Quantitative exploration of the mechanisms behind the urban thermal environment in Beijing

Investigating important urban characteristics in the formation of urban heat islands: a machine learning approach

“…As UHIs occur as a result of the land cover transformation, primarily replacement of natural vegetation and natural land cover by impervious surfaced associated with urban land uses, previous empirical UHIs research has mainly focused on the causal relationship between remotely sensed land surface temperature and remotely sensed physical land surface characteristics of urban areas such as elevation and slope [15,16], land cover types [5,10,17,18], percent impervious surface [5,15,19], percent tree canopy cover [5,7,19], vegetation abundance indices [16,[18][19][20].…”

“…Social vulnerability to environmental hazard means the relative potential for physical harm and social disruption to subpopulations of societies and their larger subsystems based on socioeconomic status, age, gender, race and ethnicity, family structure, residential location and other demographic variables [21]. In the studies investigated the relationship between extreme heat relate weather events and socioeconomic status of the area, socioeconomic variables including total population [12], population density [15,16,19], total housing units [12], gender [12], race and ethnicity [12,14,16,22,23], number of older and younger population [12,14,23,24], education [14,23], median house value [12,16,25], median household income [13,22], poverty [14,[22][23][24], housing tenure [22], number of mobile homes [12] were most widely used and found significant.…”

“…So many empirical studies have selected unit of analysis using cumulative results from prior empirical studies. There are studies that have adopted a unit of analysis consistent with the resolution of a satellite image, a grid cell [5,18,19,25], and others used a unit that of socioeconomic variables were measured such as County [13], Census tract [16,17], and Census block group [14,26]. To the best of my knowledge, Stone and Norman [7] is the only study that used a parcel as a unit of analysis.…”

“…As there is no theory that explains the aforementioned relationship, thus the machine learning technique, which allows the computer to learn without being programmed and to identify patterns in data, was used to find highly determinant variables to the formation of UHIs in Marion County, Indiana. To quantify the causal relationship between the land surface temperature and explanatory variables, previous empirical studies have been used regression approaches such as simple regression and spatial regression approaches [19,20,26] and correlation analysis [18]. The PCA method, a mathematical procedure that transforms a number of correlated variables into a smaller number of uncorrelated variables called principal component, has been widely used in many of empirical UHIs and social vulnerability studies [12,13,19] to reduce the data dimension.…”

“…As this index provides an estimate of the abundance of actively photosynthesizing vegetation, thus it can be used to infer general vegetation condition [23,26,30]. NDVI is computed as follows: where TM3 and TM4 refer to the reflectivity of Landsat TM band 3 and band 4, with the wavelength of 0.63-0.69 and 0.76-0.90 µm respectively [19,29]. The value varies from − 1 to + 1, and the greater the value, the better the vegetation condition.…”