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JING LIU PORTFOLIO

URBAN PLANNING | TRANSPORTATION PLANNING | GIS


JING LIU EMAIL

jingliu7@design.upenn.edu

MOBILE

(215)4390610

ADDRESS

115S 43rd Street

-------------------------------------EDUCATION---------------------------------University of Pennsylvania | School of Design

Master of City and Regional Planning, 2015 Fall Concentrated in Sustainable Transportation and Infrastructure Planning Master of Urban Spatial Analytics, 2015 Fall

Renmin University of China | School of Public Administration Bachelor of Science in Management Major in Land Use and Real Estate Management

I am a keen advocate of the fact that good transportation facilities tremendously improve people’s quality of life and generate great economic benefits. As a transportation planner and a spatial analyst, I am primarily interested in using spatial analysis skills and knowledges in the field of city planning, especially transportation planning for cities to promote efficiency, resilience, and quality of life.

-------------------------------------EXPERIENCE---------------------------------

Teaching Assistant School of Design, University of Pennsylvania 2014. 8-2015. 5 Assisted Professor Dana Tomlin with teaching two GIS courses - Modeling Geographical Objects and Modeling Geographical Space over two semesters. Intern China Academy of Urban Planning and Design, Beijing, China 2014.5-2014.8 Participated in making comprehensive plans of three cities in China including Liaocheng, Haerbin and Fushun. Designed deliverables and presentation slides and prepared final reports and document.

--------------------------------------SKILLS---------------------------------------Design: Adobe Photoshop, Illustrator, InDesign, AutoCAD, SketchUp Spatial Analysis: ArcGIS, ArcGIS Online, QGIS, GeoDa, Google Earth Engine Transportation Analysis: VISUM, TransCAD Programming: Python, Java Data Analysis: R, SPSS, Excel


TABLE OF CONTENT New York - New Jersey CrossRail Studio-------------------------------------------------------------------------1 Bustleton Avenue Corridor Improvement Plan------------------------------------------------------------------11 GIS Transportation Demand Modeling Enhancement----------------------------------------------------------17 Other GIS Mapping and Spatial Analysis Individual Projects---------------------------------------------------23 Community Walk-In Clinic Siting and Service Area Analysis----------------------------------------------24 Retail Trade Area and Consumer Probability Analysis-----------------------------------------------------26 Finding the Steepest Area of the Surface --Python Toolbox Design-------------------------------------28 House Value Prediction Tool -- Python Toolbox Design---------------------------------------------------30 Geovisualization of Travel Activities in Philadelphia---------------------------------------------------------32 Carpool Route Optimization----------------------------------------------------------------------------------33 Flight Flow in the World---------------------------------------------------------------------------------------34 Earth at Night--------------------------------------------------------------------------------------------------35 Mapping Regional Commuting in USA ---------------------------------------------------------------------36


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New York - New Jersey CrossRail Studio Project Type: PennDesign Transportation Planning Studio Project Time: 01. 2015 -- 05. 2015 Instructor: Marilyn Jordan Taylor, Robert Yaro My Contribution: Station Development Analysis, Station Site Planning, Graphic Design, GIS Analysis Tools: ArcGIS, Adobe Illustrator, Adobe Photoshop, Rhinoceros Collaborator: Zach Billet, Mazen Chaanine, Eugene Chao, Yexin Ding, Matt DiScenna, Mengwei Jian, Chen Ju, James T. Lantelme, Lanzi Li, Amy Jie Liu, Yukari Matsuda, Lex Powers, Brooke Ashley Wieczorek, Chi Zhang, Ge Zhang

1


Introduction

This studio is the sixth year of an ongoing research project at PennDesign focusing on the potential for intercity and high-speed rail (HSR) service in the Northeast Corridor to improve the Boston to Washington megaregion’s mobility, quality of life, and economic competitiveness. Previous studios have proposed the creation of Northeast Corridor HSR, investigated the economic benefits that such a system would produce, and recommended financing and phasing strategies for these investments, expansion and redevelopment of New York’s Penn Station and the creation of an overground rail transit network connecting the outer boroughs of New York City. Focused on 25 miles of railroad centered on New York’s Penn Station, NY-NJ CrossRail will create the expanded capacity and accessibility that will enable economic and population growth for America’s largest urban area and the economic hub of the Northeast megaregion. CrossRail will transform the metropolitan area by integrating its fragmented rail system and uncoordinated capital programs into a unified region-wide -- and region-shaping -- system by providing: •  New accessibility to Manhattan and an expanded central business district beyond Manhattan with new development centered on transit •  Doubling rail capacity along the rail corridor from Newark Airport to Jamaica, Queens

