GIS Work Sample | Ting Zhang

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GIS WORK SAMPLE

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2013-2020 Selected

Ting Zhang

+1 (917) 855 2680

tz2436 @columbia. edu



_ TA B L E O F CO N T E N TS

CATA LOG...

SPATIAL RESEARCH 01 EXTRACTIVE URBANISM 02 INFLUENZA ACTIVITIES AND NEIGHBORHOOD CHARACTERISTICS 03 STARBUCKS EFFECT 04 05 06 07

GIS SUPPORT IN URBAN DESIGN SEEDING THE MACHAMBA DISPERSING WELLNESS MOBILITY SYSTEM STUDY WASTE FRONT

OTHER GIS PRACTICES 09 MULTIBAND LANDSAT PROGRESSING 10 DEM OPERATION


EXTRACTIVE URBANISM TOOLS: COURSE: LOCATION: INSTRUCTOR:

GIS, Python, Rhino, Grasshopper, Mapbox Conflict Urbanism Mozambique Laura Kurgan

Original Delivable: https://centerforspatialresearch.github.io/conflict_ urbanism_sp2020/2020/04/30/Wu-Annie-Wu-Zhou-Zhang-Ting-ZhengChris.html Mozambique’s booming extractive industries have spurred the country’s making of modernity in the post civil war era. Through the lens of urbanism - urban development, foreign investments, infrastructure construction, settlements and resettlements, etc. - this project looks at how the extractive boom is building the country’s economy while characterizing it with spatial and socio economic fragmentation across the national territory.

LIQUEFIED NATURAL GAS (LNG) IN CABO DELGADO, MOZAMBIQUE In 2010, large reserves of natural gas were discovered in the Rovuma Basin, the offshore area of Cabo Delgado Province, northern Mozambique, which attracted a lot of foreign investment and will make Mozambique the third largest country of Liquefied Natural Gas (LNG). There are also future plans for pipeline and natural gas plants, yet it remains unknown who will invest in this development. However, the existing pipeline plan in Maputo is going to transport huge natural gas to South Africa.



CABO DELGADO | LICENSED GAS AREAS The Gas Fields were divided into onshore and offshore 6 areas held by different foreign companies, and a lot of those are owned by foreign governments. In each area, Mozambique holds 1015 percent shares, but none of the areas is operated by Mozambique. In fact, most of LNG will be shipped to those countries instead of being locally used. To support the LNG production, an onshore facility will be constructed, which is projected to influence over 10,000 People.



AFUNGI LNG PLANT | RESETTLEMENT PLAN

554 952

households are expected to be physically resettled are expected to lose access to their economic resource

A Resettlement plan was made for the construction of the LNG plant, over 500 households are expected to be physically resettled and another 1000 are expected to lose access to their economic resource. The replacement village is located at the marginal area of the plant, but the replacement agricultural land will be 10-15 km away, and there is a delay in this process while the replacement village is being constructed now.


LOSS OF MAIN SOURCE OF LIVELIHOODS

51

%

of displaced households reporting at least one member primarily engaged in FISHING

Among the displaced households, 51% are engaging in fishing, and in coastal villages, the number can be more than 80%. This map shows the vessel fishing and intertidal collecting points as well as the home ports. The restricted marine area will have a huge impact on those fishing grounds.


2012 A provisional authorization of the Right to Use and Benefit from Land (DUAT) was awarded to Rovuma Basin LNG Land, Lda., for an area of 7,000 ha.

2017 The LNG mega project reached the final investment decision (FID)

2018 The final plan was appro government.


oved by the Mozambican

2002 Construction Began. Several Attacks happened which slowed down the construction process.

2020


EMERGING INSURGENCY

Apart from the loss of livelihoods, there are also rising security concerns about the emerging attacks since 2017. A series of attacks by Islamist extremists on the civilians have causing dozens people killed. In 2019, they started to target LNG projects. The big companies have been seeking more troops from the government for protection. The ongoing conflict between the insurgents and the military forces have been bringing more pressure to the people who already have a relatively low socio-economic background in the poorest region. People are afraid of going to their fields, and the displaced households with a far allocated field will face potential starvation.



