BAIXIN REN Selected works | 2013-2019
REGIONAL STRATEGY PROJECT
MIDWEST CITY STUDY LOCATION Chicago, IL DATE 2018 SCALE Midwest Region MY ROLE Lead urban planner
This research project identified and analyzed major population and lifestyle trends that impact urban development in the Midwest region of the U.S. The study revealed three macro trends within the region: a shifting population, challenges of de-industrialization, an increase in connectivity and demands for life quality. While migration to the nation’s urban city centers remains a defining micro trend, Midwest cities continue to lose population as people relocate to South and both coasts. The research was a high level overview that help the team evaluate the impact of its current project works within the broader context of the region, and make effective strategies for future opportunities.
SHIFTING POPULATIONS
3 CHICAGO
INDIANAPOLIS
COLUMBUS
DETROIT
MILWAUKEE
CHICAGO
INDIANAPOLIS
2,719,000
852,000
822,000
688,000
599,000
610,552M
125,864M
2,896,000
792,000
715,000
945,000
597,000
476,446M
110,943M
28
14
KANSAS CITY
MINNEAPOLIS
CLEVELAND
ST LOUIS
CINCINNATI
KANSAS CITY
MINNEAPOLIS
468,000
400,000
392,000
318,000
299,000
121,638M
235,733M
442,000
382,000
476,000
347,000
296,000
107,411M
200,768M
13
36
Population 2013 Population 2000 (Decline / Growth)
3
26
30
CHICAGO
INDIANAPOLIS
COLUMBUS
DETROIT
MILWAUKEE
610,552M
125,864M
117,824M
236,500M
97,307M
476,446M
110,943M
96,482M
197,789M
86,779M
28
14
27
21
29
KANSAS CITY
MINNEAPOLIS
CLEVELAND
ST LOUIS
CINCINNATI
121,638M
235,733M
124,609M
149,951M
121,407M
107,411M
200,768M
109,190M
134,290M
104,120M
GDP Growth Rate Below National Average (Number is National Total GDP Ranking) GDP Growth Rate Above National Average (Number is National Total GDP Ranking)
2
26
High Growth (>1.25%) Normal Growth (<1.25%) Decline
3
FROM RUST BELT TO BRAIN BELT CHICAGO DETROIT
MINNEAPOLIS DETROIT
Mining, Logging Mining, Logging and Construction and Construction
1.5
1.5
1.0
1.0
0.5
0.5
1.5 1.5
Trade, Transportation Trade, Transportation & Utilities & Utilities
Other OtherServices Services
Financial Activities
Leisure Leisureand and Hospitality Hospitality
FINANCIAL ACTIVITIES
Information Information
Financial FINANCIAL Activities ACTIVITIES
Education Educationand and Health HealthServices Services
PROFESSIONAL & PROFESSIONAL & BUSINESS SERVICES BUSINESS SERVICES
PROFESSIONAL PROFESSIONAL&& BUSINESS BUSINESSSERVICES SERVICES
KANSAS ST. CITY LOUIS
KANSAS CITY CINCINNATI
Mining, Logging Mining, Logging and Construction and Construction
Other Services Other Services
Mining, Mining,Logging Logging and andConstruction Construction
2.5
2.5
2.0
2.0
2.0 2.0
1.5
1.5
1.5 1.5
1.0
1.0
0.5
0.5
2.5 2.5
Government Government
Manufacturing Manufacturing
Trade, Transportation Trade, Transportation & Utilities & Utilities
Other OtherServices Services
Financial FINANCIAL Activities ACTIVITIES
Information Information
FINANCIAL FINANCIAL ACTIVITIES ACTIVITIES
Educationand and Education HealthServices Services Health
PROFESSIONAL & PROFESSIONAL & BUSINESS SERVICES BUSINESS SERVICES
PROFESSIONAL Professional & & Business Service BUSINESS SERVICES
CLEVELAND COLUMBUS
INDIANAPOLIS CLEVELAND
Mining, Logging Mining, Logging and Construction and Construction
Other Services Other Services
Mining, Mining,Logging Logging and andConstruction Construction
2.5
2.5
2.0
2.0
2.0 2.0
1.5
1.5
1.5 1.5
1.0
1.0
0.5
0.5
Manufacturing MANUFACTURING
OtherServices Services Other
Financial Activities
Information Information
EDUCATION Education and HealthSERVICES Services HEALTH
FINANCIAL ACTIVITIES
Financial Financial Activities Activities
Professional & & PROFESSIONAL Business Service BUSINESS SERVICES
+9,500 Minneapolis,MN
+13,500 Washington, DC
+14,800 Austin, TX
+20,900 Houston, TX
+25,500 Dallas/Ft.Worth, TX +24,700 Seattle, WA
Trade, Transportation TRADE, TRANS Utilities &&UTILITIES
1.0 1.0
Leisureand and Leisure Hospitality Hospitality
Information Information
BUSINESS SERVICES
+89,600 SF Bay Area, CA
Manufacturing MANUFACTURING
0.5 0.5
Professional & PROFESSIONAL & Business Service
4
2.5 2.5
Government Government
Trade, Transportation Trade, Transportation & Utilities & Utilities
Leisure and Leisure and Hospitality Hospitality
EDUCATION Education and Health Services HEALTH SERVICES
Trade,Transportation Transportation Trade, Utilities &&Utilities
1.0 1.0
Leisure and LEISURE & Hospitality HOSPITALITY
Information Information
GovernmentGovernment
Manufacturing MANUFACTURING
0.5 0.5
LEISURE & Leisure and Hospitality HOSPITALITY
Education and Education and Health Services Health Services
Trade, Trade,Transportation Transportation &&Utilities Utilities
1.0 1.0 0.5 0.5
Information Information
GovernmentGovernment
MANUFACTURING MANUFACTURING
2.0 2.0
Leisure and Leisure and Hospitality Hospitality
Education and Education and Health Services Health Services
2.5 2.5
BRAIN GAIN
2.0
Government Government
+1,400 New York, NY +1,000 St. Louis, MO
2.0
Mining, Mining,Logging Logging and andConstruction Construction
MANUFACTURING Manufacturing
+2,300 Detroit, MI +2,100 Denver, CO
2.5
+3,300 Columbus, OH
Other Services Other Services
2.5
+6,700 Kansas City, MO
GovernmentGovernment
NEW YORK Mining, Logging and Construction Government
2.5
Manufacturing
2.0 1.5
Other Services
Trade, Transportation & Utilities
1.0 0.5
INFORMATION
Leisure and Hospitality
FINANCIAL ACTIVITIES
Education and Health Services Professional & Business Service
LOS ANGELES Mining, Logging and Construction Government
2.5
Manufacturing
2.0 1.5
Other Services
Trade, Transportation & Utilities
1.0 0.5
LEISURE & HOSPITALITY
INFORMATION
Education and Health Services
Financial Activities Professional & Business Service
SAN FRANCISCO
In Chicago, 34.9% growth in tech jobs from 20102015.Ranked 5TH in the country after SF BAY AREA, DC, NEW YORK, and DALLAS.