•  Realizing Amtrak’s Gateway program for increased trans-Hudson capacity, resilience, and replacement of deteriorated infrastructure •  Creating rapid transit-like service across the core of the region •  Improving international airport access

CrossRail will create major new economic and urban development opportunities along its entire length and will jumpstart development beyond Manhattan into Queens and Northern New Jersey. These development opportunities can be the source for a significant share of the project’s financing by capturing a portion of the value created by CrossRail around more than a dozen stations. CrossRail is an investment in… •  A long-term regional growth strategy

•  The global competitiveness of the New York region

•  The region’s resilience in the face of climate change and extreme events •  A new system of project finance and delivery

2


Meadowlands South

PENN

Long Island City

Newark Newark Liberty Airport

Jamaica

JFK Airport

3


Project Concept

Project Benefits

The region is currently served by three separate The construction of NY-NJ CrossRail will have state-owned commuter rail operators - New numerous benefits both for passengers and Jersey Transit (NJ Transit), the Long Island the existing rail operators. Rail Road (LIRR), and Metro-North Railroad - each focused on its own service area. The CrossRail will integrate the fragmented rail system and tie the region together. CONNECTICUT Hudson Line

15% 34%

Daily Ridership

JFK Airport Jamaica and LIRR Stations to the East

New Haven Line/NEC

Kew Gardens

MID-HUDSON

Doubles trans-Hudson rail capacity

Stamford Montclair-Boonton Line

LONG ISLAND

Forest Hills

Adds transit-style services along CrossRail core the potential for 3 minute- peak hour headways

$

Port Jefferson Branch

Gladstone Line

Woodhaven Blvd

23% 57%

Grand Central Jamaica

Newark

Raritan Valley Line

Newark Airport

Hicksville

Penn Station

Babylon Branch

NEW JERSEY

North Jersey

CONNECTICUT Hudson Line

New Haven Line/NEC

MID-HUDSON Stamford Montclair-Boonton Line

LONG ISLAND Port Jefferson Branch

Gladstone Line Grand Central Raritan Valley Line

Jamaica

Newark

Newark Airport

NEW JERSEY

Northeast North Jersey Corridor Line/NEC Coast Line

Rail Service Diagram with NY-NJ CrossRail New Jersery Transit Metro North

4

Hicksville

Penn Station

Long Island Railroad (LIRR)

Babylon Branch JFK

Catalyze the addition of 140,000 jobs in station areas

Phasing Plan

Coast Line CurrentCorridor RailLine/NEC Service Diagram Northeast

An estimated 360 million riders per year

Adds at least $ 48 billion to state and local tax rolls

11%

16%

As Origin As Destination Station Station

As Origin

As Destination

3,387

25,833

374,400

25,833

5,660

3,027

11,447

5,320

8,247

8,367

Woodside

87,420

10,647

Long Island City

63,987

164,907

251,433

628,347

Meadowlands South

Penn Station

80,613

172,367

Newark Penn

97,120

42,367

124,013

13,127

Newark Airport and NJ Transit Stations to the South and West


Station Analysis By drastically improving capacity and connectivity region-wide, NY-NJ CrossRail will unlock new development potential for the region and its communities. The development of key station locations is central to realizing the project’s full potential. Two station areas, one named Meadowlands South at

Newark Penn

EWR

9 mins On Grade NJ Transit

Tunnel Meadowlands Hudson River NJ Transit Amtrak PATH NJ Transit

New York Penn

Midtown East Medical Area 8 mins East River

Amtrak LIRR NJ Transit Metro North A C E 1 2 3

Queens LIC Station Sunnyside Woodside Woodhaven Blvd Forest Hills Kew Gardens Jamaica On Grade LIRR

Secaucus and the other at Long Island City/ Sunnyside offer significant opportunity for new development, and have been selected for in-depth

LIRR 7

LIRR 7

LIRR

JFK Airport

LIRR AirTran

LIRR

planning and urban design proposals to illustrate their vast potential for growth.

