Spatial Data Visualization Portfolio by deigalou

/Portfolio/

JIALU TAN Selected Works [Spatial Data Visualization/ Map] 2015-2019

MIT Urban Planing / Computer Science

I. Research Dossier - California Dreaminâ&#x20AC;&#x2122; 4.163J /11.332J Joint Urban Design Studio Collaborator: Mengfu Kuo /September - October 2018, Cambridge/

California Dreaminâ&#x20AC;&#x2122; Type: resesarch analysis, collaboration work Site: Palm Springs, CA Tutor: Prof.Rafi Segal, Alan Beger, Jonah Susskind, MIT DUSP Collaborator: Mengfu Kuo Duration: 2018.9-2018.10

[ Background] This research dossier was prepared for the urban design studio about future residential form when autonomous vehicle is widely applied. Our group studied the basic status using spatial data analysis from 3 aspects: population, natural disasters, facilities of information and communication in Palm Springs. These research results laid good foundation for the later design phase and helped understanding the unique geographical and social status of Palm Springs.

1.1 ONE MILLION INCOMING POPULATION IN 2050 30,000 pp/yr 30,000 pp/yr X 30 YEAAR = 1,000,000 0 0.5 1

Miles 3

0 0.5 1

Miles 3

2013 Density (pp / sq mi)

2014 Density (pp / sq mi)

DENS_2013

DENS_2014

0 - 1000

1001 - 3000

3001 - 6000

6001 - 10000

10001 - 17031

10001 - 16957

2,228,528 pp 17031 pp/sq mi

2,266,899 pp 16957 pp/sq mi

2013 Population Density

2014 Population Density

0 0.5 1

Miles 3

0 0.5 1

Miles 3

2015 Density (pp / sq mi)

2016 Density (pp / sq mi)

DENS_2015

DENS_2016

0 - 1000

1001 - 3000

3001 - 6000

6001 - 10000

10001 - 17194

10001 - 17082

2,298,032 pp 17194 pp/sq mi

2,323,892 pp 17082 pp/sq mi

2015 Population Density

2016 Population Density

1.2 NATURAL DISASTERS EARTHQUAKE + LIQUEFACTION Above the San Andreas Fault, the Coachella Valley therefore be able to utilize the geothermal water and energy. However, the fault is one of California’s most dangerous. For Southern California, the last big earthquake to strike was in 1857, when a magnitude 7.9 earthquake broke out 185 miles between Monterey County and the San Gabriel Mountains near Los Angeles.

0 0.5 1

Miles 3

There are many concerns from experts of the activity prediction of San Andreas Fault. The biggist one is that the Fault is too quiet for a Century. Scientists have observed that based on the movement of tectonic plates,

“earthquakes should be relieving about 16 feet of accumulated plate movement every 100 years. Yet the San Andreas has not relieved stress that has been building up for more than a century.” Faults

The landslide will also be triggered by the Earthquake. After the 1986 North Palm Springs earthquake, numerous landslides Consisting primarily of debris slides and rock falls were reported. Moreover, the liquefaction in this area can magnify the impact of the earthquake waves, cause the Valley suffered more from the damage.

Fault_zones Liquefaction

The recent sensible earthquake events are as follows: In June 1992 Landers-Big Bear quakes struck the Coachella Valley. The 1994 Northridge earthquake was felt in Palm Springs. The earthquake centered in Borrego on July 7, 2010 the Valley felt the shock waves but the sustained no damage.

The San Andreas Fault zones layered with the liquefaction in Coachella Valley Area. The overlapped zones shows the severe earthquake event may strike very closely to the development area in the Valley.

1.2 NATURAL DISASTERS FLOOD + TOPOGRAPHY The flood occurs in Coachella Valley is mainly causing by the sudden storm happened in the mountain area. Due to the desert dry soil condition and barren surface of the hills, water would congregate and flush quickly into the Valley. The desert Coachella Valley soil also cannot absorb water efficiently.

