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V. Pastore
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Making Urban Heat Big Data Meaningful for Climate Research
Thickening Big Data with Contextual Knowledge through Visualization
Urban heat is a growing concern for public health, energy demand and urban livability, yet the large-scale datasets used to monitor it remain “thin”: they show where a city is hot, but not how heat is experienced on a specific street. For non-expert users such as designers and architects, this makes the data hard to interpret and act on. This project asks how urban heat big data can be thickened with contextual knowledge through visualization, and made meaningful, meaning relevant, trustworthy and interpretable enough to support a judgment.
Following a Research through Design approach structured by the Double Diamond, the project combined a literature review on big data and data thickening with a review of eleven existing platforms and tools. None combined reliable heat data, situated experiential data and a visualization legible to non-experts. Fourteen ethnographic thermal comfort walks in Delft and Rome then captured the contextual factors that shape comfort at street level, and compared them with what existing platforms show. They were grouped into nine recurring themes, including surface heat, vegetation nuances, airflow, refuges and short-lived heat sources, none of which the platforms register. On the Delft route, each factor was further classified by its relation to the big data layer: adding nuance, hotter, cooler or temporary, with a fifth category, opportunity, marking where comfort could be improved.
These findings informed an interactive, map-based prototype that applies three established thickening strategies: onion-structured layering, ground-truthing and error-guided attention. A toggleable UHI effect layer sits beneath color-coded contextual points that are revealed through a hover-lens and open into street-level panoramas with participants’ quotes and photos. The prototype was refined over three evaluation rounds with six participants, including think-aloud sessions and structured user tests.
Non-expert users could explore and interpret the combined data without researcher mediation once the interface explained its own vocabulary, legend and layers. They recognized where their experience matched or contradicted the heat map, and examined the evidence behind each point themselves. An AI-generated interpretation feature, tested with two participants, did not clearly add value beyond its role in building the prototype: one participant doubted its claims and the other found it made the experience passive.
This project applies established big-thick data strategies to urban heat, a domain where, as far as the reviewed literature shows, they have not yet been used, and treats visualization as part of the thickening process itself. The evaluation suggests that thickened urban heat data becomes meaningful to non-experts when the visualization shows where thick data diverges from big data and lets users examine the evidence themselves. With a small sample, these findings are indications rather than validated principles. Future work should test crowdsourced contributions, keep thick data up to date as conditions change, and extend the prototype to other cities and languages.
Digital Prototype — Data Thickening Platform
https://thickening-prototype-3.ai.studio/ ...
Following a Research through Design approach structured by the Double Diamond, the project combined a literature review on big data and data thickening with a review of eleven existing platforms and tools. None combined reliable heat data, situated experiential data and a visualization legible to non-experts. Fourteen ethnographic thermal comfort walks in Delft and Rome then captured the contextual factors that shape comfort at street level, and compared them with what existing platforms show. They were grouped into nine recurring themes, including surface heat, vegetation nuances, airflow, refuges and short-lived heat sources, none of which the platforms register. On the Delft route, each factor was further classified by its relation to the big data layer: adding nuance, hotter, cooler or temporary, with a fifth category, opportunity, marking where comfort could be improved.
These findings informed an interactive, map-based prototype that applies three established thickening strategies: onion-structured layering, ground-truthing and error-guided attention. A toggleable UHI effect layer sits beneath color-coded contextual points that are revealed through a hover-lens and open into street-level panoramas with participants’ quotes and photos. The prototype was refined over three evaluation rounds with six participants, including think-aloud sessions and structured user tests.
Non-expert users could explore and interpret the combined data without researcher mediation once the interface explained its own vocabulary, legend and layers. They recognized where their experience matched or contradicted the heat map, and examined the evidence behind each point themselves. An AI-generated interpretation feature, tested with two participants, did not clearly add value beyond its role in building the prototype: one participant doubted its claims and the other found it made the experience passive.
This project applies established big-thick data strategies to urban heat, a domain where, as far as the reviewed literature shows, they have not yet been used, and treats visualization as part of the thickening process itself. The evaluation suggests that thickened urban heat data becomes meaningful to non-experts when the visualization shows where thick data diverges from big data and lets users examine the evidence themselves. With a small sample, these findings are indications rather than validated principles. Future work should test crowdsourced contributions, keep thick data up to date as conditions change, and extend the prototype to other cities and languages.