Existing Lan aucus d Us e

City Existing Island Lan g n dU Lo se

Lon

Ne

a

io

Stat

uth

Pe

M ead

ow

Isla n d

n

g

nn

m aic

C it y

Ja

So

Sec

Meadowlands South

la n ds

w ark

5


Meadowland South Station

Road Network

Development Zone

Existing Local Streets

Three zones are proposed in this area. They are Laurel Hill, a high density zone with an FAR of 2.8, Meadowlands South, the medium density zone with a FAR of 1.5, and New Secaucus, a low density zone with a FAR of 0.6. These zones are so designated based on natural geographic boundaries, existing arterials, and proximity to the Meadowlands South Station. In 45 years, the area will be transformed from 74% industrial land uses into a higher density district, with over 60% mixed-uses. One third of the mixed-uses will be commercial mixed-use, and the rest predominantly residential.

Meadowlands South will add:

74 million

Total Developed Square Feet Residential Office Retail Hotel Industrial

Proposed Local Street

FAR: 0.6

Highway Railway CrossRail

FAR: 1.5 Meadowlands South

FAR: 2.8

Transit Service Existing Transit Routes Proposed Bidirectional Transit Route 1

Land Use Changes Comparison Commercial

6%

Other

19%

39 million sf 27 million sf 3 million sf 2 million sf 3 million sf

This new urban center will concentrate highly dense mixed-use development around the existing Lautenberg Station, replacing the low-density warehouses that currently dot the landscape. Meadowlands South can accommodate at least 27 million sq. ft. of office space, 40,000 residential units, and 2.8 million sq. ft. of retail, among other uses. 6

Proposed Main Street

Proposed Bidirectional Transit Route 2

Industrial and Warehouse Retail

Current Land Use

Meadowlands South

74% Industrial Commercial

Open Space

9%

17%

Residential

6%

3%

Proposed Land Use

Water and Open Space

Mixed Use including New Urban Industries

Existing Water Existing Open Space

65%

Proposed Water Proposed Open Space

Meadowlands South


Rendering by Team Member Ge Zhang

Rendering by Team Member Ge Zhang

City Government, Education and Culuture Center Theatre Meadowlands South Station 7


Long Island City Station and Queen Sunnyside Station The plan proposes an integrated network of six distinct districts in this area: •  Queens Sunnyside Station with high-density commercial mixed-uses, affordable housing and a possibly a convention center built over and along the rail yard with an FAR of 3.2

Development Zone

Transit Plan and Service Area

•  LIC Station and the commercial mixeduse riverfront neighborhood with an FAR of 4.0 •  Hunters Point mixed-income residential neighborhood with an FAR of 3.2

•  Incubator live/work neighborhood, onestop by F line from the under-construction Cornell Technion campus in Roosevelt Island -- the applied technology university that is intended to become the New York version of MIT with an FAR of 2.5

CrossRail Station Subway Station Rerouting of #7 Line

Land Use of Infill Development

Open Space and Bike Infrastructure

•  Technology, Entertainment, and Design Innovation District anchored by the existing Silvercup Studios with an FAR of 2.5

•  Mixed-use green industry district along Dutch Kills with an FAR of 2.0 Long Island City will add:

28.5 million

Total Developed Square Feet

8

Office Residential Public Industry Other

12 million sf 10 million sf 3.2 million sf 1.7 million sf 1.6 million sf

Residential Mixed Commercial/Residential Commercial Industrial Public Facilities & Institutions Open Space Other (Parking, Transportation, etc)

Open Space Extension of Protected Bike Lane Extension of Shared Bike Lane Existing Protected Bike Lane Existing Standard Bike Lane Existing Shared Bike Lane Existing Walk Bike Lane


Convention Center Queens Sunnyside Station Landmark Mixed-use Skycraper

Rendering by Team Member Chen Ju

Rendering by Team Member Chen Ju

9


10


Bustleton Avenue Corridor Improvement Plan Project Type: Transportation Planning Final Project Project Time: 09. 2014 -- 12. 2014 Instructor: Megan S. Ryerson My Contribution: Spatial Analysis, Transportation Analysis, Graphic Design Tools: ArcGIS, VISUM, Adobe Illustrator, Adobe Photoshop Collaborator: Amy Jie Liu