0 0.5 1

Miles 3

100 Years Flood Zone 1% Possibility per year

The history of flood in this area sometimes caused the City isolated from the outside world. There are some past events can demonstrate how the flood can impact the Coachella Valley:

500 Years Flood Zone 0.2% Possibility per year Rivers PS_boundary WCV_boundary

The major flooding occurred in Palm Springs in 1938, when the Whitewater River flooded the Coachella Valley. Overflow from the Tachevah Creek caused major flooding in Downtown Palm Springs and people in the City were isolated for nearly a week. Similarly, in the winter of 1965, the Cottonwood Creek overflowed Inter-state 10 east of Highway 111, blocking traffic and isolating the City of Palm Springs. The possible Flood zone can be found overlapped with parts of the development area. Especially the WestSouth that at the foot of the mountain.

The mapping shows the relationship between Flood zones, river systems, and topography. Therefore, a flood eventâ&#x20AC;&#x2122;s cause and result can be easy to recognize.

1.2 NATURAL DISASTERS WILDFIRE + SEVERE WIND Hot, windy, and dry conditions caused by California’s severe droughts have created an environmental condition that people can easily set ablaze. Within the first eight months of 2015 alone, Cal Fire(The California Department of Forestry and Fire Protection) had already responded to nearly 5,000 wildfires over 150,000 acres.

“Humans Cause Over 95 Percent of California Wildfires” - National Geography. 2017, one of the largest wildfires tearing through southern California was caused by a downed power line. Illegal campfires can start blazes too. There was one that began in 2009 and grew to destroy more than 2,700 acres. The natural occurrences such as lightning strikes can also spark fires. Yet, it is only approximately 5 percent of wildfires in California that were caused by natural phenomenon. Through the mapping of the average wind rate and highrisk wildfire zone, we can see the fire is more likely to be ignited in the mountain area. Yet, some of them are overlapped with roads, parks, and houses. The triple match of human, wind, and dry condition may create the wildfire high-risk zone in these areas.

0 0.5 1

Miles 3

Annual Average Wind Speed at 80m Wind Speed 10m/s Wind Speed 7m/s Wind Speed 5m/s WildFire Susceptibility Very High High PS_boundary WCV_boundary

Mapping of the average wind speed and wildfire risk zones. When the dry condition, strong wind, and human action that ignites the fire all come together, the large damage even t may occur.

1.3 INFORMATION & COMMUNICATION CURRENT APPLICATION - Cellular Network

“There’s no doubt that mobile phones have become crucial to our daily lives and that’s especially true in a disaster.”

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T-Mobile cellular tower

When a disaster occur, the victims don’t just dial 911 call centers for seeking help. They also use the social media sites like Twitter and Facebook to ask for assistance, update their location and condition with family and friends.

Verizon cellular tower Sprint cellular tower Signal Coverage + Human Activity Area PS_boundary

Therefore, the cellular tower would be the essential infrastructure to cope with emergency events.

WCV_boundary

The signal coverage recorded through the human activities in Coachella Valley area also shows where people can stay connect with the world of internet.

Mapping of the signal tower and coverage.

1.3 INFORMATION & COMMUNICATION CURRENT APPLICATION For now, Coachella Valley is using an eariy earthquake warning system called Quake Guard system. The whole system is built on the local fire stations, using local infrastructure as the network to send alarms to local schools and hospitals. 0 0.5 1

The system detects P wave and S wave, and use the time difference for arriving to get a few seconds notice before the real destructive wave comes. The warning time is about 1 second for every 5 miles of distance from the earthquake’s hypocenter.

Miles 3

Fire Station Seismic Sensor Fire Station + Seismic Sensor Fault zones PS_boundary CV_boundary

“Of the 12 sets of sensors currently set in place throughout the Coachella Valley, all tied to fire stations, they will soon be networked to every school in the area. Down the road, local officials see it linked to hospitals, private businesses and even people’s homes.”

! A ! A

1.3 INFORMATION & COMMUNICATION POTENTIAL INFRASTRUCTURE FOR SENSORS

DESERT HOT SPRINGS

! A

! A RANCHO MIRAGE

PALM DESERT

INDIO

INDIAN WELLS

COACHELLA

Sensors and Internet rely on local electricity and signal towers. So the map on the right shows the existing electricity transmission line, power plants and mobile phone signal towers. Together they could form a potential network of the future sensor systmen.