Mining, Logging and Construction Government
2.5
Manufacturing
2.0 1.5
Other Services
Trade, Transportation & Utilities
1.0 0.5
CHICAGO
4TH in TECH DEGREE COMPLETIONS, with 10,454 graduates in 2014. DETROIT
MINNEAPOLIS ST LOUIS
KANSAS CITY
INFORMATION
Leisure and Hospitality
CINCINNATI
However, we are experiencing a NET LOSS of tech jobs relative to graduates. Where are they headed? How do we ATTRACT AND RETAIN them? COLUMBUS
CLEVELAND
INDIANAPOLIS
Financial Activities
NEW YORK
LOS ANGELES
- 11,200 Los Angeles, CA
SAN FRANCISCO
- 10,000 Pittsburgh, PA
- 4,400 Philadelphia, PA - 5,100 Miami, FL
- 1,600 Madison, WI
-700 Indianapolis, IN - 1,300 Chicago, IL
BRAIN DRAIN
PROFESSIONAL & BUSINESS SERVICES
- 17,200 Boston, MA
Education and Health Services
5
INDUSTRY CONCENTRATION LRT REBUILD
ELIMINATE
Montrose Beach
LRT RESTORE
A HISTORY OF LAKE SHORE DRIVE 1909
1917
1930 - 1959
NOW
-Initially proposed as “Bloulevard” -Combination of park and driveway
-Become major thoroughfare -Limited stops
-Expansion
-Resurfacing -Spot Improvement -Bridges, lower pavement layers 80yrs old
REBUILD LRT
NEW LRT
Belmont Harbor
REBUILD
LRT
LRT
NEW
Diversey Harbor
North Pond
REBUILD LRT
Enhanced Pedestrian Connections (bridges and pathways)
LRT
Lincoln Park Zoo
Lagoon A
Expanded Lakefront Habitat and Parkland
LRT
South Pond A
REBUILD
North Avenue Beach
LRT LRT
Gold Coast Promenade
ELIMINATE LRT
Lakefront Cultural Plaza Streeterville Promenade
LRT
ELIMINATE LRT
LRT
Navy Pier LRT
LRT
LRT
LRT
LRT LRT
LRT
LRT
LRT
LRT
LRT
6
Grant Park
LAKE SHORE DRIVE IMPROVEMENT PLAN
CONNECTIVITY WITHOUT BOARDERS
LEVER AGE OUR AIRPORT
MSP ORD MCI
MDW
STL
DTW
TOP AIRPORTS FOR REAL ESTATE INVESTMENT
CLE
IND CMH CVG
160 AIR CARGO SCORE
REAL ESTATE SCORE
140 76.5
0-5K 5K - 10K 10K - 50K 50K - 80K 80K - 100K 100K - 150K 150K - 200K 200K - 250K 250K - 300K 300K - 415K
120 62.7 55.3 100
48.4
62.7 38.4 66.9
80
50.4
23.0 53.2
29.2
60
42.3
43.8
40
20
68.1
58.2
61.9
64.2
49.5
62.1
25.5
41.1
53.2
46.9
28.6
26.3
MIA
LAX
MEM
DFW
SDF
EWR
IND
ANC
JFK
OAK
ATL
0
ORD
CAPITALIZE ON OUR REGIONAL R AIL 433K 741K
1,538K 792K 1,460K 1,163K
206K
206K 570K 363K
223K
1,343K
288K
102K
HIGH-SPEED R AILWAY POSSIBILITIES
12,207K
2,393K 313K
Amtrak Routes Passengers Amtrak stations Amtrak other services (bus)
7
8
CHICAGO ACTIVE PLACES
LIVABILITY IN ALL ASPECTS COST OF LIVING CHICAGO
DETROIT
MINNEA POLIS M
ST. LOUIS
KS CITY
CINCINNATI
COLUMBUS C
CLEVELAND C
MADISON
DES MOINES D
S. F.
PHILADELPHIA P
NEIGHBORHOOD
65 62 64 80
L. A.
MINNEA M POLIS
ST. LOUIS
KS CITY
CINCINNATI
COLUMBUS
CLEVELAND C
MADISON
DES MOINES
S. F.
PHILADELPHIA P
TRANSPORTATION
INDIANAP OLIS ROCHESTER
NEW YORK
L. A.
DETROIT
MINNEA M POLIS
ST. LOUIS
KS CITY
CINCINNATI
COLUMBUS C
C CLEVELAND
MADISON
DES MOINES
S. F.
PHILADELPHIA P
HEALTH & WELLNESS
ROCHESTER INDIANAP OLIS R
L. A.
NEWYORK
DETROIT
MINNEA POLIS M
ST. LOUIS
KS CITY
CINCINNATI
COLUMBUS
CLEVELAND C
MADISON
DES MOINES D
S. F.
PHILADELPHIA P
47 48 48 51 INDIANAP IN OLIS R ROCHESTER
52 69 72 51
53 70 68 57 L. A.