11


Introduction

Existing Conditions

This plan is aimed to improve the connectivity of south segment of Bustleton Avenue, which is located in northeast Philadelphia, to the whole public transportation network. The segment of the corridor starts from the intersection of Bustleton Avenue and Frankford Avenue to the intersection of Bustleton Avenue and Roosevelt Boulevard. We choose this segment to study because there is a potential disconnectivitiy problem with this corridor, considering the current subway line network and proposed Roosevelt Boulevard Subway Extension in Philadelphia 2035 Comprehensive Plan.

Relatively Low Population Density Relatively Low Median Household Income Average Unemployment Rate is 15% More Black Population on the West Side More White Population on the East Side

Map of Subway Lines in Philadelphia

Current Land Use 18% 10% 54%

Active Recreation Cemetery Civic/Institution Commercial Business/Professional Commercial Consumer Commercial Mixed Residential Culture/Amusement Industrial Park/Open Space Residential High Residential Low Residential Medium Transportation Vacant

12

Other/Unknown


Travel Behavior Analysis

Traffic Conditions Analysis

Percent of Driving Alone

Map of Traffic Volume

Percent of Public Transportation Percent of Walking & Bicycle

<=5%

<=20%

<=15%

21%-38%

16%-25%

39%-52%

26%-36%

12%-20%

53%-67%

37%-48%

21%-37%

>=68%

>=49%

>=38%

6%-11%

Inflow and Outflow of the Area

Flow Bundle Diagram

123

Isochrones Diagram

1,326

8

<=3min <=5min <=7min <=9min <=12min <=15min <=20min <=30min <=40min <=50min <=60min

13


Problems Traffic Congestion Caused by Buses

Goals Future Disconnectivity

Improve Connectivity of the Corridor to the Whole Transportation Network Reduce Air Pollution Enhance Multi-modal Transportation Service

Air Pollution From Buses

Bicycle and Pedestrian Safety Issue

Before

Interventions After

EXTEND MFL TO ROOSEVELT AVE •  Elevated extention of the subway •  Reduce to only one drive lane in each direction MOVE BUS TERMINAL •  Move the bus terminal to the intersection of Bustleton Ave and Roosevelt Blvd ADD A BIKE-EXCLUSIVE LANE AT EACH DIRECTION •  New sharrows on road 14


Effects Scenario 2: With construction of Roosevelt Blvd Subway Extension

SIO EN XT LE MF

MF

LE

XT

EN

SI O

N

N

Scenario 1: Without construction of Roosevelt Blvd Subway Extension

L

MF

Private Traffic Decreases (Left: Before, Right: After)

Private Traffic Decreases (Left: Before, Right: After)

Public Traffic Increases (Left: Before, Right: After)

Public Traffic Increases (Left: Before, Right: After)

15


16


GIS Transportation Demand Modeling Enhancement Project Type: GIS Individual Project Project Time: 01. 2015 -- 05. 2015 Tools: ArcGIS, R, GeoDA, Adobe Illustrator, Adobe Photoshop Instructor: Charles Dana Tomlin

17


Introduction

Data

Philadelphia is used as an example, and data used here is 2000 and This project aims at using Spatial Regression to improve the predictive power of the traditional regression models for predicting 2010 US Census Bureau’s American Community Survey (ACS) . Socio-economic Characteristics transportation demand. This study focus on the results of the trip Population, Population Density, Number of Household, Average generation phase of the four-step model (i.e., trip generation, trip distribution, transport mode choice and route choice). Thus, it aims Household Size, Median Income, Median House Value, Median Rent, Average Car Ownership, Percent of Population 5 Years and to contribute to the evaluation of the benefits in the application of spatial statistics tools in the analysis of demand for transport and for Younger , Percent of Population 65 Years and Older, Number of Workers, Number of Housing Unit. sustainable transport planning.

