Projects / Local Chapter Challenge

[Philippines Chapter] Mapping Urban Vulnerability areas (Crimes, Disasters, etc.) Using Open Source Data

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This Omdena Local Chapter Challenge runs for 8 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world.

You will work on solving a local problem, initiated by the Omdena Malolos, Philippines Chapter, and Laguna, Philippines Chapter

The problem

Many frameworks on the performance of cities generate urban profiles at the city scale, providing limited or no information on the performance of different city sub-units such as districts, wards, zones, settlements, or blocks. The transformative focus of the Agenda 2030 of Leaving no one Behind aligns with the local policies of many cities, their intervention focus being the reduction of spatial inequalities.

Mapping spatial inequalities within the city guides the identification of vulnerable areas, which can be expressed on a continuous scale of vulnerability. Many forms of spatial vulnerabilities such as poor access to basic services, lack of green cover, crime and insecurity, vulnerability to disaster risks, access to opportunities, and access to cultural infrastructure among others, have statistics that can be standardized for comparison and mapped – where data is available.

The individual layers of vulnerability as well as the composite layer combining the layers are useful for spatially targeted intervention by city administrators and other actors. In addition, cities may prepare profiles for their settlements based on a set of indicators to guide city residents in understanding their settlements, and service providers in setting their intervention priorities.

The project goals

The main goal of the project is to identify key characteristics of urban vulnerability and generate data layers. This can be accomplished either by (1) creating surface maps for each form of vulnerability or (2) aggregating vulnerability layers to generate a composite city vulnerability layer. 

The proposed target end state of the project is an interactive web app of the Philippines that maps vulnerability per city. Each city must have an indicated vulnerability level, key features that make it vulnerable, and quantitative factors that can be adjusted to decrease vulnerability.

Below are the milestones necessary to achieve the goal and endstate:

  • Collect satellite images and extract relevant features
  • Aggregate appropriate city level data to generate vulnerability index
  • Extract relevant features/indicators of the vulnerability index
  • Composite vulnerability data with satellite image/coordinate data into a vulnerability layer
  • Build and deploy a predictive model for the vulnerability index per city level based on relevant features
  • Build an interactive web app / map
  • Publish open-source data and project components

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work

First Omdena Local Chapter Challenge?

Beginner-friendly, but also welcomes experts

Education-focused

Open-source

Duration: 4 to 8 weeks



Your Benefits

Address a significant real-world problem with your skills

Build your project portfolio

Access paid projects (as an Omdena Top Talent)

Get hired at top organizations



Requirements

Good English

Suitable for AI/ Data Science beginners but also more senior collaborators

Learning mindset



This challenge is hosted with our friends at


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