Vishal-Maradana

[kastnerp]

@vishal-337 I need your individual notebook here asap, please.

[vishal-337]

Week-3

• Researched the definition of mobility and what makes a place dynamic and adaptable, while also exploring the significance of fairness in a city's transportation system. Read parts of “Transport Justice” by Karel Martens and “Human Transit” by Jarrett Walker.

• Researched walkability, walk score, and walk score methodology. Read "What Does Walk Score® Really Measure?” by Simbaqueba, Jorge, and looked into https://github.com/perryrjohnson/Walk-Score, a GitHub repo on reverse engineering walk score algorithm.

• Spoke with Patrick Kastner about the future direction of the project.

  Week-4

• Researched the limitations on existing walk score and subjective walkability from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10125877/ and https://www.sciencedirect.com/science/article/abs/pii/S2210670722004553?fr=RR-2&ref=pdf_download&rr=850c94e8ef3a226e

• Looked at existing walkability metrics and indexes: PIE(https://www.sciencedirect.com/science/article/abs/pii/S1361920916309488), walkability index using deep learning (https://www.sciencedirect.com/science/article/abs/pii/S2210670723005073?fr=RR-2&ref=pdf_download&rr=8539e0d14959e28b ), Walkability scoring in high-density cities ( https://www.ulab.hku.hk/walkability-scoring )

• Future Work: Looking into the different parameters to include in our walk score.

[vishal-337]

@vishal-337 I need your individual notebook here asap, please

Submitted my notebook and sorry for the delay professor.

[vishal-337]

Week-5

• Looked into different micro-level features to include in our walk score and looked more into the greenery metric which seems to be mentioned by multiple papers to be one of the most important factors in perceived walkability.
• Read more about the ways greenery could be quantified in an area and look at potential GitHub repo which could help in getting the greenery metric of a street.
• Researched more on PIE to understand its working and methodology. Specifically looked at how the PIE was implemented in cities like Chicago and Montreal and how could this be recreated for Atlanta.

[vishal-337]

Weekly Notebook Entry — Week 6

Overview

• Week Span: 02-12-2024 to 02-16-2024

Tasks for This Week

• [x] Task 1: Work on implementing one of the sub-indices of PIE (intersection density index)

Tasks

Task: [Preliminary implementation of intersection density index] - (completed)

• Description:Using osmnx completed a Python implementation of intersection density index(IDI)
• Link to Work: [Link](https://colab.research.google.com/drive/1oCfIns2IYmgrhfLfGdJ4uXyIAs0i0jh3?usp=sharing)
• Outcome & Reflection: Able to achieve a street network of Georgia Tech Campus and was able to get the basic calculation of IDI, now need to to verify the results and implement it for other areas.

Task: [Further improving the Implementation of IDI] - (ongoing)

• Current Status: Trying a different implementation of IDI using Google Maps API
• Expected Completion: 23rd February-29th February

Task: [Implementation of Land-use diversity index] - (ongoing)

• Current Status: Still need to start
• Expected Completion: 23rd February-29th February

Challenges & Solutions

• I struggled a bit with OSMNX package but going through the documentation helped.

General Takeaway, Skills & Knowledge Acquired

• gained knowledge on osmnx python package
• started using using rhino and grasshopper and started to explore its features.

Team Meetings

• Meeting Highlights: Discussed mainly on the implementation of the different sub-indices of PIE and decided that each person will work on one index

Personal Reflection

• This week has been mostly about getting the ball rolling, getting started on the base work of our metric, and get some preliminary implementation.

Plans for Next Week

• [x] Next Week's Task 1: Verify the result of IDI.
• [x] Next Week's Task 2: Work on finding a better way to calculate IDI.
• [ ] Next Week's Task 3: Start working on the Land-use diversity index.

[vishal-337]

Weekly Notebook Entry — Week 7

Overview

• Week Span: 2/19 to 2/25

Tasks for This Week

List your specific objectives for this week, including learning targets and project milestones.

• [x] Task 1:Work on finding ways to combine near by nodes that are duplicates in the osmnx walking network.
• [x] Task 2: Find geoJSON for the blocks in chicago area.

Tasks

Task 1- (completed)

• Description: The nodes in the osmnx for the walking network have multiple duplicate nodes for a specific intersection, and need a way to combine them into one

Task2:- (completed)

• downloaded the geoJSON for the Chicago sub-traffic zone, and wrote the code to extract the polygons required for IDI calculation.

Challenges & Solutions

• Struggled with finding ways to combine nodes, without disturbing the accuracy of the network structure, but was able to find some functions in osmnx and networkx.

General Takeaway, Skills & Knowledge Acquired

• Acquired knowledge on using GEOJSON and how to extract data from them and learned more functionality of osmnx in integrating shapely polygons into matplotlib.

Team Meetings

• Meeting Highlights: Continue working on individual subindices, consider how some of the parameters of one index will affect another (i.e. population density being a factor for the Commercial Density Index)

Personal Reflection

• Learning a lot on osmnx and learning about network density and how exactly it works in OSMNX.

Plans for Next Week

• [x] Next Week's Task 1: Generate IDI for chicago and compare it to the paper.

[vishal-337]

Weekly Notebook Entry — Week [Number]

Overview

• Week Span: 2/26 to 3/1

Tasks for This Week

• [x] Task 1: Implement the IDI code for Chicago
• [x] Task 2: Create a Visualization for the Chicago IDI to compare to the paper

Tasks

Task: 1 - (completed)

• Description: Take the zonings of Chicago and implement the IDI metric.
• Outcome & Reflection: Generated a IDI metric for each zone in Chicago.

