4. Analysis & Generate Resources
An example region fully configured with downloaded data has been provided for Las Palmas de Gran Canaria in Spain with the target year of 2023. See the file process/configuration/regions/example_ES_Las_Palmas_2023.yml to view configured settings and paths to data provided for this example city. This section will demonstrate how to perform an analysis of this region, using the GHSCI graphical user interface app in your web browser. The same example has also been provided as a Jupyter notebook (example.ipynb), that may be selected and opened from within Jupyter Lab.
From the launched software prompt, type ghsci to start the web app and click the displayed link to open a web browser at http://localhost:8080.
The Global Healthy and Sustainable City Indicators app opens a tab for selecting or creating a new study region (Figure 3). We can see that the city of Las Palmas de Gran Canaria, Spain has been Configured but hasn't yet had analysis performed or resources generated. Once two configured regions have had their resources generated, they can be compared. Additionally, the results of a completed policy checklist can be summarised and queried.
Figure 3. Create, search and view summary details for your study regions using the GHSCI web app interface before performing analysis, generating resources, running comparisons, or querying the results of a policy audit.
To run the example, click to select 'example_ES_Las_Palmas_2023' in the table, head to the Analysis tab and click the button. While analysis is being conducted, progress will be summarised in the terminal. This may take a few minutes to complete (Figure 4).
Figure 4. Performing analysis and generating resources will run code in the terminal window; view the outputs of these steps as they run to receive more information on what to do next.
Once completed, if you return to the 'Study regions' tab the study region summary will have the Analysed check box ticked and if you click to select the example in the table it will display the configured study region boundary on the map (Figure 5).
Figure 5. The study region boundary can be visualised on a map.
Click the study region to view a popup summary of the core set of indicators calculated (spatial distribution data will be generated shortly, and directions for producing an interactive map are provided in the example Jupyter notebook).
To generate the range of resources listed above, with the example city selected navigate to the Generate tab and click the Generate resources button. A series of outputs generated will be reported in the terminal window (Figure 6) and can be located in the study region's data output folder (Figure 7).
Figure 6. The list of generated resources is summarised in the terminal window, while a summary of core indicators for the region can be viewed on the interactive map.
Figure 7. The list of generated folders and files following analysis and generating resources for a city.
A log file will be generated in the study region folder that can assist with debugging if things go wrong or otherwise verifying the process has been successful and let you see some of the details of how things are processed. For the example city, this file is __Las Palmas de Gran Canaria__example_ES_Las_Palmas_2023_processing_log.txt.
The file _parameters.yml contains a record of your project, region and data configurations that gave rise to the generated outputs. Spatial features used in analysis as well as grid and overall city summaries are saved within a geopackage file, and CSV files of final summary results are provided too.
The PDF reports (Figures 8 and 9) are located within the folder reports, while the maps and images generated for use in that report (Figure 10) are located in the figures folder.
Figure 8. PDF reports in English and Spanish for Las Palmas.
Figure 9. Example page from the Spanish PDF policy and spatial indicators report for Las Palmas (policy results have not been completed and are are included for illustration purposes only).
Figure 10. Generated plot and map figures from the analysis of Las Palmas, with annotations in English and Spanish.
You can use the Compare function to
✔️ evaluate the overall impact of parameters and data used (sensitivity analyses)
✔️ compare results of different cities (benchmarking)
✔️ compare results for the same study region across time (monitoring)
✔️ evaluate the impact of hypothetical scenarios or interventions through analysis of modified data to represent these
See the provided Jupyter notebook tutorial or the section of this wiki on advanced features for a detailed example of how the compare functionality of the GHSCI tool can be used to evaluate aspects such as those listed above.