! A

COACHELLA

! A

!! !!

!! !! !!

! !

! !!

!! !! !!

!! !!

! A

! A !!

! A

!! !!

! !

!! !!

!! !

! !

! A

!! !!

!! !! !!

! A

Wind

Solar Thermal

Solar

Landfill Gas

Hydro

Gas

Biomass

Battery

INDIO

LA QUINTA

Cellular_Aggregate_Callsign

Transmission_Line

“What infrastructure would future sensor and Internet be built on?”

cellularTower

E E E E E E E E

General_Fu

CATHEDRAL CITY

Miles 3

riversidePowerPlants

E ! A

PALM SPRINGS

! A !!

EE EE

0 0.5 1

BANNING

EE E E EE ! E E E EA E E E E E E E E EEE EE E EEE E E

! A

!! !!

1.3 INFORMATION & COMMUNICATION POTENTIAL ELEMENTS IN FUTURE IoT SYSTEM

R 0 0.5 1

Miles 3

School_Centroids

R R

School_Property

golfcourse golfcourse Hospital Medical Center Federally Qualified Health Center

R A much more connected communication system and a broad IoT system would bring everything together to share the information and real-time data. In terms of early warnig system of natural disasters, the few secons could help avoid huge losses. If hospitals and schools could be added to the whole warning system, then hospitals could start the back-up electric generator in advance to keep the surgery going, children coul take cover and calm down. More importantly, fire stations wouldn’t have jagged garage doors which stops fire engines from geting out and save people’s lives. Water and gas valves could be shut off in case of explosion when there is an earthquake. Even though the few seconds doesn’t seem a long period, it is crucial for the whole society. in the future, personal business could be added to the IoT system and people’s homes could also be included, which together build up a real connected society.

R R R

Home Health Service

R R R RR R

Nursing Home

R R R R RR R R R R R R R RR R R R RR RR RR R R R R R R RR R R R R RR R R R RR R RRR R RRRRRRRRR RRR RRR R R R R R R R R R R RR R RR RR RR R R R R RR RR R RR R RR R R R R R R R R RR R R R R R R R RR RR RR R RR R R R R R R R R RR RR R R RR R

U.S. Department of Health & Human Services (USHHS)

Homelessness in San Francisco [Research Questions] Homelessness has been one of the striking problems in San Francisco. PART 1: PEOPLE | Where are homeless people? PART 2: RESOURCE | Where are the gaps between free resources and homeless population? PART 3: OPINION | What areas are more friendly to homeless people?

PEOPLE

II. Homelessness in San Francisco Independent Study /June-August 2018, SF/

GAP

RESOURCE

DONATIONS /VOLUNTEER

OPINION

where homeless people are

current resource distribution

friendly areas

what areas should be allocated with more resources

Gap Analysis: what areas should be improved

where donations or volunteer help could be got

Physical Built

Traditional Spa-

Twitter API

Peopleâ&#x20AC;&#x2122;s Opinion/

Envirionment

tial Analysis

Real Time Data

Feedback

AUGMENTED DECISION-MAKING PROCESS IN URBAN PLANNING

/ Part 1: Where are homeless people?

Encampment 311 data: ‘Encampment’ - people/items cases of people reporting findings of encampment items 40,000 cases in this category Method: - clean 311 data - GIS ‘display XY data’ to map the coordinates

encampment

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$ Miles 1

Encampment Heat Map GIS - ‘Spatial Analysis’, ‘Point Density’ Tenderloin, south of SOMA, Civic Center, Nob Hill are the neighborhoods with the most encampment.

E encampment Homeless Encampment

Density 70.18 - 97.14 97.15 - 160.08 160.09 - 307.05 307.06 - 650.26 650.27 - 1,451.66 1,451.67 - 3,323.01 3,323.02 - 7,692.78 7,692.79 - 17,896.58

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Miles 1

/ Part 2: Where are the gaps of current free resources and the homeless population? Used data from freeSF website and mapped the free resources in 5 categories. Then applied spatial analysis (buffer) to assign scores based on the distance to resources.