Methodology

Spatial Characteristics: Global Autocorrelation Indicator: Moran Scatterplot Quadrant A value of spatial autocorrelation can show how much the value Local Autocorrelation Indicator: LISA I of a variable in one region is dependent on the values of the same Local Street Density, Bus Density, Distance to Subway Station variable in neighborhood locations. Land Use Characteristics: Percent of Residential Land Use The model used in this project consists in the introduction of indicators of spatial autocorrelation (Global and Local) as variables. They are added to the traditional variables in the multiple regression Global and Local Spatial Autocorrelation model, or traditional model. Global indicators, like Moran’s I, provide Moran Scatter Plot a unique value as a measure of data spatial association, the local Global Spatial Autocorrelation indicators produce a specific value for each area. indicator - Moran’s I index of Total The global spatial variables in this model are binary (dummy) variTrip is 0.297, which means that the ables associated to the quadrants of the Moran Scatterplot (global number of trips increases when the indicator). Local spatial variables used in this model is LISA indica- neighbors’ value increases. tor. Other spatial pattern characteristics considered in this project include street density, density of bus stops, and the proximity to Local Spatial Autocorrelation indicarailway stations. These criteria are generated by GIS and GeoDa, tor - LISA Index Map and Significance map and then the regression model is conducted in R. LISA Cluster Map LISA Significance Map shows that there number 2000 is the base year in this project. Data in year 2000 was used for the calibration and also for checking the performance of the best of trips in this area has the demand models. trend of clus2013 is the target year and the number of trips is forecasted using tering. the model. 18


Other Criteria Population

Population Density

Number of Household

Average Household Size

Median Income

Median House Value

Median Rent

Average Car Ownership

Percent of Population 5 Years and Younger

Percent of Population 65 Years and Older

Number of Workers

Number of Housing Unit

Street Density

Bus Stop Density

Distance to Subway Stations

Land Use

19


Results and Discussion On the Left is the comparison of the spatial regression model used in this project and the traditional regression model that doesnâ&#x20AC;&#x2122;t consist in spatial criteria including spatial autocorrelation indicator and spatial transportation network information. The result shows that the spatial regression model is superior to the traditional model because the spatial regression model has higher R-squared number. The LISA cluster map of the residuals shows that the residuals of spatial regression model shows lower level of spatial autocorrelation because the LISA residual map of the spatial regression model shows the residuals are more dispersed. This map on the right shows the prediction of 2013â&#x20AC;&#x2122;s trip demand using spatial regression model. We can see that center city, and major residential areas are places where generates the most trips.

Spatial Regression Model

Traditional Regression Model Regression Results

Histogram of Residuals

Trip Demand Prediction for 2013

LISA Cluster Map

Legend PredictedTotalTrips 0 - 736.36 736.37 - 1351.45 1351.46 - 1868.16 1868.17 - 2385.18 2385.19 - 3293.17

20


21


22


Other Spatial Analysis and GIS Mapping Individual Projects Project Type: GIS Individual Project Project Time: 09. 2013 -- 04. 2016 Tools: ArcGIS, ArcGIS Online, Python, Adobe Illustrator, Adobe Photoshop Instructor: Charles Dana Tomlin, Ken Steif, Amy Hillier

23


Community Walk-In Clinic Siting and Service Area Analysis This project aims at finding the vacant parcels for new community walk-in clinics. The proper parcels should meet all of the below criteria and finally there are four parcels selected. Tools used in the project includes Network Analysis, Zonal Statistics, Euclidean Distance and Kernel Density. High Density of Children

High Density of The Old

Service Area of Existing Clinics Commercial Zone Along Aterials

Parcels with Lower Prices

24

High Density of The Poor

Area Near Bus Lines

Selected Parcles for New Community Walk-in Clinics


Map of The Old and Young Population Density and Walk-in Clinic Service Area

3 2

1

Calculating the Number of Old and Young Population Served by the New Walk-In Clinics In order to calculate the number of old and young population within 1 mile distance of the clinics, I firstly convert census tracts to points, and then use Kernel Density to generate the density map of the population of children and old people. Then I use Network Analysis to generate 1-mile service area of the clinics. Next, I use Zonal Statistics to calculate the mean value of the density of the old and young. Next step is to use Calculate Geometry to calculate the area of the service areas. Finally, I get the population of the old and young by multiplying area and density.