Digital Prototype — Data Thickening Platform
https://thickening-prototype-3.ai.studio/ ...
Urban heat is a growing concern for public health, energy demand and urban livability, yet the large-scale datasets used to monitor it remain “thin”: they show where a city is hot, but not how heat is experienced on a specific street. For non-expert users such as designers and architects, this makes the data hard to interpret and act on. This project asks how urban heat big data can be thickened with contextual knowledge through visualization, and made meaningful, meaning relevant, trustworthy and interpretable enough to support a judgment.
Following a Research through Design approach structured by the Double Diamond, the project combined a literature review on big data and data thickening with a review of eleven existing platforms and tools. None combined reliable heat data, situated experiential data and a visualization legible to non-experts. Fourteen ethnographic thermal comfort walks in Delft and Rome then captured the contextual factors that shape comfort at street level, and compared them with what existing platforms show. They were grouped into nine recurring themes, including surface heat, vegetation nuances, airflow, refuges and short-lived heat sources, none of which the platforms register. On the Delft route, each factor was further classified by its relation to the big data layer: adding nuance, hotter, cooler or temporary, with a fifth category, opportunity, marking where comfort could be improved.
These findings informed an interactive, map-based prototype that applies three established thickening strategies: onion-structured layering, ground-truthing and error-guided attention. A toggleable UHI effect layer sits beneath color-coded contextual points that are revealed through a hover-lens and open into street-level panoramas with participants’ quotes and photos. The prototype was refined over three evaluation rounds with six participants, including think-aloud sessions and structured user tests.
Non-expert users could explore and interpret the combined data without researcher mediation once the interface explained its own vocabulary, legend and layers. They recognized where their experience matched or contradicted the heat map, and examined the evidence behind each point themselves. An AI-generated interpretation feature, tested with two participants, did not clearly add value beyond its role in building the prototype: one participant doubted its claims and the other found it made the experience passive.
This project applies established big-thick data strategies to urban heat, a domain where, as far as the reviewed literature shows, they have not yet been used, and treats visualization as part of the thickening process itself. The evaluation suggests that thickened urban heat data becomes meaningful to non-experts when the visualization shows where thick data diverges from big data and lets users examine the evidence themselves. With a small sample, these findings are indications rather than validated principles. Future work should test crowdsourced contributions, keep thick data up to date as conditions change, and extend the prototype to other cities and languages.
Digital Prototype — Data Thickening Platform
https://thickening-prototype-3.ai.studio/
Following a Research through Design approach structured by the Double Diamond, the project combined a literature review on big data and data thickening with a review of eleven existing platforms and tools. None combined reliable heat data, situated experiential data and a visualization legible to non-experts. Fourteen ethnographic thermal comfort walks in Delft and Rome then captured the contextual factors that shape comfort at street level, and compared them with what existing platforms show. They were grouped into nine recurring themes, including surface heat, vegetation nuances, airflow, refuges and short-lived heat sources, none of which the platforms register. On the Delft route, each factor was further classified by its relation to the big data layer: adding nuance, hotter, cooler or temporary, with a fifth category, opportunity, marking where comfort could be improved.
These findings informed an interactive, map-based prototype that applies three established thickening strategies: onion-structured layering, ground-truthing and error-guided attention. A toggleable UHI effect layer sits beneath color-coded contextual points that are revealed through a hover-lens and open into street-level panoramas with participants’ quotes and photos. The prototype was refined over three evaluation rounds with six participants, including think-aloud sessions and structured user tests.
Non-expert users could explore and interpret the combined data without researcher mediation once the interface explained its own vocabulary, legend and layers. They recognized where their experience matched or contradicted the heat map, and examined the evidence behind each point themselves. An AI-generated interpretation feature, tested with two participants, did not clearly add value beyond its role in building the prototype: one participant doubted its claims and the other found it made the experience passive.
This project applies established big-thick data strategies to urban heat, a domain where, as far as the reviewed literature shows, they have not yet been used, and treats visualization as part of the thickening process itself. The evaluation suggests that thickened urban heat data becomes meaningful to non-experts when the visualization shows where thick data diverges from big data and lets users examine the evidence themselves. With a small sample, these findings are indications rather than validated principles. Future work should test crowdsourced contributions, keep thick data up to date as conditions change, and extend the prototype to other cities and languages.
Digital Prototype — Data Thickening Platform
https://thickening-prototype-3.ai.studio/