Integrating land-cover data with data on population and household characteristics to assess densification along the BRT ROUTE in the city of Tshwane

This paper is centred on an investigation of whether the integrating earth observation and census data can result in useful information to support transport planning and monitoring. In particular circular buffers with radii of 500 m were created covering the current City of Tshwane’s Bus Rapid Transit (BRT) service areas in order to estimate the proportion of population with convenient access to BRT; and to assess densities in terms of both population and relevant land use characteristics. A combination of data sets were used, namely, the 2011 census, GPS locations of the BRT stops and BRT routes, the satellite-derived urban land-cover and the building land use data. The results indicate that 5% of the population in the City of Tshwane has convenient access to the BRT service. Population densities along the BRT service area range from 2 351 to about 37 518 people per 0.79 km2. Some of the BRT service buffers have low population densities and low building densities; while others have moderate to high population densities and high proportions of residential and commercial properties. Routes from Pretoria central towards Sunnyside vary from medium to high densities with respect to population and residential (cluster type residences, flats and student accommodation) and commercial buildings. It can be deduced from these results that there is potential to proceed with proposed densification strategies along BRT routes given that current densities are lower than desired at specific parts of the route. This could subsequently promote public transport accessibility and usage. In conclusion, publically available data that were used in this study and further enhancements of the methodology can be used as a tool for monitoring the implementation of the densification strategy along the BRT route.Papers Presented at the 2018 37th Southern African Transport Conference 9-12 July 2018 Pretoria, South Africa. Theme "Towards a desired transport future: safe, sufficient and affordable"

Modelling representative population mobility for COVID-19 spatial transmission in South Africa

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.The National Research Foundation (NRF) and Canada’s International Development Research Centre (IDRC).https://www.frontiersin.org/journals/big-dataam2022Statistic