HOMELESS-RELIABLE RESOURCES GIS - â&#x20AC;&#x2DC;display XY dataâ&#x20AC;&#x2122;, latitude, longitude + food + hygiene + medical +housing + technology

food_homeless hygiene_homeless medical_homeless housing_homeless technology_homeless

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Miles 1

LAY BUFFERS GIS - ‘Euclidean distance’, maximum distance 3 miles (roughly 1-hour walking)

food_homeless <VALUE> 0 - 1,584 1,585 - 3,168 3,169 - 4,752 4,753 - 6,336 6,337 - 7,920 7,921 - 9,504 9,505 - 11,088 11,089 - 12,672 12,673 - 14,256 14,257 - 15,840

0.5

$ Miles 1

ASSIGN SCORES GIS - ‘Reclassify’, reverse values, give higher values to nearer areas

food_homeless Reclass_food Value 1 2 3 4 5 6 7 8 9 10 11 12

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$ Miles 1

Overlay 5LAYERS Categories OVERLAY

points

buffers (3 miles)

scores ( reclassify 0-12)

food

0.35

hygiene

0.35

medical

0.1

housing

0.1

technology

0.1

AREAS WITH 10+ SCORE (RANGE 0-12) GIS - â&#x20AC;&#x2DC;Raster Calculatorâ&#x20AC;&#x2122;, Top 1/6 areas with good connection to the resources food_homeless hygiene_homeless medical_homeless housing_homeless technology_homeless Distance Score (10+) Value High : 12 Low : 0 Distance Score (8+) Value High : 12 Low : 0 Distance Score Value High : 12 Low : 1

$ 0

0.5

Miles 1

OVERLAY THE HOMELESS ENCAMPMENT DENSITY MAP WITH THE RESOURCE SCORE MAP If we use the 8+ score buffer, we can see that outer mission, outer Richmond and Cow Hollow districts are not covered by the resources. food_homeless hygiene_homeless medical_homeless housing_homeless technology_homeless Distance Score (8+) Value High : 12 Low : 0 E encampment Homeless Encampment Density 70.18 - 97.14 97.15 - 160.08 160.09 - 307.05 307.06 - 650.26 650.27 - 1,451.66 1,451.67 - 3,323.01

3,323.02 - 7,692.78 7,692.79 - 17,896.58

0.5

Miles 1

OVERLAY THE HOMELESS ENCAMPMENT DENSITY MAP WITH THE RESOURCE SCORE MAP If we use the 10+ score buffer, we can see that Mission, and South Beach districts are not covered by the resources. food_homeless hygiene_homeless medical_homeless housing_homeless technology_homeless Distance Score (10+) Value High : 12 Low : 0 E

encampment

Homeless Encampment Density 70.18 - 97.14 97.15 - 160.08 160.09 - 307.05 307.06 - 650.26 650.27 - 1,451.66 1,451.67 - 3,323.01 3,323.02 - 7,692.78 7,692.79 - 17,896.58

0.5

Miles 1

PHASE 1: URGENT

PHASE 2: FUTURE IMPROVEMENT

COW HOLLOW EMBARCADERO

PHASE 1 INNER RICHMOND SOUTH BEACH

PRIORITIZED AREAS FOR FUTURE DEVELOPMENT

OUTER RICHMOND

Areas circled in red should be prioritzed in the future for more resources. Areas circled in white should also be assigned more resources if possible. NGOs or SF government should prioritze these areas for future improvement. OUTER SUNSET

PHASE 2 MISSION DISTRICT

BALBOA PARK

OUTER MISSION CROCKER-AMAZON

/ Part 3: Which neighborhoods are more friendly to homeless people? Here used Twitter API to scrape people’s tweets about homeless people and used sentimental analysis to calculate percentage of friendly people in each neighborhood.

