International Journal of Environmental Research and Public Health

Article Spatially Analyzing the Inequity of the Urban Heat Island by Socio-Demographic Characteristics

Man Sing Wong 1,2, Fen Peng 1, Bin Zou 3,*, Wen Zhong Shi 1,2 and Gaines J. Wilson 4 1 Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China; [email protected] (M.S.W.); [email protected] (F.P.); [email protected] (W.Z.S.) 2 Joint Spatial Information Research Laboratory between The Hong Kong Polytechnic University and Wuhan University, Wuhan 430072, China 3 School of Geosciences and Info-Physics, Central South University, Changsha 410083, China 4 Department of Biological Sciences, Huston-Tillotson University, Austin, TX 78702, USA; [email protected] * Correspondence: [email protected]; Tel.: +86-186-0731-2100

Academic Editor: Yu-Pin Lin Received: 2 February 2016; Accepted: 8 March 2016; Published: 12 March 2016

Abstract: Recent studies have suggested that some disadvantaged socio-demographic groups face serious environmental-related inequities in Hong Kong due to the rising ambient urban temperatures. Identifying heat-vulnerable groups and locating areas of Surface Urban Heat Island (SUHI) inequities is thus important for prioritizing interventions to mitigate death/illness rates from heat. This study addresses this problem by integrating methods of remote sensing retrieval, logistic regression modelling, and spatial autocorrelation. In this process, the SUHI effect was first estimated from the Land Surface Temperature (LST) derived from a Landsat image. With the scale assimilated to the SUHI and socio-demographic data, a logistic regression model was consequently adopted to ascertain their relationships based on Hong Kong Tertiary Planning Units (TPUs). Lastly, inequity “hotspots” were derived using spatial autocorrelation methods. Results show that disadvantaged socio-demographic groups were significantly more prone to be exposed to an intense SUHI effect: over half of 287 TPUs characterized by age groups of 60+ years, secondary and matriculation education attainment, widowed, divorced and separated, low and middle incomes, and certain occupation groups of workers, have significant Odds Ratios (ORs) larger than 1.2. It can be concluded that a clustering analysis stratified by age, income, educational attainment, marital status, and occupation is an effective way to detect the inequity hotspots of SUHI exposure. Additionally, inequities explored using income, marital status and occupation factors were more significant than the age and educational attainment in these areas. The derived maps and model can be further analyzed in urban/city planning, in order to mitigate the physical and social causes of the SUHI effect.

Keywords: environmental inequity; land surface temperature; socio-demographic characteristic; spatial autocorrelation; urban heat island

1. Introduction Environmental inequity can be defined as a type of inequality when a particular social group is disproportionately burdened with environmental problems, e.g., air pollution and heat stress [1–3]. The underlying contributors to environmental inequity can be political, economic, and social factors [4,5]. Hong Kong is a highly urbanized and densely populated city with a complex and dense population spatial distribution, including different ages, educational attainments, occupations, marital statuses, and incomes. Hong Kong also has one of the highest income inequities in the

Int. J. Environ. Res. Public Health 2016, 13, 317; doi:10.3390/ijerph13030317 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2016, 13, 317 2 of 17 world [6]. The Urban Heat Island (UHI) is a phenomenon in which ambient air temperature in the city center is elevated relative to surrounding non-urbanized areas [7,8]. The main causes of UHI include: (i) the replacements of soil and vegetation by impervious surfaces, e.g., concrete and asphalt; (ii) urban structures, e.g., tall buildings and narrow streets; and (iii) anthropogenic heat discharges [9]. As a consequence of rising temperature, the numbers of people suffering adverse heat health effects (e.g., heat exhaustion, heat stroke) are expected to increase, and these effects may be disproportionately distributed over disadvantaged populations [10]. UHI is strongly related to the built environment [11–13] as well as some socioeconomic and demographic characteristics [14–16]. Huang et al. found that the land surface temperatures were highly correlated with poverty, lower education, ethnic minorities, and elderly people in Baltimore, U.S. [15]. In Hong Kong, Chan et al. found that people younger than 75 years, married, and living in districts of low socioeconomic status were more susceptible to high temperatures, which lead to the higher of mortality rates among these groups [16]. There are several methods used to measure the UHI: (i) traditional observations based on atmospheric observation data from monitoring stations [17]; (ii) remote sensing techniques that derive land surface and air temperature [18], vegetation indices [19], and thermal landscape [20]; and (iii) modelling and prediction, e.g., boundary layer models [21,22]; (iv) fixed-point observation method; and (v) transect sampling method. Due to the sparse and unevenly distribution of monitoring stations in Hong Kong, remote sensing data thus provide a synoptic observation covering the entire territories. A Surface Urban Heat Island (SUHI) can be defined as the UHI effect measured by land surface temperature. With the results of the SUHI, an inequity analysis of population exposure to the SUHI for specific socio-demographic groups can therefore be conducted [14,15,23]. This study aims to: (i) investigate whether any groups of socio-demographic characteristics or disadvantaged groups are more likely to reside in urban heat island core areas in Hong Kong; and (ii) examine the spatial distribution patterns of the inequity hotspots in Hong Kong. The study results can provide crucial information to help decision-makers in Hong Kong and other similar administrations understanding the spatial inequities of environmental burdens over geographical area, and thus prioritize interventions to mitigate death/illness rates from heat for disadvantaged populations.