4

25


Retail Trade Area and Consumer Probability Analysis Retail Trade Area Generation

Retail Trade Area Diagram

A trade area is the geographic area from which a community generates the majority of its customers. Conducting trade area analysis is very important to the retailers since it has significant help for defining sales and market share targets, minimizing cannibalization, defining the geographic range to direct marketing towards , and conducting competitor threat analysis. This project aims at creating trade areas for 51 shopping malls in this area, and analyzing the consumer probability for one of the shopping malls -- Post@Modern The map on the left is the trade sheds for 51 shopping centers generated by Creating Thiessen Polygon tool in ArcGIS. The summary table showing each trade areaâ&#x20AC;&#x2122;s information below is generated by Zonal Statistics by Table tool in ArcGIS. The Travel Potential Map is created by using five criteria including employment density, median income, distance to highways, distance to bus stations and distance to employment centers. It is created for the calculation of weighted population, which will be used in calculating consumer probability.

Travel Potential Map

Travel Potential Criteria

26


Consumer Probability Diagram

Consumer Probability Calculation The consumer probability is calculated by using gravity model. Two criteria used in this gravity model are gross area of shopping centers, and weighted population calculated by using population and travel potential index. Frequent shoppers of the target shopping center is defined as the shoppers whose consumer probability is above average level. From the summary table below we can see that frequent shoppers of the target shopping center are low income and non-white people. Summary Table of Characteristics of Frequent Shoppers and Unfrequent Shoppers

Exploring The Relationship between Median Income and Consumer Probability

27


Finding the Steepest Area of the Surface -- Python Toolbox Design This python tool box is developed to calculate the steepness of the surface and generate the raster grid of the steepness and steepest area of the surface. Here we define the steepness as the degree of the variation of the slope, which can be explained as slope of slope. This toolbox can be created by firstly adding new toolbox to ArcToolBox and add the python script to the new tool box in ArcGIS. Input Elevation Grid

Slope Grid Slope Tool

Slope Tool

Deviation Grid

Reclassify

Slice Tool

Output Steepest Area Grid

28

Smoothed Slope of Slope Grid Raster Calculator

ArcGIS Model Builder

Focal Statistics

Output Steepness Grid

Slope of Slope Grid


Python Script

“““This script is used to find the roughest part of the grid. Here I define the roughest part to be where the difference between the pixel’s value of “slope of slope” and the focal mean of “slope of slope” is very large. The geoprocessing tools used in this script are slope, focal statistics (mean), math, slice and reclassify. ””” # Import external modulesimport sys, os, string, arcpy # Check to see if Spatial Analyst license is available if arcpy.CheckExtension(“spatial”) == “Available”: try: # Activate ArcGIS Spatial Analyst license arcpy.CheckOutExtension(“spatial”) # Read user inputs from dialog box inputGridName = arcpy.GetParameterAsText(0) outputSlopeGridName = arcpy.GetParameterAsText(1) outputSlopeOfSlopeGridName = arcpy.GetParameterAsText(2) outputDevGridName = arcpy.GetParameterAsText(3) outputSlicedDevGridName = arcpy.GetParameterAsText(4) outputRoughestGridName = arcpy.GetParameterAsText(5) neighborhood = arcpy.GetParameterAsText(6) iterations = arcpy.GetParameterAsText(7) # Set processing extent arcpy.env.extent = inputGridName # Create slope grid # Set local variables inRaster = “elevation” outMeasurement = “DEGREE” zFactor = 0.3043 # Execute Slope SlopeGridLayer = Slope(inputGridName, “DEGREE”, 0.3043) # Create slope of slope grid # Set local variables

inRaster = “elevation” outMeasurement = “DEGREE” zFactor = 0.3043 # Execute Slope outputSlopeOfSlopeGridName = Slope(SlopeGridLayer, “DEGREE”, 0.3043) # Create iteration counter integerIterations = int(iterations) counter = range(integerIterations) # Iterate focal statistics smoothing arcpy.AddMessage (“Smoothing input surface “ + iterations + “ times \n with a neighborhood of: “ + neighborhood) for number in counter: tempLayerName = arcpy.sa.FocalStatistics(tempLayerName, neighborhood, “MEAN”) # Calculate difference between real elevation and focal mean of elevation deviationsLayer = arcpy.sa.Minus(tempLayerName, inputGridName) absDeviationsLayer = arcpy.sa.Abs(deviationsLayer) # Slice the deviationsLayer SlicedDeviationsLayer = Slice(absdeviationsLayer, 5, “NATURAL_ BREAKS”, 1 ) # Create the grid of the roughest part RoughestLayer = Reclassify(“SlicedDeviationsLayer “, “Value”, RemapValue([[1,NoData],[2,NoData],[3,NoData],[4,NoData],[5,1])) # Save newly created grids deviationsLayer.save(outputDevGridName) RoughestLayer.save(outputRoughestGridName) 29


