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Method for analysis and planning based on micro-climate detection Disclosure Number: IPCOM000215841D
Publication Date: 2012-Mar-13
Document File: 6 page(s) / 568K

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Many cities today have the problem of being significantly warmer than surrounding rural areas. This is a result of lack of vegetation and increased absorption of solar radiation by materials such as pavement and rooftops. Currently cities try to address this problem generally such as planting trees, painting rooftops white, using light-colored pavement, restricting impervious coverings, but make little headway because they're unable to identify localized climate effects that contribute to a city's overall climate issues. This article describes how data from mobile temperature collection devices (on city vehicles) can be combined with data from stationary collection sites to obtain a very granular picture of localized micro-climates throughout a region. This data is used to guide heat island mitigation efforts.

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Method for analysis and planning based on micro -climate detection

Current methods for climate island detection are on a macro scale and data used is typically satellite-based.

Data would be collected for analysis by measuring data from stationary measurement sources such as airports, schools, etc., as well as mobile sources, such as sensors on city vehicles (fire trucks, police cars, garbage collection, busses, trains, utility vehicles). Examples of data collected include: Sunlight, temperature, humidity, atmospheric pressure.

The reason for mobile data collection is that readings from a representative sample of stationary sites provide less granular conditions for a given time of day, measured in arbitrary (e.g. 1 minute) increments. This stationary site data provides the basis for comparison with more granular readings which are capable of detecting issues affecting the specific sources of climate variations.

There are expected natural deviations from a city-wide average temperature. These deviations may result from proximity to water, elevation, windward/leeward hillsides, etc. The idea is to identify what the natural temperature would be without the city's man-made structures, such as buildings, pavement, etc. That "natural" climate would be the baseline against which the micro-climate analysis is done.

A map of deviations from the "natural" climate would be generated by the fleet of mobile sensors.

Once enough readings have been obtained in any given area to provide statistical significance for a given time of day, analysis can proceed. Deviations from the "natural" climate can be identified and investigated further. When appropriate, deviations can be remedied by optimizing city resource planning. Some methods for remediation of micro-climate deviations might include: Reducing impervious ground coverings, reflective pavement coatings, planting trees, painting roofs white, etc.

The following plot represents hypothetical data points collected from both mobile temperature transmitters and stationary weather sites over a 20-minute period between 7:55 AM and 8:15 AM. The black x's represent stationary sites, each transmitting temperature readings every 5 minutes; each string of colored circles represents the readings from a mobile transmitter (on garbage trucks, utility vehicles, ambulances, etc.).

Because the temperature changes through the day, this data cannot simply be evaluated as-is; later data points would be expected to be warmer than earlier ones. For this simplified example, the map will be evaluated in 10 minute windows.


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These are the locations with readings between 7:55 AM and 8:05 AM:

From this map, a clustering algorithm can be applied to establish where there are enough data points (there are well-studied algorithms in this area, such as the expectation maximization algorithm). Adjustments to the parameters of the clustering algorithm can increase or decrease the granularity of clusters. These clus...