NEW YORK
83 59 85 78
55 43 71 54
$
$
HOUSING ACCESSIBILITY
$
NTA
ATL A
ELP
AS
DA LL
NC
S
ISC
AD
PHIL
S INE
GE LE
FRA
SAN
MO
AN
LOS
DES
LIS
TER
ON
HES
DIS
MA
D AN
APO
IAN
RO C
IND
I
BU S
VEL
CLE
ITY
NAT
LUM
CO
SAS C
CIN
CIN
POL IS
IS
LOU
KAN
ST
RO IT
NEA
HIA
CHICAGO
DETROIT
KS CITY
CINCINNATI
65 62 65 66
HOUSING OPTIONS $
MIN
COST OF
$
COST OF LIVING
DET
CH
IAC GO
O
81 70 76 70
CHICAGO
55 52 72 41
56 67 65 70
49 55 64 53
55 58 56 64
CHICAGO
74 56 82 68
55 59 55 65
68 64 70 69 NEWYORK
DETROIT
72 56 70 55
65 66 62 69 INDIANAP IN OLIS ROCHESTER
CHICAGO
$
INDIANAPOLIS ROCHESTER
68 64
HOUSING AFFORDABILITY
$
$
NEW YORK
L. A.
55 58
HOUSING SUBSIDIZING
NEIGHBO
PROXIMITY TO GROCERY AND MARKETS
NEIGHBORHOOD QUALITY
CHICAGO
DETROIT
KS CITY
CINCINNATI
72 56 55 59
PROXIMITY TO PARKS
INDIANAPOLIS ROCHESTER
49 55 NEW YORK
PROXIMITY TO LIBRARIES
L. A.
81 70
ACCESS TO JOBS BY TRANSIT bus cta stop Clark
Rogers
Effective December 20, 2015 Howard 7600N
7400N
Howard Terminal Red/Purple/Yellow lines
Devon 6400N Foster 5200N
N
Clark
Irving Park 4000N
Wrigley Field
Addison 3600N Belmont 3200N@Halsted Diversey 2800N@Broadway Armitage 2000N
Lincoln Park Zoo Chicago History Museum Dearborn 30W
North 1600N
22
Walton 932N
Clark/Lake station
State 0E/W
Clark 100W
Division 1200N Clark/Division station Red Line
Blue/Brown/Green/Orange/ Pink/Purple lines
Randolph 150N Washington 100N (Night Owl Route)
Harrison Polk 800S
N22 Night Owl Service via
ACCESS TO JOBS BY AUTO
TRANSIT CONVENIENCE
MIXED-USE NEIGHBORHOODS
COMPACT NEIGHBORHOODS
PERSONAL SAFETY
VACANCY RATE
HEALTH & WELLNESS
bus cta stop Clark
Rogers
Effective December 20, 2015 Howard 7600N
7400N
Howard Terminal Red/Purple/Yellow lines
Devon 6400N Foster 5200N
N
Clark
Irving Park 4000N
Wrigley Field
Addison 3600N Belmont 3200N@Halsted Diversey 2800N@Broadway Armitage 2000N
Lincoln Park Zoo Chicago History Museum Dearborn 30W
North 1600N
Walton 932N
Clark/Lake station
State 0E/W
Clark 100W
Division 1200N Clark/Division station Red Line
Blue/Brown/Green/Orange/ Pink/Purple lines
Randolph 150N Washington 100N (Night Owl Route)
Harrison Polk 800S
N22 Night Owl Service via
FREQUENCY OF TRANSIT
22
TRANSPO CHICAGO
DETROIT
KS CITY
CINCINNATI
74 56 WALK TRIPS
56 67
INDIANAPOLIS ROCHESTER
52 69
HAN B UI IN T LD DL I
COST OF LIVING
! UT -O
BU
CONGESTION HOURS
NEIGHBORHOOD QUALITY
TRANSIT HEALTH & CONVENIENCE WELLNESS
NEW YORK
L. A.
83 59
SAFE STREETS
WATER QUALITY
AIR QUALITY
HEALTH & CHICAGO
DETROIT
KS CITY
CINCINNAT
55 52
47 48
INDIANAPOLIS ROCHESTER
53 70
SMOKING PREVALENCE
NEW YORK
L. A.
55 43
OBESITY PREVALENCE
EXERCISE OPPORTUNITIES
ACCESS TO HEALTH CARE
QUALITY OF HEALTH CARE
9
WORKPLACE RESEARCH PROJECT
WORKPLACE FOR COMMUTERS LOCATION Philadelphia DATE 2017 SCALE Philadelphia Metropolitan MY ROLE Lead urban planner
In 2017, a client considered to add a new regional office to help shorten employees’ daily commute distance. These employees are currently based in 3 major and 12 other offices. Based on a comprehensive list of current employees’ home zipcode and their current work locations, the study quantified the total number of employees who reduced commute distance, and the total distance of mileage saved. This research helped the client to decide which new location can best benefit employees.
ASSUMPTIONS • Zipcode boundaries are based on TIGER/Line Shapefile, 2015, 2010 nation, U.S., 2010 Census 5-Digit ZIP Code Tabulation Area (ZCTA5) National • For all employees, commute start point are based on the center of each Zipcode district centroid. • 99.5% people who commute drive cars. All commute distance is calculated by straight lines (from zipcode centroid to workplace point), not specific driving route.
APPROACH
Geolocate
Distance
Home Home
Home
Home Home
Workplace
Compare
Dist (Home-to-Current Workplace) Dist (Home to Proposed Workplace)
Home Home
Workplace
Current Workplace
Proposed Workplace
-For employee, the geo-locating bridge is ‘Zipcode’ - Home zip and standard Zipcode map -For workplaces, they are based on latitude/longitude given by the client 10
1. PROJECT EMPLOYEES AND WORK LOCATIONS • Existing Work locations include 15 places. • There are 3 major work locations: -Spring House -Horsham -Malvern
G G G
SPRING HOUSE MALVERN
GG G
GG
HORSHAM
G G
FORT WASHINGTON
G DEPUY G WEST CHESTER
• There are 12 other work locations: -NJ Bridgewater US NJ Rt. 22 East -NJ New Brunswick J&J Plaza -NJ Pennington American Boulevard -NJ Piscataway Centennial Avenue -NJ Piscataway Hoes Lane -NJ Raritan Rt 202S (Pharma) -NJ Somerville Rt. 22 -NJ Titusville Trenton-Harbourton Road -PA Fort Washington Camp Hill Road -PA Fort Washington Delaware Drive -PA West Chester Airport Rd. Area -PA West Chester Lawrence Drive
ALL EMPLOYEES PROJECTED
SPRING HOUSE BASED EMPLOYEES
• 2 Touchpoint locations are: -DePuy West Chester Airport Road -Fort Washington • 3,409 Pharma employees are assigned in the PA region. • All 100% employees are assigned to the 15 workplaces. HORSHAM BASED EMPLOYEES
SUMMARY - EMPLOYEES COMMUTE DISTANCE DISTANCE TO WORKPLACES 0-5 MILE
EMPLOYEES #
%
422
12.4%
5-10 MILE
814
23.9%
10-20 MILE
1287
37.8%
20-30 MILE
509
14.9%
30-40 MILE
226
6.6%
ABOVE 40 MILE
151
4.4%
SUM
3409
100.0%
MALVERN BASED EMPLOYEES 11
2. VISUALIZE THE DISTANCE DIFFERENCE • For the 1643 Spring House based employees, re-calculate the direct distance from home zipcode address to Spring house, home to Fort Washington and to Depuy West Chester. And repeat the same process for 807 Horsham based employees, 841 Malvern based employees and 118 other place based employees. • By visualizing the commute pattern it can be noticed that, for each individual employee, the wider angles, the more different from their original direction. The closer to the centroid of all homes, the better for employees on average.
