TY - GEN
T1 - Model-based clustering of social vulnerability to urban extreme heat events
AU - Tuccillo, Joseph V.
AU - Buttenfield, Barbara P.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Geodemographic classification methods are applied to Denver Colorado to develop a typology of social vulnerability to heat exposure. Environmental hazards are known to exhibit biophysical variations (e.g., land cover and housing characteristics) and social variations (e.g., demographic and economic adaptations to heat mitigation). Geodemographic model-based classification permits a more extensive set of input variables, with richer attributions; and it can account for spatial context on variable interactions. Additionally, it generates comparative assessments of environmental stress on multiple demographic groups. The paper emphasizes performance of model-based clustering in geodemographic analysis, describing two stages of classification analysis. In so doing, this research examines ways in which high heat exposure intersects with socioecological variation to drive social vulnerability during extreme heat events. The first stage classifies tract-level variables for social and biophysical stressors. Membership probabilities from the initial (baseline) classification are then input to a second classification that integrates the biophysical and social domains within a membership probability space to form a final place typology. Final place categories are compared to three broad land surface temperature (LST) regimes derived from simple clustering of mean daytime and nighttime land surface temperatures. The results point to several broad considerations for heat mitigation planning that are aligned with extant research on urban heat vulnerability. However, the relative coarseness of the classification structure also reveals a need for further investigation of the internal structure of each class, as well as aggregation effects, in future studies.
AB - Geodemographic classification methods are applied to Denver Colorado to develop a typology of social vulnerability to heat exposure. Environmental hazards are known to exhibit biophysical variations (e.g., land cover and housing characteristics) and social variations (e.g., demographic and economic adaptations to heat mitigation). Geodemographic model-based classification permits a more extensive set of input variables, with richer attributions; and it can account for spatial context on variable interactions. Additionally, it generates comparative assessments of environmental stress on multiple demographic groups. The paper emphasizes performance of model-based clustering in geodemographic analysis, describing two stages of classification analysis. In so doing, this research examines ways in which high heat exposure intersects with socioecological variation to drive social vulnerability during extreme heat events. The first stage classifies tract-level variables for social and biophysical stressors. Membership probabilities from the initial (baseline) classification are then input to a second classification that integrates the biophysical and social domains within a membership probability space to form a final place typology. Final place categories are compared to three broad land surface temperature (LST) regimes derived from simple clustering of mean daytime and nighttime land surface temperatures. The results point to several broad considerations for heat mitigation planning that are aligned with extant research on urban heat vulnerability. However, the relative coarseness of the classification structure also reveals a need for further investigation of the internal structure of each class, as well as aggregation effects, in future studies.
KW - Biophysical vulnerability
KW - Geodemographic classification
KW - Gini index
KW - Social vulnerability
KW - Urban heat exposure
UR - http://www.scopus.com/inward/record.url?scp=84989904090&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-45738-3_8
DO - 10.1007/978-3-319-45738-3_8
M3 - Conference contribution
AN - SCOPUS:84989904090
SN - 9783319457376
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 114
EP - 129
BT - Geographic Information Science - 9th International Conference, GIScience 2016, Proceedings
A2 - O’Sullivan, David
A2 - Wiegand, Nancy
A2 - Miller, Jennifer A.
PB - Springer Verlag
T2 - 9th International Conference on Geographic Information Science, GIScience 2016
Y2 - 27 September 2016 through 30 September 2016
ER -