USING MULTIPLE OVERLAPPING CIRCLES TO COVER SAN FRANCISCO Each square is 1 mile by 1 mile. Get the coordinates of each circle’s center.

1666

PUBLIC AWARENESS OF HOMELESSNESS The amount of tweets reflects different levels of public awareness to the homeless issue. However, the total amount of tweets collected through these smaller circles is far less than the amount collected by using the whole SF range.

PUBLIC AWARENESS COMPARED WITH HOMELESS DENSITY There is more public awareness where people are more exposed to the problem.

PUBLIC OPINION TOWARDS HOMELESSNESS Run each tweet through the sentiment analysis and get the percentage of ‘positive’, ‘neutral’, and ‘negative’ tweets in total tweets.

‘FRIENDLY AREAS’ In the areas where public are more aware of the issue, SOMA and South Beach are the relatively friendly areas with positive attitude. Tenderloin and Lower Heights have about 10% percent less positive people. It’s also worth to mention that SOMA has the highest percentage of negative tweets.

CONCLUSION OF THE HOMELESS RESEARCH

PART 1 | WHERE DO THE HOMELESS ENCAMP? Tenderloin, south of SOMA, Civic Center, Nob Hill are the neighborhoods with the most encampment.

PART 2 | HOW GOOD IS EACH AREA IN TERMS OF EASY ACCESS TO RELIABLE LIVING RESOURCE FOR THE HOMELESS? Outer Richmond, Outer Sunset, Cow Hollow, Balboa Park, and Outer Mission need urgent input on resources. South Beach and Mission District should be the second tiers of neighborhoods to allocate more resources.

PART 3 | WHERE SHOULD FUTURE MARKETING BE DIRECTED TO? South Beach is the most friendly area in SF to homeless people and the public are very well aware of the issue. Where the homeless density is high, generally there is better public awareness towards a certain issue. Areas which get enough exposure to the problem but not right in the center of the problem seems to have the most friendly people and is mostly likely to be the place for raising donations and getting help.

Does Urban Design Even Matter?T Type: resesarch design, collaboration work Site: Boston Tutor: Prof. Sarah E.Williams, MIT DUSP Collaborator: Joshua Brooks, Gonzalo Ortega Duration: 2017.10-2017.12 [ Main Concept] What is the relationship between various spatial patterns in the urban fabric of Boston and the prevalence of pedestrian and bicycle accidents? Can Bostonâ&#x20AC;&#x2122;s diverse spatial make up give insight in to what types of urban form most often house active transportation accidents? Can this information be used to evaluate new development and design proposals? This research project is aimed at identifying what spatial patterns in the physical environment have higher rates of active transportation accidents. Using ten spatial patterns that vary across the landscape of Boston this project will work to identify and prioritize what types of improvements should be made and where those improvements should happen.

III. Does Urban Design Even Matter? 11.520 Geographic Information Workshop /October - December 2017, Cambridge/

[Significance] In an effort to make urban areas more livable and attractive to a diverse set of users many communities are focused on increasing pedestrian and bicycle safety. How should we go about doing this and how can we ensure that the continued growth of urban areas do not repeat mistakes of the past. The objective of this project is to identify individual patterns and groups of patterns that have the highest or lowest probability of active transportation accidents. Because Bostonâ&#x20AC;&#x2122;s built environment has such diverse representation of individual patterns it offers a very unique place to study this issue. While this study will most clearly benefit the City of Boston by helping categorize the types of areas that are most prone to pedestrian and bicycle accidents it can also serve as a tool for other municipalities to understand their own urban environments. Additionally, this tool has the potential to serve as an evaluation method for new communities, districts and development areas. This research could lead to significantly lower rates of active transportation accidents, thus making cities and communities safer and more livable. [Conclusion] Narrower streets that have a smaller foot print for vehicular traffic are safer for pedestrians than wider street. The wider sidewalks and smaller block sizes significantly decrease the rates of accidents per person. Higher concentrations of street trees led to fewer pedestrian and bike accidents. Wider ROW widths do not make safer streets. Presence of street lights increase pedestrian safety.

