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ENVS257 and ENVS279
GIS for Human Geographers GIS for Planners Summative Assessment 2
Use GIS and statistical analysis to identify key commuting patterns in Liverpool and their push-and-pull factors. Key assumptions: - Various area characteristics (such as un-/self-/employment rates) can be used to understand why flow movements occur. - Principal flows are defined by your own threshold of importance. - Movements are for work-related purposes (commuters); not re- location residential purposes (migration). Submission deadline: Check Module Handbook/CANVAS. Project Tasks You will write a short executive report (max. 500 words) that will answer the following questions: 1. What are the main commuting patterns in Liverpool? 2. Where are flows largest? What might this suggest? 3. How does the geography of employment/unemployment relate to flows? 4. How important are distance, origin population, destination population (employed people) and other variables (e.g., unemployment) in explaining commuting flows between the areas of Liverpool? In addition to answering these questions in your report you should present, as a minimum, the following information: 1. A map of percentages of unemployed people. 2. A map showing commuting flows between MSOAs in Liverpool (with your own flow thresholds). 3. A table summarising Poisson regression outputs. Overall, your report should not have more than 6 maps or charts. Maps can be generated within R or QGIS. Data INFUSE - Employment data. - Lots of other variables: age, gender, car ownership etc. CIDER - Flow Data CDRC - Day and Night-time Populations - Geodemographic Classification - Index of Multiple Deprivation (IMD) - Internet User Classification (IUC) OS - Infrastructure Poisson Regression - What factors have an influence on commuter flows? Employment- related and other socio-economic and demographic factors. - Define the variables (what they measure, units) in your regression model. - Explain the sign obtained (+/-) for each slope coefficient. - Comment on the level of significance (highly, moderately, mildly or none) of each coefficient which is the likelihood that the coefficient obtained is a result of pure chance. A highly significant coefficient shows that the relationship between the outcome and explanatory variable is very unlikely to have occurred by chance. - Comment on the model fit statistic (AIC) – Akaike information criteria. How this changes when we add more variables to the model, or change the flow threshold. - Provide reasons for these relationships. Poisson Regression Variables Model 1 (Baseline Model incl. all inter- zonal flows) Model 2 (e.g. Model with top 100 rows) Model 3 (e.g. Model with top 50 rows) Model 4 (e.g. Model with top 20 rows) Model 5 (e.g. Model with top 5 rows) Distance Est(z-stat.)** Est(z-stat)** Est(z-stat.)** Est(z-stat)** Est(z-stat.)** OEmpoyed … DEmployed … OtherVar … AnotherVar … AIC: … Significance Levels: *: Slope coefficient estimate is mildly significant. **: Slope coefficient estimate is moderately significant. ***: Slope coefficient estimate is highly significant. Spatial Dependence - Moran’s I statistic - Clustering of unemployment values. - Test if other explanatory variables also cluster. - Discuss the magnitude – whether negatively, positively autocorrelated. - Discuss p-value: if smaller, this is highly significant. The Report Clear Structure (500 words, +/- 10%): • Introduction • Methodology • Results • Conclusions • Caveats/Limitations Provide a total of 6 figures: • Maps (Min 4, Max 5) – well-labelled and clearly illustrated. - One map showing unemployment rates. - One map showing flows between MSOAs in Liverpool. - Additional maps at your discretion – as deemed relevant to the analysis. • Tables (Min 1, Max 2). Use References (list is not included in word count). Higher Marks • Use GIS layers from earlier parts of the course. • Test effects of additional variables downloaded from INFUSE: prepare data by yourself instructions provided. • Spatial dependence testing on unemployment and other variables. • Good explanations/reasoning for effects of area characteristics on commuter flows. • Evidence of additional reading – i.e. beyond what is provided or recommended reading, and updated versions of policy/planning documents. Final Thoughts • Help and Support is available outside labs on MS Teams - please post questions there rather than by email – and read responses to everyone else’s questions.