SPRING HOUSE
HORSHAM
FORT WASHINGTON
DEPUY WEST CHESTER
FORT WASHINGTON
DEPUY WEST CHESTER
SPRING HOUSE BASED EMPLOYEES COMMUTE
HORSHAM BASED EMPLOYEES COMMUTE
GG G GG G
GG
GG FORT
FORT WASHINGTON
WASHINGTON
MALVERN DEPUY WEST CHESTER
MALVERN BASED EMPLOYEES COMMUTE
DEPUY GG WEST CHESTER
OTHER LOCATION BASED EMPLOYEES COMMUTE
3. BENEFITS COMPARISON If Depuy West Chester is chosen as the additional workplace, 804 of the total 3,409 employees would be able to shorten their commute distance. • The 435 employees who currently based in Spring House can save 12.2 miles on average. • Employees who based in Horsham, their average saved commute mileage is the highest. • The 229 Malvern based employees didn’t shorten their commute distance significantly.
12
People who can shorten their commute distance if select Depuy as the additional workplace
SPRING HOUSE
HORSHAM
DEPUY WEST CHESTER
DEPUY WEST CHESTER
SPRING HOUSE BASED EMPLOYEES
HORSHAM BASED EMPLOYEES GG G
end
GG G
Main_Locations Touchpoint_Locations
ernCompareDistDots 1.000000 - 5.000000
GG
5.000001 - 10.000000
Commute distance shortened by
10.000001 - 15.000000 15.000001 - 30.000000
GG
MALVERN
30.000001 - 51.000000
Increased 0-5 Mile
DEPUY WEST CHESTER
1.000000 - 5.000000
5-10 Mile
G DEPUY G WEST CHESTER
5.000001 - 10.000000 10.000001 - 15.000000
10-15 Mile
15.000001 - 30.000000
15-20 MIle >20 Mile
30.000001 - 51.000000
1.000000 - 5.000000
MALVERN BASED EMPLOYEES
OTHER LOCATION BASED EMPLOYEES
# of People
5.000001 - 10.000000 10.000001 - 15.000000 15.000001 - 30.000000 30.000001 - 51.000000
DEPUY WEST CHESTER Current base employees
# of Employees who Percentage of get shorter commute Employees based in distance current location
Average Commute Saved Miles Distance shortened Range (Mile)
Total Miles Saved
SPRING HOUSE
435
26.5%
12.2
0.4-23.7
5307
HORSHAM
121
15.0%
12.9
1.5-26.3
1560.9
MALVERN
229
27.2%
2.5
0.1-5.1
572.5
OTHER
19
16.1%
14.2
0-61.4
269.8
TOTAL
804
7710.2
# OF PEOPLE INCREASE COMMUTE DISTANCE 2030Over 0-5 NOT 0 -10 10-20 30 40 40 Mile Sum IMPACTED Mile Mile Mile Mile Miles Sum 101 435 0 300 390 518 0 0 1208 17 121 0 139 156 391 0 0 686 206 229 0 612 0 0 0 0 612 3 19 4 8 18 15 13 41 95 327 804 4 1059 564 924 13 41 2601
# OF PEOPLE SHORTEN COMMUTE DISTANCE Current base employees SPRING HOUSE HORSHAM MALVERN OTHER TOTAL
Over 40 Miles 0 0 0 1 1
3040 Mile 0 0 0 2 2
2030 Mile 124 35 0 3 162
1020 Mile 94 32 0 5 131
5-10 Mile 116 37 23 5 181
TOTAL 1643 807 841 118 3409
13
People who can shorten their commute distance if select Fort Washington as the additional workplace
SPRING HOUSE
end
eDistDots Fort Main_Locations
HORSHAM
FORT WASHINGTON
Touchpoint_Locations
FORT WASHINGTON
ernCompareDistDots Fort
0
0 1.000000 - 5.000000
0
5.000001 - 10.000000 10.000001 - 15.000000 15.000001 - 30.000000
00
30.000001 - 51.000000
SPRING HOUSE BASED EMPLOYEES
HORSHAM BASED EMPLOYEES
1.000000 - 5.000000
GG G
0 5.000001 - 10.000000
GG G
0 10.000001 - 15.000000
0
15.000001 - 30.000000
Legend
30.000001 - 51.000000
00 1.000000 - 5.000000
G
Main_Locations
G
Touchpoint_Locations
5.000001 - 10.000000
OtherCompareDistDots
10.000001 - 15.000000
>20
FORT1.000000 - 2.000000 WASHINGTON
15.000001 - 30.000000
MALVERN
30.000001 - 51.000000
GG
GG FORT
2.000001 - 5.000000
WASHINGTON
15-20 1.000000 - 2.000000 1.000000 - 5.000000 2.000001 - 5.000000 5.000001 - 10.000000
10-15
10.000001 - 15.000000
1.000000 - 2.000000
15.000001 - 30.000000
2.000001 - 5.000000
5-10
30.000001 - 51.000000
1.000000 - 5.000000
1.000000 - 2.000000
MALVERN BASED EMPLOYEES
OTHER LOCATION BASED EMPLOYEES
2.000001 - 5.000000
0-5
5.000001 - 10.000000
1.000000 - 2.000000
10.000001 - 15.000000 15.000001 - 30.000000
GG
2.000001 - 5.000000
If Fort Washington is chosen as the additional workplace, 1130 of the total 3,409 employees would be able to shorten their commute distance. • Though 705 Spring House based employees would be able to shorten commute distance, they can only save up to 4.5 miles. • 299 Horsham based employees can save up to 5.8 miles in commute distance. • 288 in Malvern would benefit most in this assumption, their commute distance can be reduced by an average of 10.5 Miles. >=0
30.000001 - 51.000000
1.000000 - 2.000000 2.000001 - 5.000000
FORT WASHINGTON Current base employees
# of Employees who Percentage of get shorter commute Employees based in distance current location
Average Commute Saved Miles Distance Range shortened (Mile)