2. Materials and Methods

2.1. Study Area Hong Kong is located at latitude 22˝91142 N~22˝331442 N, longitude 113˝50172 E~114˝261302 E (Figure1), and is one of two special administrative regions of the People’s Republic of China. It is situated on the south-eastern coast of mainland China, with a total population of 7.19 million in 2013, which consists of (17.7% of the total population), Kowloon Peninsula (30.0% of the total population), and (52.2% of the total population) [24]. Hong Kong is situated in a humid sub-tropical climate, where spring is warm and wet, and autumn is warm and dry. Approximately 1948 h of sunshine can be observed per year, and about 90% of the rainfall occurs between April and September [25]. Monthly mean air temperatures range between 16.1 ˝C and 28.7 ˝C. The record high and low temperatures ever recorded by the (HKO) are, respectively, 36.1 ˝C on 19 August 1900 and 18 August 1990, and 0.0 ˝C on 18 January 1893 [26]. The mean monthly air temperature on November 2005 was 23.0 ˝C, based on data observed at the Hong Kong International Airport. Int. J. Environ. Res. Public Health 2016, 13, 317 3 of 19 Int. J. Environ. Res. Public Health 2016, 13, 317 3 of 17

FigureFigure 1. 1.Study Study area area of of Hong Hong Kong. Kong.Left Left:: provinceprovince boundaryboundary of of China; China;Right Right:: TertiaryTertiary Planning Planning Units Units (TPU)(TPU) of of Hong Hong Kong. Kong.

2.2.2.2. DataData SourcesSources and and Analysis Analysis MemonMemonet et al. [al.27 ][27] stated stated that thethat maximum the maximum values of values mean monthlyof mean UHI monthly (urban-rural UHI temperature)(urban-rural intemperature) the year 2005 in werethe year observed 2005 were in winter observed and in spring, winter andand thespring, second and highestthe second UHI highest values UHI were values also observedwere also in observed autumn in autumn Hong Kong. in Hong Thus, Kong. a cloud-free Thus, a Landsatcloud-free 5 ThematicLandsat 5 Mapper Thematic (TM) Mapper image (TM) on 23image November on23 November 2005 was used2005 inwas this used study. in this The study. day of The 23 Novemberday of 23 November 2005 was cloud-free2005 was cloud-free and withlow and windwith speed,low wind and speed, there was and no there rainfall was withinno rainfall +/´ within3 days +/ during− 3 days the during data acquisition. the data acquisition. The Landsat The 5 TMLandsat image 5 wasTM image orthorectified was orthorectified to the World to Geodeticthe World System Geodetic (WGS) System 84 Universal(WGS) 84 Universal Transverse Transverse Mercator (UTM)Mercator zone (UTM) 49N coordinatezone 49N coordinate system. system. InIn addition,addition, HongHong KongKong topographictopographic andand digitaldigital mapsmaps werewere acquiredacquired andand usedused inin thethe study.study. TheThe 2006 2006 population population data data at Tertiaryat Tertiary Planning Planning Units Units (TPU) (T inPU) Hong in Hong Kong wereKong retrieved were retrieved from the from Census the andCensus Statistics and DepartmentStatistics Department of Hong Kong of Hong [28], which Kong provides [28], which open provides data sets [open29]. Thedata boundaries sets [29]. ofThe TPUs boundaries are demarcated of TPUs byare the demarcated Planning by Department the Planning of theDepartment Government of the of Government the Hong Kong of the Special Hong AdministrativeKong Special Administrative Region (HKSAR). Region TPUs (HKSAR). typically represent TPUs typically geographical represent areas geog boundedraphical by areas roads, bounded railway lines,by roads, coastlines, railway contours, lines, coastlines, waterways, contours, lot boundaries waterways, or zoning lot boundaries boundaries or zoning of town boundaries plans, which of town can provideplans, which a common can provide geographic a common system geographic for the compilation system for of statisticalthe compilation data [30 of]. statistical The boundaries data [30]. of TPUsThe boundaries are regularly of updatedTPUs are to regularly reflect population updated to dynamics, reflect population in which dynamics, a TPU of lessin which than1000 a TPU persons of less isthan merged 1000 persons with an is adjacent merged TPU. with an A TPUadjacent is a TPU. geographic A TPU unitis a geographic used by the unit Government used by the for Government planning purposes.for planning The purposes. entire territory The entire of Hong territory Kong of was Hong divided Kong into was 287 divided TPUs into in 2006. 287 TPUs in 2006.