House Value Prediction Tool -- Python Toolbox Design This script can be used to make prediction of the house value using three given variables: median income, population density and teacher-student ratio. The tool will generate the shapefile of the house prediction result, as well as adding the field of house value prediction with results in the attribute table. In this script, we use a model shown below: House Value Prediction House Value = 60320 + 5.3* Median Income - 1.3* Population Density + 556* Teacher Student Ratio Using this model, we predict the house value of Cape May County, which is shown in the map on the right.

Median Income

Population Density

Teach-Student Ratio

House Value 279200 - 406100 Median Income med_inc

7 - 2014

406101 - 489700

Teacher Student Ratio

489701 - 589000

7.5 - 10.1

42676.5 - 65793.67

2015 - 5422

10.2 - 11.3

589001 - 706200

65793.68 - 81072.50

5423 - 11993

11.4 - 12.1

706201 - 949800

98556.46 - 120836.00

11994 - 24836

12.2 - 12.8

120836.01 - 166772.33

24837 - 58821

12.9 - 14.4

81072.51 - 98556.45

30

Pop Density

0

20 Miles


Python Script “”” THIS SCRIPT PREDICTS, REPORTS, AND RECORDS THE HOUSE VALUE OF EACH FEATURE IN A SPECIFIED SHAPEFILE, USING MEDIAN INCOME VALUE, POPULATION DENSITY VALUE AND TEACHER STUDENT RATIO VALUE OF EACH FEATURE. To create an ArcToolbox tool with which to execute this script, do the following. 1. In ArcMap > Catalog > Toolboxes > My Toolboxes, either select an existing toolbox or rightclick on My Toolboxes and use New > Toolbox to create (then rename) a new one. 2. Drag (or use ArcToolbox > Add Toolbox to add) this toolbox to ArcToolbox. 3. Right-click on the toolbox in ArcToolbox, and use Add > Script to open a dialog box. 4. In this Add Script dialog box, use Label to name the tool being created, and press Next. 5. In a new dialog box, browse to the .py file to be invoked by this tool, and press Next. 6. In the next dialog box, specify the following inputS (using dropdown menus wherever possible) before pressing OK or Finish. DISPLAY NAME Input Shapefile? Input Field of Median Income? Input Field of PopDense? Input Field of TeacherStudentRatio? Output Shapefile? Output Field?

DATA TYPE Shapefile Field Field Field Shapefile Field

PROPERTY>DIRECTION>VALUE Input Input Input Input Output Output

for “Input Shapefile”, “Input Field of Median Income”, “Input field of PopDense” and “Input of TeacherStudentRatio”, choose “Obtain from Input Shapefile”. 7. To later revise any of this, right-click to the tool’s name and select Properties. “”” # Import necessary modules import sys, os, string, math, arcpy, traceback # Allow output file to overwrite any existing file of the same name arcpy.env.overwriteOutput = True -try: # Request user input of data type = Shapefile and direction = Input nameOfInputShapefile = arcpy.GetParameterAsText(0) arcpy.AddMessage(‘\n’ + “The input shapefile name is “ + nameOfInputShapefile) # Request user input of data type = Field and direction = Input nameOfMedianIncomeField = arcpy.GetParameterAsText(1) arcpy.AddMessage(‘\n’ + “The name of the Median Income field used is “ + nameOfMedianIncomeField) # Request user input of data type = Field and direction = Input nameOfMedianIncomeField = arcpy.GetParameterAsText(1) arcpy.AddMessage(‘\n’ + “The name of the Median Income field used is “ + nameOfMedianIncomeField) # Request user input of data type = Field and direction = Input nameOfPopDensityField = arcpy.GetParameterAsText(2)