Total Miles Saved
SPRING HOUSE
705
42.9%
2.5
0.1-4.5
1762.5
HORSHAM
299
37.1%
4
0.2-5.8
1196
MALVERN
288
34.2%
10.5
0.7-18.3
3024
OTHER
38
32.2%
13.2
0-42.8
501.6
TOTAL
1330
6484.1
# OF PEOPLE INCREASE COMMUTE DISTANCE 2030Over 0-5 NOT 0 -10 10-20 30 40 40 Mile Sum IMPACTED Mile Mile Mile Mile Miles Sum 705 705 0 938 0 0 0 0 938 197 299 0 508 0 0 0 0 508 66 288 0 189 364 0 0 0 553 12 38 4 19 15 22 9 11 76 980 1330 4 1654 379 22 9 11 2075
# OF PEOPLE SHORTEN COMMUTE DISTANCE Current base employees SPRING HOUSE HORSHAM MALVERN OTHER TOTAL 14
Over 40 Miles 0 0 0 2 2
3040 Mile 0 0 0 4 4
2030 Mile 0 0 0 3 3
1020 Mile 0 0 149 13 162
5-10 Mile 0 102 73 4 179
TOTAL 1643 807 841 118 3409
4. SUMMARY This summary is based on the assumption that only people get shorter distance will relocate to the new work station. TAKE-AWAYS
EXISTING
• Fort Washington option can benefit the largest group of employees, which can help as many as 1,330 employees shorten their commute distance. • However, Fort Washington is very close existing offices Spring House and Horsham that people who already work here would not necessary relocate to save a few miles due to operational efficiency. • Depuy can help 804 employees to save their commute distance with a total mileage of 7,710. It can help capture employees who commute from southwest to Spring House or Horsham.
• Further comparison for locations will need to include operational management factor at a departmental scale.
<10 Mile
0-10 M 10-30 M 45.3% 45.9%
1544
10-30M 52.7%
0-10 M 36.3%
<10 Mile
1236
36.3%
10-30 Mile
1796
52.7%
>30 Mile
377
11.1%
SUMMARY - EMPLOYEES COMMUTE DISTANCE DISTANCE TO WORKPLACES
#
EMPLOYEES %
0-5 MILE
422
12.4%
5-10 MILE
814
23.9%
10-20 MILE
1287
37.8%
20-30 MILE
509
14.9%
30-40 MILE
226
6.6%
ABOVE 40 MILE
151
4.4%
SUM
3409
100.0%
IF FORT WASHINGTON SELECTED
IF DEPUY WEST CHESTER SELECTED >30 M 8.8%
>30 M 11.1%
45.3%
10-30 Mile
1565
45.9%
>30 Mile
300
8.8%
SUMMARY - EMPLOYEES COMMUTE DISTANCE
>30 M 10.1%
0-10 M 10-30 M 39.6% 50.3%
<10 Mile
1349
39.6%
10-30 Mile
1714
50.3%
>30 Mile
346
10.1%
SUMMARY - EMPLOYEES COMMUTE DISTANCE
DISTANCE TO WORKPLACES
#
%
DISTANCE TO WORKPLACES
#
%
0-5 MILE
596
17.5%
0-5 MILE
539
15.8%
EMPLOYEES
EMPLOYEES
5-10 MILE
948
27.8%
5-10 MILE
810
23.8%
10-20 MILE
1257
36.9%
10-20 MILE
1224
35.9%
20-30 MILE
308
9.0%
20-30 MILE
490
14.4%
30-40 MILE
171
5.0%
30-40 MILE
198
5.8%
ABOVE 40 MILE
129
3.8%
ABOVE 40 MILE
148
4.3%
SUM
3409
100.0%
SUM
3409
100.0% 15
16
PUBLIC REALM RESEARCH PROJECT
USING GIS TO MEASURE THE QUALITY OF URBAN PUBLIC SPACE LOCATION City of Chicago DATE 2016 SCALE City MY ROLE Lead urban planner
This project aims at providing designers an intelligent tool working through a process to decompose urban design terms into factors that can quantitatively measure how good a public space is, analyze them, and construct a new grading system with these new usable indicators. Currently, lacking of effective and objective measurement for quality of urban public space has prevented collaboration between urban public space design and geographic information system. Urban designers usually use perceptual terms when describing and assessing a place. Public urban spaces in the city of Chicago are selected as a sample for tool-building and demonstration. The product can be applied in the real world for three purpose: case study, preliminary site analysis and design decision-making.
17
ABSTRACT Lack of effective and objective measurement for quality of urban public space has prevented collaboration between urban public space design and geographic information system (GIS). Urban designers usually use perceptual terms when describing and assessing a place. The current project aims at providing designers an intelligent tool working through a process to decompose these urban design terms into factors that can quantitatively measure how good a public space is, then analyze them, and construct a new grading system with these new usable indicators. The indicators included in this project are land use, demographics, neighborhood, transit network, retail and space feature. Public urban spaces in the city of Chicago are selected as a sample for tool-building and demonstration. The product can be applied in the real world for three purpose: case study, preliminary site analysis and design decision-making.
