tigerdash| Planet, With People
Planet, With People
tigerdash
Summary
Our vision is to help make urban cities climate resilient by design through data.tigerdash is a dashboard that provides urban planning institutions with climate risk assessments on urban areas which are derived from combined geospatial, social, and climate data.
How We Addressed This Challenge
We built a dashboard that urban planners can access to see climate risk assessments on any given city or locality.
It is important because Philippines, by geography, is typhoon ridden. About 20 tropical cyclones hit the Philippines waters wherein 7-9 make landfall each year. Investments in renewable energy, sustainable systems, and zero-carbon would result to a carbon-free world in 2-3 decades, if everything goes as planned. Therefore it is vital that layers of protection and resilience must be invested upon today to minimize casualties, increase preparedness, and improve the capabilities on withstanding climate-induced events.
tigerdash provides instant climate risk assessment on any given area in the urbanscape through a resilience index. The indices are resulting from data analysis of all the indicators: climate, spatial, and social. The indicators have a number of datasets that are scored per se, and the scores add up and averaged to come up with the indices. The indices will indicate if a given area is anywhere between very resilient, resilient, fairly resilient, risky, very risky. The individual scores of the datasets in any given indicator can also provide the planning institutions a view on where the risks root from, and come up with land use and development plans to mitigate the issues accordingly.
Here is the link to the prototype: https://cyntwikip.github.io/projects/gsa/tigerdash.html
Here is another link to the dashboard with graphs: https://bit.ly/3iroZbB
The indices are presented in color coded blips, which will soon be developed into boundaries, covering a specific selected location. The colors indicate the assessments right away. If the users wish to know further details, they can select the blips and see the breakdown of the scores. Graphs are also used to make side-by-side comparison of given data, such as the proportion of resilient to risk-prone areas in a given city in percentage and also in number.
We aim that this will be used by urban planning and architectural institutions to design more sustainable and climate resilient cities. This is aimed to also have the capability to be integrated in their digital systems. Not only we cull all the relevant data that they look for prior to making plans, we also provide an assessment to see issues right away.
How We Developed This Project
All of us are sustainability advocates. We are passionate about the kind of progress that does not jeopardize the world's future. With our technological expertise, we want to use our capabilities in building innovative solutions that fill sustainability gaps.
We truly wanted to provide clear and accurate mapping of the assessments on any given part of an urban city. To be able to do this, we want to gather as much recent data as we could from government institutions and units pertaining to the indicators mentioned. Since the government offices do not have APIs where we can extract data real-time, we had to manually scour for data and crunch it to arrive with accurate indices. For missing recent data, we used simulated or synthetic data with reference to past data. As for scoring the indices, we based on the moderate or "safe" levels per parameter to have a baseline of which qualitative or quantitative data is at safe levels or otherwise.
We used PowerBI for our dashboard and HTML/CSS, JavaScript, Mapbox, and Github for our web application with geospatial analytics capability. For the data analysis and feature engineering, we used Python, GeoPandas, OpenStreetMap, and GADM. Our challenges undoubtedly comes from the efficiency and accuracy of gathering data. It is either the "recent" data is not very recent, or it is not publicly available. But a part of our value proposition is collecting all these vital data so that the users would not have to bother wasting time and resources to collect them one by one from various government agencies. See the prototype here: https://cyntwikip.github.io/projects/gsa/tigerdash.html
How We Used Space Agency Data in This Project
We used Landsat (NASA) for our map.
Project Code
Data & Resources
- HTML/CSS and Javascript for client-side web application
- GitHub for hosting
- Preparatory and Initial Data Analysis using MS PowerBI - https://bit.ly/3iroZbB
- Python and GeoPandas for feature engineering, data science, and geospatial analysis
- OpenStreetMap and Overpass for getting spatial features
- Database of Global Administrative Areas (GADM)
- Landsat (NASA) map background in Mapbox
- Climate-data.org for climate features
- Population and density data from https://worldpopulationreview.com/
- Video Background - http://www.freepik.com/
*The indicators' data are simulated. The indicators are calculated from the actual values using https://www.researchgate.net/publication/327433953_MEASURING_URBAN_RESILIENCE_USING_CLIMATE_DISASTER_RESILIENCE_INDEX_CDRI as guide.