INFLUENZA ACTIVITIES & NEIGHBORHOOD CHARACTERISTICS TOOLS: DATA: COURSE: LOCATION: INSTRUCTOR:

Python (Geopandas, Sklearn, Matplotlib), GIS NYC DOHMH, US Census Exploring Urban Data with Machine Learning New York City Boyeong Hong

Full Report: https://issuu.com/tingzg/docs/influenzafinalreport

Influenza-Like Illness (ILI) Overall Emergency Department Visit Rate Per 100 People by ZIP Code 2016-2019


Triggered by the COVID-19 outbreak, this project uses machine learning to learn the pattern of the pandemic activities in New York City and the relationship with the neighborhood characteristics. The regression model estimated the influenza rates at the census tract level in New York City according to the various demographic data. With the clustering models, the study also identified important neighborhood characteristics related to influenza activities, which can help the health agencies and communities better prepare and mitigate the hazard brought by contagious diseases and provided a tool to facilitate neighborhood development.

COVID-19 Overall Confirmed Case Rate Per 100 People By ZIP Code Till June 7th 2020


Projected Average Influenza-Like Illness (ILI) Overall Emergency Department Visit Rate Per Year Per 100 People By Census Tract 2016-2019 The random forest regression model gave its prediction of ILI rate to each census tract. We compared the ILI rate derived from NYC Syndromic Surveillance Data, which is on ZIP Code equivalent area level, and the predicted ILI rate on census tract level. The comparison proved (1) the prediction matched the original training data, and (2) the prediction, since having a finer granularity, can identify the ILI rate difference between census tracts within the same ZIP Code area.

Projected Average ILI ED Visit Rate Per Year Per 100 People


Predicted Influenza-Like Illness (ILI) Overall Emergency Department Visit Rate Per Year Per 100 People By Census Tract

Predicted Average ILI ED Visit Rate Per Year Per 100 People


Kernel Density Plots of Standardized Features By Each Cluster


New York City Census Tract Clusters - KMeans

CLUSTERING - K-MEANS The resulting cluster group 0 (in Red) highly collocated with census tracts experiencing high Influenza activities. The visualization shows that neighborhoods with following characteristics: high percentage of population enrolled in public health insurance, high percentage of households with children, low education level in population, low to medium household income (less than 75k) are among the most vulnerable areas of Influenza activities.


THE STARBUCKS EFFECT TOOLS: DATA: COURSE: LOCATION: INSTRUCTOR:

GIS (Network Analysis), Python Scripting From Starbucks Website GIS New York City Leah Meisterlin, Carsten C. Rodin

Full Report: https://issuu.com/tingzg/docs/starbuckseffect

STARBUCKS DISTRIBUTION AND WALKABILITY ANALYSIS Thiessen polygons are generated from Starbucks points such that each polygon defines an area of influence around its sample Starbucks, so that any location inside the polygon is closer to that Starbucks than any of the other. Compare it to the network analysis of the Starbucks in NYC, We see a concentration of Starbucks in midtown, downtown Manhattan and Dumbo Brooklyn, where there is one Starbucks within every 1min walkable distance, while in uptown and other waterfront areas, the boundaries extend to 2 - 5 min walkability.

Uptown Manhattan

Midtown Manhattan


Starbucks Thiessen Polygon Walking Distance <660 ft 1 min <1320 ft 2 min <1980 ft 5 min <2640 ft 10 min


S E E D IN G THE MACHAM BA TOOLS: DATA: STUDIO: LOCATION: INSTRUCTOR:

GIS (ArcMap, ArcScene), Rhino OpenStreetMap, HDX Urban Design III, Columbia GSAPP Beira, Mozambique Kate Orff, Geeta Mehta, Thaddeus Pawlowski, Lee Altman, Dilip Da Cunha, Julia Watson, Adriana Chavez Storymap: https://storymaps.arcgis.com/stories/ f27ab8fa6c294c9ebb67e261d191f5b7 The city of Beira has an extensive and integrated system of traditional agriculture that is under threat. Our project conceives of this system as more than just agriculture - it is a productive and preventative flood infrastructure. We envision that this agricultural system could coordinate communities, organize the city, and be the key to recovery and ongoing resilience. The goals of this project are followed: Consolidating and organizing cooperatives at a city scale; Protecting social and ecological capital; Empowering women in agriculture; Diversifying income and create job opportunities; Integrating adaptive, nature-based infrastructure.