CONTEXT With the world’s population growth and cities urbanization, people who live in the urban area care more about the space quality that they live in every day. As urban designers or planners, we always try to find a way to make our cities more livable and attractive. Perceptually, public spaces are usually seen as an influential urban feature in evaluating the city’s living quality. Many of them are geographically located at a dominant place in the city, while either historically or culturally remarkable. So the success or failure of these public spaces matters a lot to the life quality of local neighborhoods. Urban designers has been exploring the ways of how to effectively assess a public space for some time. Among the methods, Kevin Lynch’s <The Image of City> reveals one of the most famous one--by survey. The book presented some results of how observers take in information of the city. He reported that users understood their surrounding in consistent and predictable ways, and then forming mental maps, which are mostly consisted of five basic elements—paths, edge, district, node and landmark. However, surveys and analysis by designers are more based on an instinctive and perceptual way of evaluation, thus they have limitations to some extent. Firstly, they are helpful for describing the overall qualities of a space that whether it is comfortable, appealing, intriguing and memorable or not, but people may not think about which elements really make this place work. Secondly, surveys usually take long time to get effective and enough feedback. Urban design projects are usually time intensive or of large scale, it is unfeasible to take surveys every time in depth. So in order to have a better understanding of the rational reasons which contributed to the success of a public space, urban designers need a quantitative tool for measurement.
18
This project is an individual research project aiming at exploring how Geographic information system can be effectively used in design realm. Inspired by the design and case study works from Public Realm Studio, the quality of public space is crucial to a city since successful public spaces can add great value to adjacent properties and to the city. Through the Advanced Topics in GIS class, a refined model to better scale the quality of an urban public space was developed.
PURPOSE The research proposed a way of evaluating public spaces by using multiple measures that could be quantified by GIS. Since the tool is created for urban designers, first of all, find out the key perceptual words that designers would consider to use in describing or assessing a public space. Secondly, check out each term that how physical factors can be decomposed from the general terms. In the process, GIS database will provide these basic factors like physical forms, socio-economic and environmental feature. Thirdly, analyze the decomposed factors, and use regression method to test which factors can be used as effective indicators. By integrating all customized indicators, we can construct a grading system used for quantify the space quality. This method could be used for case analysis, and in design process, it could be used as tool for site evaluation, alternative plans design and test, and decision-makings.
19
APPROACH: 1. Term translation There are 5 categories for indicators that we will potentially use in the measurement. Below Land Use Mix—Land Use—Vacancy, mix of land use. People—Demographics —Neighborhood density, education attainment, household income, and age. Accessibility—Transit network—including metro stations, bus stations, and transfer stations, bicycle network, and pedestrians. Proximity to energy and resource—Retail—Healthy food density and accessibility which includes fresh grocers and farmers markets, cafes, restaurants and shopping. Space quality—Space feature—Enclosure, imageability, building condition, block, street or building scale, attractiveness.
Urban Design Perceptual Description
20
Planning Terms
Decomposed Factors
Land use mix
Land use
Mix of land uses Vacancy
People
Demographics
Density Education Income Age
Neighborhood
Neighborhood
Crime
Accessibility
Transit network
Metro stations Bus stations Transfer stations Bicycle routes Pedestrians
Proximity to energy & resource
Retail
Fresh grocers Farmers markets Cafes Restaurants
Attractive
Space feature
Enclosure Imageability Historic or Modern
2. Data preparation After we translate perceptual terms into all fundamental factors for measurement in urban design realm, we need to see if these elements could be found from GIS database or collected from other sources. Below is a summary table that describes how each raw data from GIS database will be used and translated into urban design language. In this step, data interpolation only means the preliminary decompose, converting and recompose.
Category Land Use
Demographics
Neighborhood
Transit
Factor
Data Source
Description
Data interpolation
Vacancy
ACS 2013 Data
Table
Need geocoding
Land uses
Zoning_nov2012
Shapefile
No
Neighborhood density
CensusBlockTIGER2 Shapefile 010
Need recalculate
Education attainment
ACS 2013 Data
Table
Need geocoding
Household income
ACS 2013 Data
Table
Need geocoding
Age
ACS 2013 Data
Table
Need geocoding
Crime
Crimes_2001 to present.xls
Table
Need geocoding
Bus stations
CTA_BusStops
Shapefile
No
Metro stations
CTA_Stations
Shapefile
No
Transfer stations
CTA_RailLines & MetraLines
Shapefile
Need find intersections within a walkable radius
Shapefile
No
Table
Need geocoding and recalculate
Bicycle network Pedestrian streets Pedestrian Streets
Retail
Space quality
Healthy food density and access
Grocery_Stores & Farmers’ market
Shapefile
Cafes/Restaurants
Shapefile
Shopping
Shapefile Need viewshed processing
Enclosure
Buildings_2010
Imageability
LandmarkDistrict_n Shapefile ov2012
No
Building condition
Boundaries_Census Shapefile _Blocks_2010
No
Block and Street scale
Boundaries_Census Shapefile _Blocks_2010
Need calculation
Building scale
Buildings_2010
Shapefile
Need calculation
Attractiveness
ParkArt
Shapefile
No
Shapefile
21
3. Data analysis After all potential factors prepared, the second stage is to analyze each of their relevance to the quality of urban public space. So regression will help us in identifying the degree of relevance and the likely weight for each of the factor. Firstly, dependent variable y should be well selected. In this study, park scores based on Google reviews will be chosen as a scale of people’s opinion upon the quality of the space. There are two reasons for that: Firstly, the study aims at building a bridge between perceptual assessment and objective factors. Secondly, urban public space is used for serving people who live in an urban life, so people themselves are the best scale to assess the quality. In this step, I selected parks which have more than 5 people’s grades for it, and these places include civic parks, community parks and neighborhood parks. The score range is among 0-5, based on people’s average review grade. Since the precision is only 1 decimal figure, I re-scaled the range into 0-20 so that the regression result can be more reasonable.
Y DEPENDENT VARIABLE
1 5
Park score
Park buffer
Secondly, for each independent variables x, the data in park’s buffer area are selected. To achieve that, region group should be applied to all parks so that each of them will become an individual island. Then use zonal statistics to create a buffer area for the park. In this dataset, grid cell’s size is 30m*30m, which means 15 cells’ buffer can simply cover 2-3 blocks away from each study park. After that, apply zonal statistics to calculate the factor’s mean value, or variety value, or minimum value based on factor features. Therefore, each park has a series of data from different factors for the preparation of regression. 22
LAND USE MIX
Land use mix
Park place land use_variety
DEMOGRAPHIC
Population Density
Poverty ratio
Park place population density_mean
Park place poverty ratio_mean 23
What needs to point out is the last category, space quality data. For enclosure, in this regression analysis, the ratio between ‘surrounding average building height’ and ‘park area’ is used to measure the enclosure feeling. However, in individual cases, a better way to depict enclosure is to use viewshed function in Arc Toolbox. Firstly, build a tin layer based on selected buildings height information. Then convert the tin layer to raster file, and reclassify the layer to make the building topology sharper, in order to depict viewshed more accurate. And same steps to adjacent trees and other vegetation. Finally choose an appropriate point where people usually clustered, and apply viewshed to generate a sight layer for this public space. This method is not applicable here is because, model builder can never select a single point for all places. Imageability is based on the distance to city’s important assets and landmarks. And attractiveness is based on the number and distance to public arts. Historic or modernity feeling is based building’s age.