The Meaning of The Study WORK-FLOW

WORK FLOW | Processing Data in ArcGIS

Processing Spatial Data in Arc Map

By determining which neighborhoods have higher rates of accidents per person we can equitablly prioritize capitol improvement dollars across the city and determine the types of interventions that will yield positive results.

WORK FLOW: obtain datasets

Parcels / Buildings / Open Space Base Maps READING

BEDFORD

Street Center Lines Intersections Sidewalk Width Street Width Row Width Ratios Between Elements READING

PEABODY

BEDFORD

WAKEFIELD

BURLINGTON

SALEM

WOBURN

WINCHESTER

MALDEN

WINCHESTER

WINCHESTER LEXINGTON

MALDEN NAHANT

MEDFORD

ARLINGTON

REVERE

CHELSEA SOMERVILLE

BOSTON WATERTOWN

WINTHROP

BOSTON

WINTHROP

CAMBRIDGE BOSTON

WINTHROP

WATERTOWN

NEWTON

BROOKLINE WELLESLEY

BOSTON

NEEDHAM

WATERTOWN

QUINCY DOVER

DEDHAM

G GG GG GG G GG G GG G GGG GG G GGGG GGGGG G GGG G G G G G G G GG G GGG G GGG G G G GG GGGGG GGG GG G G GG G G G G G GG G G GG GG G G G GG GG G G G GG G GG G GGG G G GG G G GG G G G GGGGGG GG G G G GG G G G G G GG GGGG G GG GGG G GGGGGG G GGG GG G G G G GG G G G GG G G G GGG G G G GGG G G GG G G G G G G G GG G GGG GG G GG G G G GG G G G GG G GG G GG G G GGGGGGGG G G GGGG G GGG G GGGG GGGG GGG G G G G G G GG GG G GG G G G G G G GG GGG G GGG G G G G GG GG GGGGG GGG GG G GG G G G GGGGG G G GGG G G G G G G G GG G GG G GG GG GG G G G G G G G G G GG GG GGG GG GG G G G G G G GG G G GGG G G G GG G G G BROOKLINE G G GG GGG GG G GG G G G G GG G G G G G G G G G G G G G GG G G G G GG G G G G G G G GGGG G G GGG G G GG GG G G GG GG G GG G GGGGG G G G GG G G GG G G G GG G GG GG G GGG G G G G G GG G G GG G BOSTON G G G GGG GG G GG GG HULL G GGG G GG G G GG GG G G GG GG G GG G GG G G G G G G G G G GG G G G G G G G G G G G G G G G G G GG G G HULL G G G G G G G G G G GG G G G G GG G G G GG G GG G G GG G GGG G G G G G GGG G G G G GG G G G G G G G G G G G G G GGG G G G GG G GG G G G G G G G G G G G GG G G G G GG G G G GGG G G G G G G GG G G G G G GGG G

