15 meters, Base on this trend, We decide to use 170 as the minnimum elevation for a new tower. Reclassify the elevation layer, pixels with elevation higher than 170 get values of 1s, while the others get 0s

4. EXCLUDING THE SEASURFACE AND SHORELINE Both shorelines and sea are no ideal location for a new cell tower. We believe that cell towers are more likely to be located on a stable ground that is less vulnerable when nature disasters, such as tunami and earthquake

Note: the dark brown area is the sea and its shoreline

Calculate each grid’s distance from the sea

Reclassify, area within 1km from the sea get values of 0s, the other get 1s

5. AVOIDING SIGNAL INTERFERENCE WITH CURRENT TOWERS A new cellphone tower will probably not locates too close to an existing tower, because the cellphone signals will interference with each other during the transmission. Also, it is economically inefficient to put a new tower near to existing ones.

Euclidean distance from towers

6. DETECTING CURRENT UNDERSERVED ZONES The transmission of cellphone signals behaves like the trasmission of light. Therefore, some valleys are more likely to be underserved because the signals are blocked by its adjacent hills. We captured the areas that current tele signals cannot reach. These underserved zones indicates a higher possibility of having a new cell tower built near them.

Use elevation layer and tower layer to generate viewshed layer

Reclassify: area within 1609m (i.e. one mile) from the towers get values of 0s, while the others get 1s`

in applying the Reclassify: visible “view shed” funcarea as 0s, invisible tion, Change OFF- area as1s SETA to 100m, add RADIUS2 as 35000m (it's GSM maximum coverage distance)

XINLIN HUANG 4420 LOCUST, PHILADELPHIA, PA 19104 1-215-882-0712

Skills and Strengths

xinlinhuang7@gmail.com

Education UNIVERSITY OF PENNSYLVANIA, SCHOOL OF DESIGN Master of City Planning in Urban Design + Certificate of Landscape Studies

May 2013

SUN YAT-SEN UNIVERSITY, SCHOOL OF GEOGRAPHY AND PLANNING Bachelor of Science in Urban/Rural Planning & Management & Resource Environment

July 2011

Exchange student in UNIVERSITY OF COLOGNE, GERMANY. Studied urban development and design strategies of deindustrialized cities under globalizaztion

Mar. - Aug. 2010

Professional Experience Intern in EMBARQ INDIA, MUMBAI OFFICE Conducted research and info-graphic design for open space design parameters as the office’s open space design guidelines; Participated in the master plan of a 158 acre new urban sector and designed 2 lake front parks, 1 neighborhood park and a playground Part-time in URBAN PLANNING & DESIGN INSTITUTE OF GUANGDONG, CHINA Participated land use impact analysis and design recommendations for future inter-city lightrail stations of Longshan Town; Accomplished regional landscape and feature plan for Nansha, a 575km2 port district of Guangzhou Intern in QUANZHOU PLANNING, DESIGN & RESEARCH INSTITUTE Conducted tourist resources evaluation and development plan for a city’s mountain district of 16000 acres, and contributed to its post-earthquake tourist trail design

May - Aug. 2012

Oct. 2010 - Feb. 2011

Jul.- Aug. 2008

+ 6 years of training focused on understanding cities through their social, economic, and political aspects + Familiar with researches for planning projects, including data mining, field study, spatialized analysis of demographic, land use, transportation and other socio-economic data + Experience of working in public and nonprofit sector + Experience in planning and design projects from regional scale to city block + Expertise and passion for visual communication + Proficiency in Adobe Creative Suite, ArcGIS, AutoCAD, MS Office ( Excel + Word +PowerPoint ), RhinoCeros, SketchUp, and V-ray

Additional Experience One of the translators for book POLITICAL GEOGRAPHY: WORLD ECONOMY, NATIONSTATE AND LOCALITY (6th edition) Research assistant for an academic project focusing on the gentrifying communities of Guangzhou

Urban Spatial Analysis I have three years of experience in using GIS to conduct urban spatial analysis. My skill set includes mapping, vector and raster spatial analysis, and GIS modeling. These GIS project are the samples showcasing my skills. If you are interested in seeing more of my work, please contact me. I look forward to hear about your feedback. Thank you.

Xinlin Huang Phone: 1-215-882-0712 Email: xinlinhuang7@gmail.com LinkedIn