TRANSIT
Distance to CTA stations
Park place distance to CTA stations_mean
Distance to Farmers’ market
Park place distance to markets_mean
RETAIL
24
SPACE QUALITY
Building height
Park place building height_mean
Distance to Landmarks
Park place distance to landmark_mean
Building Ages
Park place building ages_mean 25
RESULTS Sampling function in GIS is applied to collect each park’s data. Each park’s score together with its series of independent variables are used to build up a regression model in IBM SPSS. In this case, multiple regression is one of a good analysis type. This is because, firstly, the model is based on multiple variables and the research is to explore the relationship between the people’s assessment to a public space and all other objective factors. Secondly, multiple regression allows people to determine the overall goodness of fit, and the relative contribution of each of the predictors to the total variance explained, which means the ‘relative contribution’ of each independent variable can be generated. The assumption made to run this regression is, dependent variable should be measured on a continuous scale rather than an ordinal scale. Below table is a part of the original data entered into the regression software. Next step is to interpret data from the regression result. The R represents the multiple correlation coefficient, while R square represents the proportion of variance in the dependent variable that can be explained by the independent variables. In this case, R is 0.736, indicating a good level of prediction; and R square is 0.541, meaning that our independent variables can explain 54.1% of the variability of the dependent variable.
R
Model 1
R Square .736
Adjusted R Square
.541
a
Std. Error of the Estimate
.541
3.459
In the Coefficients table (table in next page), the results show how each predictor variables—the variety of zoning, population density, poverty ratio, distance to bike routes, distance to CTA Station, distance to pedestrian streets, distance to farmers market, building height, distance to public art, distance to landmark and building age, correlates to the predicted Y. B values are for the regression equation in predicting. They are the coefficient for each factors and can be presented in this way.
R SQUARE 0.56 (Constant)
BS
td. Error
141.190
7.588
Standardized Coefficients Beta
tS
ig.
18.607
.000
LAND USE
ZONING_VARIETY
1.194
.032
.838
37.125
0.000
DEMOGRAPHICS
DENSITY_MEAN
-.058
.002
-.298
-36.663
0.000
.843
.138
16.275
0.000
.011
.000
.608
162.522
0.000
0.21
.000
-.319
-65.375
0.000
POVERTY RATIO_MEAN TRANSIT NETWORK DISTANCE TO BIKE ROUTES_MEAN DISTANCE TO CTA STATIONS_MEAN DISTANCE TO PEDESTRIAN STREET_MEAN RETAIL DISTANCE TO FARMERS MARKET_MEAN SPACE QUALITY BUILDING HEIGHT_MEAN DISTANCE TO PUBLIC ART_MINIMUM DISTANCE TO LAND MARK_MEAN BUILDING AGE_MEAN
26
Unstandardized Coefficients
-0.5
.000
.000
-.173
-28.763
0.000
-.001
.000
-.339
-46.813
0.000
.021
.071
.005
.297
.767
.000
.000
.185
39.270
0.000
.000
.000
.502
55.818
0.000
-.070
.004
-.097
-17.711.
000
Y predicted=B1*X1+B2*X2+…..Bn*Xn For example, for every unit increased in poverty ratio, we expect a -0.5 unit decrease in the park review score, holding all other variables constant. Similarly, for every unit increase in the variety of zoning types, we can expect a 1.194 unit increase in park score. For t and Sig. columns, they are the t-stat and their associated p-values used in testing whether a given coefficient is significantly different from zero. Usually a p-value smaller than 0.05 indicates the factor is significantly different from 0. In this result table, only ‘Building Height’ factor is over 0.05. So this factor is proved to be not statistically significant so that should be removed. In conclusion, in terms of the data collected, variety of zoning types, distance to bike routes, distance to CTA Stations, all of which have a positive effect to the quality of parks. While population density, poverty ratio, building ages have a negative coefficient to the Y variable. Chart below is a correlation analysis between poverty ratio and park score.
Park Score and Poverty ratio PARKSCORE 30 25 20 15
PARKSCORE
10 5 0
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
27
DISCUSSION Though the regression result show that the model is of a good fitness. However, in the research process, there are still a lot of issues that are unable to be well solved and lead to some deficiencies of the model. Firstly, lacking of enough sampling data. Since the park review score are based on Google, some famous and big parks usually get a large number of reviewers contributing to the assessment. Theoretically, the larger number of reviewers, the better results can be obtained. However, there are still a lot of small neighborhood parks lacking of effective scores from enough reviewers. Therefore, to improve this model, getting more samples should be one of the most important aspects. Secondly, due to the different size of parks, bigger parks usually get more samples in data collecting, thus have more influence to the final regression result. In City of Chicago, this problem would not cause too much imprecision because selected parks are all from the city area, which means usually the bigger park it is, the more influence it will have on the city. However, assuming another city possess a very big riverfront park in rural area, and it works as only a protection or a buffer to the river, rather than contributing to peopleâ&#x20AC;&#x2122;s urban life, we have to customize the model then. Thirdly, there are still some perceptual terms cannot be well translated. For example, enclosure is actually an important concept in urban design realm. However, purely using building height is not enough to depict this factor. As mentioned above, viewshed should be more appropriate to describe this term, but this function is better applied in a single public space assessment, there are limitations for the citywide scale.
Chicago Millennium Park
28
PRACTICE AND APPLICATION The grading system can be applied in urban design and planning realm, as an important tool that used for measuring the quality of a public space. The first application is to evaluate existing open space and public space, learn lessons from the correlations between indicators and success, and explore potential development in the future. Below is an example of how the grading system applied in Millennium Park. In this case, viewshed function is applied. Secondly, it could be used for preliminary site analysis, discovering site strengths, deficiencies, opportunities and challenge for design. Since design projects usually have a short term, this tool will help designers learn the site in a quick but accurate way. Thirdly, in design process, the method could be used for scheme comparison and decision-making. In competitions and project bidding, the method could also be used as a measurement that probably is more convincing.