LACK OF ELEVATIONAL PROGRAMMING

Beira 2020


DI SPE RSI N G WE L L N E SS TOOLS: DATA: STUDIO: LOCATION: INSTRUCTOR:

GIS US Census Urban Design II, Columbia GSAPP Hudson Valley, New York Kaja KĂźhl, Anna Dietzsch, Jerome Haferd, Liz McEnaney, Justin Moore, Shachi Pandey, Raafi Rivero, David Smiley, Dragana Zoric

Project Video: https://vimeo.com/380161906 Healthcare industry accounts for 10% of the total greenhouse gases in the US. In the Hudson Valley, geography drives people’s health seeking behavior, residents travel up to 1.5 hours for their basic health needs. At the same time, many hospitals have 50% vacant bed space, that can be repurposed. The project challenges the perspective of the current healthcare system from being a measure of cure to an extension of health and wellbeing of the community. We reimagine dispersion of wellness through an additive typology that empowers the role of social infrastructure to spread a wellness network in rural areas that substantially lower the environmental impacts, and create an equitable and sustainable model.



MOBILITY SYSTEM STUDY TOOLS: DATA: STUDIO: LOCATION: INSTRUCTOR:

GIS US Census LEHD Urban Design II, Columbia GSAPP Hudson Valley, New York Kaja KĂźhl, Anna Dietzsch, Jerome Haferd, Liz McEnaney, Justin Moore, Shachi Pandey, Raafi Rivero, David Smiley, Dragana Zoric

Transportation accounts for 46% of the total greenhouse gases in the New York State. In the Hudson Valley, the transit system is divided into three levels: inter-regional, intra-regional and local, and they are all ran by different private companies, which lead to many disconnections and inconvenience. The limited public transit system made people tend to drive themselves. On the other hand, the map on the right exhibits people’s movements in the Hudson Valley. New York City and Albany attracts a lot of commuters traveling a long way from the Hudson Valley through car. In most of the counties, there is less than 10% of residents using public transit.



WASTE FRONT TOOLS: DATA: STUDIO: LOCATION: INSTRUCTOR:

GIS NYC Open Data Urban Design I, Columbia GSAPP Sunset Park, New York City Tricia Martin, Nans Voron, Hayley Eber, Sagi Golan, Quilian Riano, Austin Sakong, Shin-pei Tsay

Project Video: https://vimeo.com/354209483 Sunset Park’s Waterfront is an Industrial area which has multiple underutilized NYC properties with a great connectivity, and it currently hosts SIMS, the facility which receives and sorts 100% NYC’s residential recyclable waste. But residential waste only represents 25% of NYC’s Waste Stream. The remaining 75% is Commercial Waste of which only 22% gets recycled. And this recycling scattered all over the city, costing a lot of money and polluting due to its distribution. This project proposes a Green Waste System that can manage and recycle NYC waste, by locating a series of processes in one specific area, therefore reducing transportation, money and time invested in recycling, creating an asset at Sunset Park that will give back to the community by providing jobs, education, public spaces and energy.


ALL NYC RESIDENTIAL RECYCLABLE WASTE GOES TO IN SUNSET PARK WATERFRONT ONLY 22% OF THE COMMERCIAL WASTE GET RECYCLED IN SMALL FACILITIES ALL OVER THE CITY


GIS PRACTICE - MULTIBAND RASTER PROGRESSING


GIS PRACTICE - DEM OPERATION


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