2.3.2.3. MethodologyMethodology AA suite suite of methodsof methods including including remote remote sensing sensing retrieval retrieval algorithms, algorithms, Population Population Dynamic MappingDynamic ModelsMapping (PDMM), Models logistic(PDMM), regression logistic modelsregression and spatialmodels autocorrelationand spatial autocorrelation analysis in Geographical analysis in InformationGeographical Systems Information (GIS) were Systems utilized (GIS) to evaluatewere utilized inequities to evaluate in exposure inequities to the SUHI in exposure in Hong Kong.to the SUHI in Hong Kong. 2.3.1. Derivation of Land Surface Temperature Image 2.3.1. Derivation of Land Surface Temperature Image This study applied a mono-window algorithm to retrieve the Land Surface Temperature (LST) from aThis 2005 study Landsat applied TM imagea mono-window at 30 m spatial algorithm resolution. to retrieve A mono-window the Land Surface algorithm Temperature is appropriate (LST) forfrom retrieval a 2005 usingLandsat only TM a singleimage thermal at 30 m band,spatial whereas resolution. the A split-window mono-window algorithm algorithm is recommended is appropriate forfor two retrieval thermal using channels only a [single31]. The thermal land surface band, whereas temperatures the split-window were divided algorithm into six classes is recommended (e.g., low, sub-low,for two thermal medium, channels sub-high, [31]. high, The and land extreme surface hightemp temperature)eratures were baseddivided on into the meansix classes and standard(e.g., low, deviationsub-low, (SD)medium, of the sub-high, temperature high, distribution and extreme [ 32high], in temperature) which the classes based ofon high the mean and extreme and standard high temperaturedeviation (SD) were of identifiedthe temperature as the distribution SUHI core areas.[32], in The which details the on classes the LST of high retrieval and areextreme available high fromtemperature previous were studies identified [33]. The as rationalethe SUHI for core categorizing areas. The SUHIdetails or on non-SUHI the LST retrieval areas of are a 120 available m ˆ 120 from m previous studies [33]. The rationale for categorizing SUHI or non-SUHI areas of a 120 m × 120 m grid

Int. J. Environ. Res. Public Health 2016, 13, 317 4 of 17 grid is to find whether the total grid numbers of 30 m ˆ 30 m pixels with high and extreme high temperatures are greater than eight (half of the total pixel number) inside the 120 m grid.

2.3.2. Reclassification of Socio-Demographical Indicators and LST Following previous studies [4,34,35], groupings of age, income, educational attainment, marital status and occupation were deemed to be valid representations of socio-demographic indicators. These characteristics were categorized into different levels based on the reference categories (Table1)[ 4]. In order to characterize SUHI exposure inequity using the logistic regression model, SUHI areas should first be identified and validated. The thresholds and classes of LST were determined using Equation (1):

T “ µ ˘ χ¨ SD (1) where T denotes temperature threshold value, µ is mean land surface temperature of Hong Kong, SD is standard deviation of LST, and χ denotes multiple of deviation (values is assigned 0.5, 1.0) [36]. Table2 displays the derived temperature ranges and classes of land surface temperature over Hong Kong.

Table 1. Standards of categorization and reference categories for socio-demographic measurements.

