Presentation 1: How and why do General Practice registers and ONS population estimates for Leeds differ?
By: Riz Uddin
What I will be talking about: Since the 2011 Census, data gathered concerning population estimates of Leeds through counts of people register with General Practitioners (GPs) has largely differed from the population estimates produced from Mid-Year Estimates (MYEs) published by the Office for National Statistics (ONS). GP Registers report up to 60,000 more people living in Leeds than in the MYE. This work investigates discrepancies between GP register and MYE counts across particular areas in Leeds and between different sub-groups of the population. Understanding these discrepancies is important for many areas involving city planning such as health planning, transport planning, and election preparation. A single agreed population estimate is highly desirable for targeted commissioning of services. This project has used Geographical Information Systems and classification methods to assess where the discrepancies exist within Leeds and gave further understanding as to why these discrepancies are occurring, indicating potential recent changes in the population composition of Leeds which are unaccounted for by the MYEs.
Presentation 2: Analysis of urban mobility structure with complex networks and telecoms data
By: Ivana Kocánová
What I will be talking about: Understanding urban mobility patterns plays a crucial role in the maintenance of infrastructure, city planning or disruptive events prevention. This research compares 2011 Census data with Telefonica Origin-Destination flows using Complex Network Analysis (CNA). This allows us to study the dynamics of the Leeds urban mobility system and assess the resilience of complex transportation networks. Moreover, using Community detection methods like Modularity optimization we can define unique functional commuting zones.
Presentation 3: Forecasting the Future of Policing
By: Dr Alex Coleman
What I will be talking about: In the 21st Century policing agencies have become involved in a diverse number of roles, often whilst managing relatively restricted resources. Consequently, a key strategic priority for police is identifying means to anticipate the impact of short, medium and long-term changes in demand. The aim of my internship project has been two-fold: (1) to explore various approaches for generating crime-related police demand projections at specific geographic and temporal scales; and (2) to begin development of an agent based model of police resourcing that simulates the capacity of police forces to respond to various crime-related demand scenarios. Predominantly, work has focused on developing approaches to generate simulated crime-related demand at the desired temporal and spatial resource and has been built into an open-source python package crime_sim_toolkit. This work hopes to provide a solid building block for future research into developing new approaches to help police understand, analyse, and anticipate the impact of changing demand.