R and shiny based tool to visualize results produced by the integrated land-use/transport models SILO and MITO, the repository can be reached here.
Versions
- Version 1.0: Includes the SILO and MITO visualizer, this version is located in the Version_01_Silo_Mito branch here
- Main version: Only SILO visualizer, improved version, more flexible and easier to implement new study cases, this is located in the main branch here
Version 1.0
Functions to visualize results produced by integrated land-use/transport models SILO and MITO.
Open the "dashboard.Rmd" file and click on "Run Document" to open an interactive dashboard.
Upload your result files and use the buttons on the sidebar to control the content and style of the visualization.
Currently, if you are changing the case study (e.g. Munich, Kagawa, etc.), you need to modify the code of the file dashboard.Rmd, line 18, variable default_implementation.
There are examples of input files in this repository: the folders examples/CASE_STUDY/ contain two files that are required to visualize the results of SILO. They are called resultFile.csv and resultFileSpatial.csv.
You can also compare results of multiple scenarios by checking the "Compare scenarios" and uploading results of the comparison scenario.
Main version
Newer and updated R and shiny based tool to visualize results produced by the integrated land-use/transport models SILO.
At least R 3.6.1 is required to install the packages and run the tool
Main functions
- Visualize spatial and aspatial model variables and compare scenarios.
- Export results in .csv format.
- Click on map function to analyze zone-related variables.
- Change scenarios to compare.
- Automatic study case change when load a new scenario located in a different implementation.
Download and install instructions
Installation instructions can be found in the next .pdf file
Open examples
Once the application is open, look for the desired example in the use_cases folder and select one scenario folder.
To compare scenarios, click the checkbox "Compare scenarios" and select the folder corresponding to the second scenario.
Available examples
You can explore the tool with the two available use cases:
- Munich
- Kagawa
Load your own use case
You can also load your own scenarios based on SILO outputs. To load your own use case, please follow the next steps:
Folder structure
- Create your own implementation folder in the use_cases folder. It is recommendable to use a 3-letter name (f.e. muc)
- In your new folder, create a new folder for each scenario you want to visualize, paste the output .csv files (the full list of .csv files is in the "scenarios" section)
- In the implementation folder, create a new folder called "zone_shapefile", and paste your zones shapefile.
Load your zone system shapefile
- The shapefile must be named as "zone_system.shp" and must contain at least the columns:
- shp_id: Identifier linked with the shapefile zones
- shp_muni: Identifier linked with the aggregated zone system
- shp_area: Zone area in square meters
Any question regarding the reference system or columns data type please refeer to the examples
Scenarios
In each scenario folder, paste the .csv output files from SILO, even though not all the tables are needed, it is recommended to have the next .csv files:
- aveHhSize.csv
- carOwnership.csv
- commutingDistance.csv
- dwellingQualityLevel.csv
- dwellings.csv
- eventCounts.csv
- hhAveIncome.csv
- hhRentAndIncome.csv
- hhSize.csv
- hhType.csv
- jobsBySectorAndRegion.csv
- labourParticipationRate.csv
- landRegions.csv
- persByRace.csv
- persMigrants.csv
- popYear.csv
- regionAvailableLand.csv
- regionAvCommutingTime.csv
- resultFileSpatial.csv
Please check the examples to get details about the data structure.
Recommendations
When you use the "compare scenario" function,please make sure that:
- Have the same amount of files in each scenario folder., the names must be the same as well.
- The time horizon must be the same to avoid errors in the comparison functions.
Help, questions and support
Please contact Rafael Muñoz by slack (Rafael Muñoz) or email: rafael.nieto@tum.de. He will contact you as soon as possible.