Income (HK$ Educational Age Marital Status Occupation per Month) Attainment Pre-primary Managers, Administrators, Professionals, 0–14 <4 K Unmarried Primary Associate Professionals 1 Secondary Sixth 14–60 1 4 K–10 K Married 1 Clerks, Service Workers, Shop Sales Workers form Widowed Craft and Related Workers, Plant and Machine >60 10 K–20 K Post-secondary 1 Divorced Operators, Assemblers Separated Elementary Occupations, Skilled Agricultural and 20 K–40 K Fishery Workers, Occupations not classified >40 K 1 1 Reference category for comparison based on existing studies.

Table 2. Temperature ranges and classes of land surface temperature in Hong Kong.

Temperature Range Class Extreme high temperature area TS > µ+ SD High temperature area µ + 0.5SD < Ts ď µ + SD Sub-high temperature area µ < Ts ď µ + 0.5SD Medium temperature area µ ´ 0.5SD ď Ts ď µ Sub-low temperature area µ – SD ď Ts<µ ´ 0.5SD Low temperature area Ts < µ ´ SD

2.3.3. Population Density by Socio-Demographic Characteristics The population density in Hong Kong was modelled using the PDMM method. The socio-demographic characteristic data were presented by TPU in different homogenous zones based on a combination of areal weighting and an urban-classification method [37]. Empirical sampling provides a proportional density fraction used as a weighted value representing each urban class. The urban class is based upon land use and land cover data at 30 m spatial resolution. The ArcMap extension module “dasymetric mapping” was used to model population density, which can automate the areal interpolation process within a GIS framework [38]. The modelling of population distribution using PDMM at relatively high spatial resolution data has been used in previous studies [39,40]. Since the populations of 30 m ˆ 30 m grids in some sparsely populated areas were almost close to zero, Int. J. Environ. Res. Public Health 2016, 13, 317 5 of 17 the lower resolution 120 m ˆ 120 m grid was thus selected to calculate the ORs of the TPUs, where there are 16 grids in order to ensure data quality.

2.3.4. Logistic Regression Model In terms of the nominal variable (SUHI/non-SUHI areas) and socio-demographic variable at 120 m ˆ 120 m grids (i.e., which indicates that each TPU has dozens of grids for regression modeling), a binary logistic regression was adopted to calculate the OR value of each TPU at the 120 m spatial resolution using the SPSS software package (International Business Machines Corporation (IBM), New York, NY, USA). In this process, the nominal variable classified as SUHI areas and non-SUHI areas was the dependent variable (within the SUHI core areas were coded as 1, and outside the SUHI core areas were coded as 0). Socio-demographic factors were the independent variable (the reference and target groups were coded as 0 and 1, respectively). The population numbers with different socio-demographic characteristics at 120 m ˆ 120 m grids were input as weights in the calculation. Thus, a total of 287 Odds Ratio (OR) values were calculated in Hong Kong as the number of TPUs by each categorical socio-demographic factor. The Odds Ratio value indicates the relative amount by which the odds of the outcome increases (OR greater than 1.0) or decreases (OR less than 1.0) as the predictor value increases by 1.0 unit.

