[South African Chapter] Mapping Urban Vulnerability areas (Crimes, Disasters, etc.) using Open Source Data

Local Project Johannesburg, South Africa

Coordinated by the Lead of South Africa, Bhoola Shenese,

Status: Ongoing

Partnership.

Project background.

Predicting crime rate and disaster rate by using Descriptive analysis.

Mapping Vulnerability is the process by which the location, access to basic amenities, and susceptibility of the urban poor towards crimes and disasters can be understood. Vulnerability mapping reflects the crime and disaster approach to proactively reach out and understand the issues of the urban poor. It is the mechanism by which the actual needs of the urban poor are reflected in the plans and budgets of the economy.

  • Dissatisfied with emergency services where respondents live.
  • Respondents do not trust people in their communities due to the high crime rate.
  • Most people in the community cannot come back from a loss, resulting in poverty.

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 guide 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 extension, 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.

Project goals.

The AI solution should identify key characteristics of urban vulnerability and generate data layers on them from city-level data, open data sources, extraction of data from imageries, and/or complementary sources. The analysis could involve the creation of surface maps for each form of vulnerability and aggregating the layers to generate a composite ‘city vulnerability layer’.

For useful results, the resolution of the data must be good to enable precise identification of vulnerable locations within the city fabric. Identification of locations with multiple vulnerabilities may guide decision-makers on deteriorating locations, especially when monitoring is done at a consistent temporal scale.

Project plan.

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