Task: 2 - (completed)

• *Description: Had to create a visualization of Chicago map with zoning to represent IDI value.
• Outcome & Reflection: Used matplotlib to create a colourmap for representing the IDI value of each zone. Compared this result to the one in the paper, to check for accuracy. The results are very similar to the paper.

Challenges & Solutions

• The main challenge was dealing with GeoJSON and extracting the right data for the geometry, but with some exploration of the data, was able to extract the correct data.

General Takeaway, Skills & Knowledge Acquired

• Gained knowledge on how to use matplotlib cmaps for ploting shapely files.

Team Meetings

• Meeting Highlights: General discussion on team tasks and individual updates.

Personal Reflection

• Was able to complete the implemantation of IDI and can move on to other metric.
• Was not able to contribute too much being a very week

Plans for Next Week

• [ ] LDI implentation.

[vishal-337]

Weekly Notebook Entry — Week [Number]

Overview

• Week Span: 3/4 to 3/8

Tasks for This Week

• [ ] Task 1: Start the implementation of the Land Diversity Index

Tasks

Task: [1] - (ongoing)

• Current Status: Right now trying to figure out how to extract the land use of a given polygon, and how can we find out the area of each land use,
• Expected Completion: Expected to complete by 3/15

Challenges & Solutions

• The biggest challenge has been to look at different methods to extract land use data and what would be the best way to execute

General Takeaway, Skills & Knowledge Acquired

• Explored more functionality of OSMNX.
• Explored various datasets online to get the land use data.

Personal Reflection

• This week has been mostly exploring the osmnx documentation and exploring datasets online, and trying different implementations for extracting land use data.

Plans for Next Week

• [ ] Complete the code implementation of LDI.

[vishal-337]

Weekly Notebook Entry — Week [10]

Overview

• Week Span: [3/11] to [3/15]

Tasks for This Week

List your specific objectives for this week, including learning targets and project milestones.

• [ ] Task 1: Extracting land use data for LDI
• [ ] Task 2: Coding the LDI calculation

Tasks

Task1 - (completed)

• Description: For each zone of chicago implemented the code for extracting the land use data
• Outcome & Reflection: Able to extract a list of dictionaries, which contained the data for Land use and also consists of area use

Task: [2 - (ongoing)

• Current Status: Still try in to implement the math for the calculation
• Expected Completion: 1 week

Challenges & Solutions

• Faced some errors in extracting land use, was able to get through by using error exceptions implementation.

General Takeaway, Skills & Knowledge Acquired

• Spent time in understanding the LDI calculation and read more about the methodology.

Personal Reflection

• This week has been mostly about studying the LDI calculation and understanding the extracting land use data and converting into an usable format.

Plans for Next Week

• [ ] Next Week's Task 1: LDI calculation implementation.

[vishal-337]

Weekly Notebook Entry — Week [11]

Overview

• Week Span: [3/25] to [3/29]

Tasks for This Week

List your specific objectives for this week, including learning targets and project milestones.

• [ ] Task 1: Coding the LDI calculation

Tasks

Task1: [ (ongoing)

• Current Status: Still try in to implement the math for the calculation, completed a preliminary implementation but facing problems due to the mismatching of area of the land use and the total polygon area, which is causing an error in the LDI values
• Expected Completion: 1 week

Challenges & Solutions

• Faced issues in calculating area of the polygon, the problem was not in calculation but in figuring out the mismatch of area between the land use data and Chicago zone data. Still trying to figure out the fix for this problem

General Takeaway, Skills & Knowledge Acquired

• Found that some of the data from OSMNX functions is not reliable so tried getting some GeoJSON files online, which have the correct data required.

Plans for Next Week

• [ ] Next Week's Task 1: LDI calculation implementation.

[vishal-337]

Weekly Notebook Entry — Week [12]

Overview

• Week Span: [4/1] to [4/5]

Tasks for This Week

List your specific objectives for this week, including learning targets and project milestones.

• [ ] Task 1: Coding the LDI calculation

Tasks

Task1: (completed)

• *Description: Coded up the calculation for LDI, for each polygon of the chicago zone calculated the LDI for each zone**
• Outcome: Was able to extract an array with the LDI values which is going to used for PEI

Challenges & Solutions

• Faced an issue when calculating the LDI, specifically for area calculation, but was later able to fix it through by just using the total usable land in a polygon(i.e the the sum of area for all land uses) instead of the total area.

General Takeaway, Skills & Knowledge Acquired

Plans for Next Week

• [ ] Next Week's Task 1: Verify the results by comparing it to the referred paper.
• [ ] Next Week's Task 2: Recreate IDI and LDI implementation for atlanta

[vishal-337]

Weekly Notebook Entry — Week [13]

Overview

• Week Span: [4/8] to [4/12]

Tasks for This Week

List your specific objectives for this week, including learning targets and project milestones.

• [ ] Task 1: Recreate the LDI and IDI implementation for atlanta

Tasks

Task1: (completed)

• *Description: Restructured the IDI and LDIcode to make the implementation faster and did it for atlanta data **
• Outcome: Divided the previously written code to different parts, one generates a CSV file with only the required data and with sub-indecies values for each zone. Another for visualizing the data
• Link to Work: https://github.com/VIP-SMUR/Sp24-Mobility-1/blob/main/IDI_Vizulization.ipynb
• https://github.com/VIP-SMUR/Sp24-Mobility-1/blob/main/IDI_generator.ipynb
• https://github.com/VIP-SMUR/Sp24-Mobility-1/blob/main/LDI_Vizulization.ipynb
• https://github.com/VIP-SMUR/Sp24-Mobility-1/blob/main/LDI_Vizulization.ipynb

Challenges & Solutions

General Takeaway, Skills & Knowledge Acquired

Plans for Next Week

• [ ] Next Week's Task 1: Implement the PEI code combining all sub-indicies.