SULPHUR DE
A PROTOTYPE FOR RESILIENT U
d Av
2n 5
e
3rd
C1
e
Av
Av
on st Jeffers
e
e
d Av
greenway
Nashville Public Marke Square
4
Market Commons
5
wellness retreat (hote
6
heritage restaurants (repurposed historic b
7
upscale rental apartm
8
workforce rental apar
e
H1
18 11
Av
O4
eN
O1
B
P
Ro
C3
6
sa
P1
P
Av
eN
4th
H2
7th
eN
Av 3rd
O6
3
Av
2n
M
Sulphur Dell Spring
3
4
e
n st
6th
C5
so Jack
1 2
B 6
Av
P2 O5
5th
The Maket Square
C2
Pa
eN
Av
R4
Blvd
R1
P
12
rks
5th
R9
P
4th
C4
R5
n St
20
so
Jack
14
13
2
6th
townhouses
market rental apartme
11
community wellness c
12
Meharry Medical Colle clinic
13
pharmacy
14
student housing + aca services
15
fitness center
16
clinical education cam
17
medical R&D center
18
State Library + archive
19
State Museum
eN
Av
9 10
R3
7th
15
Av eN
O2
P
19 P
R2
1
21
7
8
aL Ros
R8
B
P
Pa
16
rks
O3
9
d Blv
10 2
R6
S
rris Ha
on
20
st
21
R7
P 17
Sounds Ballpark
Bicentennial Mall Stat Park
B
proposed b-station hu
P
‘Texas doughnut’ par (below elevated cour
perspective views (on presentation boa
0
125’
250’
500’ 10
100 50
200 m
Multiple design scheme comparison--ULI Hines competition
REFERENCE 1. KarlG. Spatial Concepts in Urban design and GIS. TeachSpatial-resource for spatial teaching and learning. 2010. http://www.teachspatial.org/node/634 2. David Early. Using Geodesign to Create Livable Health Communities. GeoDesign Summit 2014 Vedio. 2014. http://video.arcgis.com/watch/3472/using-geodesign-to-create-livable-healthy-communities-_dash_-part-2-of-2 3. Marnie Purciel, et al. Creating and validating GIS measures of urban design for health research. Journal of Environmental Psychology. 2009. Pp457-466. 4. Introduction to SAS. UCLA: Statistical Consulting Group. from http://www.ats.ucla.edu/stat/sas/notes2/ . 5. http://uli.org/awards/2014-hines-competition-harvard-university/
29
SCHOOL WORKS
MEASURING THE ACCESSIBILITY OF PUBLICTRANSIT SERVICE IN CINCINNATI To better assess Greater Cincinnatiâ&#x20AC;&#x2122;s public transportation service, a variety of criteria are applied including demographic feature, stops on and off, serving radius, transit routes analysis. The research aims at helping public-transit dependent people find a best zone to live in.
DATA DECOMPOSITION AND RECOMPOSITION
30
Single and Pairs
Bus stop service radius
Bus routes and stops
Most popular stops
Neighborhoods bus stops
Overall assessment
31
SCHOOL WORKS
LOCATION SELECTION FOR WALMART IN PALMAR, COSTA RICA
1. Hydrology - Get rid of rivers, streams, and wetlands that are unsuitable for Wal-mart site.
2. Development - Select undeveloped lands, and exclude roads, existing buildings.
3. Vegetation - Choose only open land and abandoned plantation for environmental sustainability.
By multiple times reclassify and calculation directly from single layers, data are recomposed and turned into usable information. The final map is the product of Restriction map* Advantage map. In the final map, the higher score the land is, the more suitable for the site.
32
4. Road Accessibility - Wal-mart prefer places closer to roads for convenient transit.
5. Buildings - More buildings means more clients and opportunities for Walmart.
6. Elevation - For transportation and construction consideration, flatter sites are better.
*
A. Restriction Map - In order to exclude all restricted lands for construction, multiply calculation is generated as ReclHydro*ReclDev*ReclVeg.
B. Advantage Map - Based on a series of map that a Wal-mart would prefer, advantage map gave each pixel a score by accumulation.
Map A * Map B= Final Map
33
SCHOOL WORKS
UPENN FOOD TRUCKS ON SNOW DAY By given the assumption that people at Penn generally walk at a speed of 5 feet per second on Hard Surface conditions but only half as fast over Green Space and 1/10 as fast when walking in the Street, while Buildings and Water are impassible. Because of a recent snowstorm, all speeds were changed to 13 1/3 feet per second. Given the 3 lunch trucks and 40 typical customers, we will determine how this new condition will affect trucks, customers, and landscape in between them. Q1: How many customers will be gained or lost in a snowstorm for each truck?
Before
After
1. Create two friction grids for the campus condition of before after snow. Reclassify the original layer, set the categories as seconds per foot taken to the food truck. 2. Use focal statistics (maximum) to enlarge the pixels which represent the customers and food trucks. Then region group the food truck only layer.
3. Apply cost allocation for both layers of before and after snow to see which customers are in each of the truck's zone.
4. Use zonal statistics (sum) to count the customers in each zone.Generate how many customers each truck gain or lost due to the snow.
Value
34
-2 -1 3
5. Results: The southwest truck gained two customers. The north truck gained one customers. The southeast truck lost three customers.
Q2: How much travel time will be saved or lost due to the snow? Before
After
1. Began with the before and after snow layer, perform cost distance to the truck only layer as the source data, and the friction layer as input cost data. Then generate how long time customers will cost in both before and after snow maps. 2. The length of time that customers saved or lost will be calculated by substract the before snow layer from the after snow layer. Negative values means times saved, and vice versa.
Q3: How many more or less customers will be traversing on the pixel on their way to lunch? Before
After
1. Started with the time cost layer, generated two before and after snow layers via flow direction.
2. Then apply flow accumulation to visualize the customer paths to the food trucks. Finally, substract the before-paths from afterpaths by raster calculator. Negative values means less customers will choose the paths, and vice versa.
35
DRAWINGS
36
37