One of the main challenges of cities is the increasing social inequality imposed by the way population groups, jobs, amenities and services, as well as the transportation infrastructure, are distributed across urban space. In my PhD thesis, the concepts of accessibility and segregation are used to study these inequalities. Accessibility and segregation can be defined as the interaction of individuals with urban opportunities and with individuals from other population groups, respectively. Interactions are made possible by people’s activities and movement within a city, which characterise accessibility and segregation as inherently dynamic and individual-based concepts. Nevertheless, they are largely studied from a static and place-based perspective. My thesis proposes an analytical and exploratory framework for studying individual-based accessibility and segregation in cities from a time geographic perspective, using individuals’ travel trajectories in space and time.
An agent-based simulation model, named AxS (Accessibility x Segregation), was developed to generate individual trajectories dynamically, employing standard datasets such as census and OD matrices and allowing for multiple perspectives of analysis by grouping individuals based on their attributes such as income, ethnicity, or gender. The model’s ability to simulate people’s trajectories realistically was validated through systematic sensitivity tests and statistical comparison with real-world trajectories from Rio de Janeiro, Brazil, and travel times from London, UK. The approach was applied to two exploratory studies: São Paulo, Brazil, and London, UK. The first revealed inequalities in accessibility by income, education and gender and also unveiled within-group differences beyond place-based patterns. The latter explored ethnic segregation, unveiling patterns of potential interaction among ethnic groups in the urban space beyond their residential and workplace locations. Those studies demonstrated how inequality in accessibility and segregation can be studied both at large metropolitan scales and at fine level of detail, using standard datasets, with modest computational requirements and ease of operationalisation. The proposed approach opens up avenues for the study of complex dynamics of interaction of urban populations in a variety of urban contexts.
The first experiment developed with the AxS model was carried out for the Greater London Authority (GLA) study area. This experiment simulates urban flows with randomly generated origins and destinations inside the urbanised area of the GLA.
The second experiment developed for the GLA study area was carried out with real flow data from the UK 2010 Census. The urban area was divided in zones (MSOAs, from the census) and the information on the number of people living and working in each area was input to the model. A sample of those flows was simulated, and the result can be seen in the video below:
In the AXS model, flows can be disaggregated by population groups defined by characteristics such as ethnicity, income, occupation, etc. This can give us insights into the collective ‘activity space’ of each group, highlighting the more diverse and the more segregated areas of the city in terms of flows. The video below shows the results for the GLA area, disaggregated by ethnic group:
An application of the AXS model to the study of segregation was presented at the ABEP-UK Conference 2018, which took place at London on the 10th of March 2018.