NEWTON

BROOKLINE

WELLESLEY

HULL

BOSTON

HULL NEEDHAM

NEEDHAM

QUINCY DOVER

DEDHAM

MILTON

HINGHAM

QUINCY DOVER

DEDHAM

MILTON

HINGHAM

Bike Counts Interpolated Bike Density

WESTWOOD WEYMOUTH BRAINTREE

WORK-FLOW [ CANTON

NORWOOD

WALPOLE

Bike Accidents Rate of Accidents

WESTWOOD

QUINCY HINGHAM GG

Miles 3.2

Pedestrian Density Interpolated Pedestrian Density

WESTWOOD

WEYMOUTH BRAINTREE

[

CANTON

NORWOOD 0.4 0.8 1.6

Miles 3.2

MILTON

HINGHAM

Pedestrian Accidents Rate of Accidents

WEYMOUTH

BRAINTREE

CANTON

NORWOOD 0.4 0.8 1.6

HULL

DEDHAM

MILTON

WESTWOOD

GG G G G GG GG G WINTHROP G G G G G G BOSTON G WINTHROP G G GG G G G

G GG

G G DOVER

G G G G

WINTHROP

NEWTON

G G GG

CAMBRIDGE

G G

WELLESLEY

G G GG

GG G G WINTHROP

G GG

SOMERVILLE

WALTHAM

G GG G

WATERTOWN

CHELSEA

BELMONT

SOMERVILLE

WALTHAM

GG G G GG CAMBRIDGE

Datasets Sources: City of Boston GIS, Vision Zero (Boston EMS), Anthony Vanke

EVERETT

CHELSEA

BELMONT

SOMERVILLE

WINTHROP

REVERE

EVERETT

CHELSEA

BELMONT WALTHAM

NAHANT

MEDFORD

ARLINGTON

REVERE

EVERETT

CAMBRIDGE

MALDEN NAHANT

MEDFORD

ARLINGTON

REVERE

EVERETT

BELMONT

SAUGUS

MELROSE

LEXINGTON

MEDFORD

[

CANTON

NORWOOD 0.4 0.8 1.6

Miles 3.2

WEYMOUTH BRAINTREE

[

Processing Spatial Data in Arc Map RANDOLPH

2.4

WALPOLE

WORK FLOW: Logistic Map

SWAMPSCOTT

LYNN

STONEHAM

SAUGUS

WINCHESTER

Trees Density of Street Trees

SALEM

SWAMPSCOTT WOBURN

LYNN

STONEHAM

Street Lights Street Light Coverage

PEABODY

WAKEFIELD

MELROSE

LEXINGTON

ARLINGTON

READING

SALEM

Regulated intersections Y/N

BURLINGTON

SWAMPSCOTT WOBURN

MELROSE

LEXINGTON

Traffic Lights

PEABODY

BEDFORD

WAKEFIELD

SAUGUS

MELROSE

WALTHAM

READING

LYNN

STONEHAM

SAUGUS

Existing Bike Network Bike Network Categories SALEM

BURLINGTON

SWAMPSCOTT WOBURN

LYNN

STONEHAM

PEABODY

BEDFORD

WAKEFIELD

BURLINGTON

RANDOLPH

WALPOLE

2.4

RANDOLPH

WALPOLE

2.4

RANDOLPH

0.4 0.8

1.6

2.4

Miles 3.2

Legend street light Count1_1 0

1 2 3 4

1:2000

5 6-7 8 - 41

Streetlight Density in Boston.

/October - December 2017/ GIS Spatial Analysis

Spatial Patterns & Accident Rates

Street Tree Coverage

Average Street Width

RATE OF PEDESTRIAN ACCIDENTS Compare to Number of Street Trees RATE OF PEDESTRIAN ACCIDENTS Compared to Average Street Width

RATE OF BIKE ACCIDENTS Compared to Number of Street Trees

0.050%

0.800%

0.050%

0.700% 0.040%

0.600%

0.030% 0.020%

0.030%

0.020%

23.0

28.0

33.0

38.0

43.0

0.000%

48.0

RATE OF BIKE ACCIDENTS Compared to Avergae Street Width

0.0

RATE OF PEDESTRIAN ACCIDENTS Compare to Number of Street Trees

2.0

4.0

6.0

14.0

16.0

18.0

0.000%

20.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

Number of Street Trees Per Cell

0.050% 18.0

28.0

38.0

48.0

58.0

0.600%

Rate of Accidents

0.100%

0.030%

0.020%

68.0

0.500% 0.400% 0.300% 0.200%

0.010%

Average Street Width

0.100% 0.000%

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

0.000%

20.0

0.0

2.0

4.0

6.0

Number of Street Trees Per Cell

2 Miles

RATE OF PEDESTRIAN ACCIDENTS compared to Density of Intersections

0.020%

0.010%

5.0

10.0

15.0

20.0

25.0

RATE OF PEDESTRIAN ACCIDENTS Compared to Street Light Density

0.150% 0.100%

5.0

10.0

15.0

20.0

0.000%

25.0

0.100%

10.0

15.0

20.0

0.050% 0.000% 0.0

0.030%

0.020%

0.000%

25.0

Number of Intersections Per Cell

4.0

6.0

8.0

10.0

12.0

14.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Number of Street Lights Per Cell

