Heat action using crowd sourced data
Author: G. K. Bhat, Chairperson, Taru
Indian heat action plans rely on satellite based data and forecasts. These data sets are coarse data based on a few stations across the city. The temperature across any large city with a high population density can show a variation range of 2 to 5 degrees depending on canopy, wind movement, construction of buildings etc. Due to unplanned and rapid urbanisation all major large and medium sized cities in India are facing the phenomenon of urban heat islands. In order to address and mitigate the effects of urban heat islands it is important to understand the variations across the city to take action on ground. Understanding this phenomenon directly enables people to reach thermal comfort. Thermal comfort indexes can be determined and derived by temperature and humidity monitoring.
To monitor the temperature and humidity across the city, when it has very few data monitoring stations, it is necessary to have crowdsourced data across the city to enable government infrastructures and resources to be ready for aggravated situations of crisis - like all local health centres across the city need to be prepared for illnesses due to a rise in temperatures. Crowd sourced data on temperature and humidity at given intervals will be high density and low quality data. This high density and low quality data can then supplement and substantiate the high quality, low density data from government sources, thus enabling communities and populations towards preparedness.
To enable crowdsourcing of these data sets on real time basis, Taru is developing a cost effective (costing less than 30 USD each) Wi-Fi based temperature and humidity logger, which is being field tested now. This system will enable near-real time decision making. About 100 such stations across the city can provide actionable information to redirect scarce health system resources to hot spots. The examples of real time monitoring are presented in following figures
Such big data can be presented collectively on maps, will be an evidence to enable the municipal authorities to direct their response systems at ground level and to be prepared for heat emergencies. If this system of crowd sourced temperature and humidity data can be set up across the city through partnership with municipal authorities and other stake holders, it can drastically contribute positively to reduction of heat island effects of a city. This can be an effective way of garnering people’s voices to change the government's perception and create greater by-in for these kinds of innovations.