arcpy.AddMessage(‘\n’ + “The name of the Population Density field used is “ + nameOfPopDensityField) # Request user input of data type = Field and direction = Input nameOfTeacherStudentRatioField = arcpy.GetParameterAsText(3) arcpy.AddMessage(‘\n’ + “The name of the Teacher Student Ratio field used is “ + nameOfTeacherStudentRatioField) # Request user input of data type = Shapefile and direction = Output nameOfOutputShapefile = arcpy.GetParameterAsText(4) arcpy.AddMessage(“The output shapefile name is “ + nameOfOutputShapefile) # Request user input of data type = field and direction = Outnput nameOfHouseValueField = arcpy.GetParameterAsText(5) arcpy.AddMessage(“The name of the House Value field to be added is “ + nameOfHouseValueField + “\n”) # Replicate the input shapefile and add a new field to the replica arcpy.Copy_management(nameOfInputShapefile, nameOfOutputShapefile) arcpy.AddField_management(nameOfOutputShapefile, nameOfHouseValueField, “Long”, 10, 5) # Create an enumeration of updatable records from the shapefile’s attribute table enumerationOfRecords = arcpy.UpdateCursor(nameOfOutputShapefile) # Loop through that enumeration, calculating each row’s house value - for nextRecord in enumerationOfRecords: # Retrieve the value of median income, population density and teacher student ratio from the table and then calculate the house value medianIncome = nextRecord.getValue(nameOfMedianIncomeField) popDensity = nextRecord.getValue(nameOfPopDensityField) teacherStudentRatio = nextRecord.getValue(nameOfTeacherStudentRatioField) housevalue = 60320 + 5.3* medianIncome - 1.3* popDensity + 556*teacherStudentRatio nextRecord.setValue(nameOfHouseValueField, housevalue) enumerationOfRecords.updateRow(nextRecord) arcpy.AddMessage(“ Median Income = “ + str(nextRecord.getValue(nameOfHouseValueField))) # Add a blank line at the bottom of the printed list arcpy.AddMessage(‘\n’) # Delete row and update cursor objects to avoid locking attribute table del nextRecord del enumerationOfRecords -except Exception as e: # If unsuccessful, end gracefully by indicating why and where arcpy.AddError(‘\n’ + “Script failed because: \t\t” + e.message ) exceptionreport = sys.exc_info()[2] fullermessage = traceback.format_tb(exceptionreport)[0] arcpy.AddError(“at this location: \n\n” + fullermessage + “\n”)

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Geovisualization of Travel Activities in Philadelphia The following maps generated by ArcGIS are showing the travel activities (number of trips) during 24 hours using Household Travel Survey data collected by Delaware Valley Regional Planning Commission (DVRPC).

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Before 6am

6am - 8am

8am - 10am

10am - 12am

12am - 2pm

2pm - 4pm

4pm - 6pm

After 6pm


Carpool Route Optimization An optimized carpool route is generated based on the existing street grid using Network Analysis in ArcGIS. In this project, there are five origins and five destinations, and the shortest route is shown in the map on the left.

Route for Carpool

Origins

Destinations

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Flight Flow in the World This project maps 59,0000 flight path in the world using data from OpenFlight. Lines in the map are linking the origin airport and destination airport. The map is generated using ArcGIS and QGIS.

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Earth at Night This map generated by ArcGIS Online shows the nighttime view of the earth. The lines on the map represents highways, roads and railroads. Electric light, which could be regarded as a symbol of urban development, is concentrated along transportation system.

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Mapping Regional Community in USA This Project examines the geography of commuting in the contiguous United States, using tract-to-tract travel data of US Census Bureauâ&#x20AC;&#x2122;s American Community Survey (ACS). This map on the left shows the spatial patterns of commuting less than 100 miles between census tracts in the lower 48 states. The yellow hot spots are places where trips happens the most. This maps helps to understand how individual towns and cities connect, how population is distributed and the look of economic geography of the nation. This map is generated using ArcGIS and QGIS.

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Jing Liu liujing7@design.upenn.edu PennDesign, University of Pennsylvania

City Planning/Transportation Planning/GIS Portfolio - Jing Liu  
City Planning/Transportation Planning/GIS Portfolio - Jing Liu  
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