0.200% 0.150% 0.100% 0.050% 0.000%

0.0

2.0

4.0

6.0

8.0

Number of Street LIghts Per Cell

2.0

0.250%

0.010%

0.050%

0.100%

RATE OF BIKE ACCIDENTS Compared to Street Light Density

Rate of Accidents

0.150%

0.300%

0.040%

0.200%

Number of Street LIghts Per Cell

0.050%

0.250%

5.0

0.020%

RATE OF PEDESTRIAN ACCIDENTS Compared to Street Light Density

RATE OF BIKE ACCIDENTS Compared to Density of Intersections

Total Street Tree Count

0.250%

0.030%

Number of Intersections Per Cell

0.300%

2 Miles

RATE OF BIKE ACCIDENTS Compared to Street Light Density

0.010%

0.0

0.300%

0.040%

Number of Intersections Per Cell

0.0

20.0

0.050%

0.0

18.0

Street Light Coverage

0.200%

0.000%

16.0

Rate of Accidents

0.030%

14.0

CHANGING PATTERNS

Rate of Accidents

0.250%

Rate of Accidents

0.300%

12.0

RATE OF BIKE ACCIDENTS Compared to Density of Intersections

0.040%

10.0

Average Street Width

Intersection Density (Block Size) 0.050%

8.0

Number of Street Trees Per Cell

CHANGING PATTERNS

0.000%

12.0

0.700%

0.150%

0.000%

10.0

0.800%

0.040%

0.200%

0.000%

8.0

RATE OF BIKE ACCIDENTS Compared to Number of Street Trees

0.050%

0.250%

Rate of Accidents

0.300%

Number of Street Trees Per Cell

0.300%

Rate of Accidents

0.400%

0.100% 18.0

Average Street Width Per Cell

25.0

0.500%

0.200%

0.010%

0.010% 0.000%

Rate of Accidents

0.040%

sections

CHANGING PATTERNS

2 Miles

Total Intersection Count

10.0

12.0

14.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

Number of Street Lights Per Cell

2 Miles

Total Street Light Count

20.0

Spatial Patterns VS Real Street View in Back Bay & Mattapan

Mattapan | Average Sidewalk WIdth

Mattapan | Average Street Light

Mattapan | Real Street View

Back Bay | Average Sidewalk WIdth

Back Bay | Average Street Light

Back Bay | Real Street View

IV. Other Works /2015 - 2019/

Criminal Networks in Montreal /November - December 2019/ Spanish Speakers in Bronx NY /September - October 2017/ Historical Sites in Xiâ&#x20AC;&#x2122;an Muslim Distric /March - September 2016/ Guming Village Public Space Design /November - December 2015/

Mapping Criminal Networks in Montreal. /October - December 2019/ Network Analysis, GIS Spatial Analysis

GIS Problemset: Spanish Speaker in Bronx. /September - October 2017/ GIS Spatial Analysis

1 Reduce Building Height to Bring Mosques in Sight Restore the traditional landscape which is mosque-centered community. Reduce building height allows people see the important spots in the region.

2 Change over-constructed courtyards to open space Due to a lack of open space , use these courtyards which are beyong restoring to original pattern as pocket gardens.

local residents visitors W residential

E commercial

3 Insert Homestay Hostel Hotel service industry in the region is relatively lack. Using idle living space as hostels increases income of the local families and also encourages mixing of different nationalities.

Historical Site in Xiâ&#x20AC;&#x2122;an Muslim District.

/March - September 2016/ Urban Design, Mapping

river

Rebuild the two poles.

Design routes linking the cultural spots, such as dyehouses, to attract tourists. Form a mixed community.

The Threshing Ground

fields & crops

cultural spots, silversmith's house

The Sacred Woods N

1:2000

Guming Village Public Space Design.

/November - December 2015/ Urban Design, Mappinp

/Portfolio/ JIALU TAN Selected Works [Spatial Data Visualization/ Map] 2015-2019 MIT Urban Planing / Computer Science Email: jialutan@mit.edu Phone: 617-390-6780