2.3.5. Spatial Autocorrelation Method (i) Global Autocorrelation Analysis Global spatial analysis or global spatial autocorrelation analysis yields only one statistical result to summarize the entire study area. In short, global spatial analysis assumes homogeneity within the study area. In practice, the homogeneity assumption may not hold. Thus having only one statistical result may not be suitable for diverse and more complex spatial patterns [41,42]. There are several ways to test the global autocorrelations of events. The most popular among spatial autocorrelation methods is Moran’s I statistic, which is used to test the null hypothesis that the spatial autocorrelation of a variable is zero [43,44]. If the null hypothesis is rejected, the variable will be considered spatially autocorrelated. Moran’s I statistic of spatial autocorrelation is described by Cliff and Ord [45]. In this study, the Hong Kong TPU was used as the base spatial unit [46]. Spatial autocorrelation analysis was developed based on the ORs value of TPUs by categorical socio-demographic factors. Moran’s I index indicates the extent of global spatial autocorrelations of SUHI inequities by categorical socio-demographic indicators (i.e., age, income, educational attainment, marital status, and occupation). The analyses were conducted using the “Univariate Moran’s I” in GeoDa. (ii) Local Hotspot Detection Although global autocorrelation has been observed, the more detailed local pattern of hot spots requires further assessment. The challenge is in finding an appropriate test for local spatial autocorrelation in the presence of global spatial autocorrelation. Univariate local Moran’s I-based cluster mapping has been suggested as an effective method in detecting the hot spots or cluster areas of environmental exposure inequity based on spatial autocorrelation theory [45]. To identify hot spots or cluster areas that are statistically significant, the cluster and outlier analysis [47] functions were utilized to identify local spatial patterns of the SUHI inequity in the study area. Moran’s I index calculates the difference between the target and the mean for all values, with a range between ´1.0 and +1.0 [47]. If spatial objects that are located more closely together have similar attributes (i.e., high values near high values; low values near low values), Moran’s I index will be positive, and if different attributes are located close together, the index value will be negative. If the values in the dataset tend to be stochastic spatially, the index will be near zero. The Univariate local Moran’s I in GeoDa was utilized to identify local hot spot areas of SUHI exposure inequity in this study [48]. A z-score is used to test the null-hypothesis. A high positive Int. J. Environ. Res. Public Health 2016, 13, 317 6 of 17 z-score (z > 3.29) for SUHI exposure inequities of a TPU with p value at the 99.9% confidence level indicatesInt. J. Environ. the surrounding Res. Public Health features 2016, 13, 317 have either high or low OR values (i.e., high-high, or6 low-low). of 19 Inversely, a low negative z-score (z < ´3.29) for SUHI exposure inequities in a TPU with p value at the 99.9%indicates confidence the surrounding level indicates features a significant have either spatial high outlier or low ( i.e.OR, high-low,values (i.e. or, high-high, low-high) or [49 low-low).]. High-high areasInversely, are deemed a low to negative high-risk z-score areas (z < of −3.29) spatial for inequitiesSUHI exposure by socio-demographicinequities in a TPU with characteristics p value at in the study.the 99.9% confidence level indicates a significant spatial outlier (i.e., high-low, or low-high) [49]. High-high areas are deemed to high-risk areas of spatial inequities by socio-demographic Currently, there are two basic categories of neighbor definitions: contiguity (shared borders) characteristics in the study. and distance. Contiguity-based weights matrices include rook and queen. Distance-based weights Currently, there are two basic categories of neighbor definitions: contiguity (shared borders) and matricesdistance. include Contiguity-based distance bands weights and k matrices nearest neighborsinclude rook [50 ].and For queen. accurate Distance-based detection of weights the hotspot areasmatrices with SUHI include exposure distance inequity bands and in this k nearest study, neig “k-nearesthbors [50]. neighbors” For accurate belonging detection to of “Distance-based the hotspot weightsareas matrices” with SUHI were exposure employed inequity to createin this spatialstudy, “k-nearest weights. neighbors” This is because belonging the size to “Distance-based of TPU areas varies considerablyweights matrices” and some were of thememployed do notto create share spatial borders weights. with neighbors.This is because In thisthe size process, of TPU the areas number of k-nearestvaries considerably neighbors wasand setsome as of six them in order do not to ensureshare borders that every with polygonneighbors. has In thethis same process, number of neighbors.the number of k-nearest neighbors was set as six in order to ensure that every polygon has the same number of neighbors. 3. Results 3. Results 3.1. Distribution of Urban Heat Islands in Hong Kong 3.1. Distribution of Urban Heat Islands in Hong Kong For the Landsat TM data, the mean temperature of the LST was 24.8 ˝C. Figure2 indicates there is For the Landsat TM data, the mean temperature of the LST was 24.8 °C. Figure 2 indicates there an observable and strong urban heat island effect in Hong Kong. These SUHI core areas are mainly is an observable and strong urban heat island effect in Hong Kong. These SUHI core areas are mainly distributeddistributed in several in several highly highly urbanized urbanized areas areas suchsuch as the the commercial commercial centers centers in Kowloon in Kowloon Peninsula, Peninsula, the northernthe northern Hong Hong Kong Kong Island Island and and Hong Hong Kong Kong InternationalInternational Airport. Airport.

FigureFigure 2. Spatial 2. Spatial distribution distribution of Surfaceof Surface Urban Urban Heat Heat Island Island (SUHI) (SUHI) areas areas in in Hong HongKong. Kong. TheThe SUHISUHI map was derivedmap was based derived on based temperature on temperature ranges ranges and classes and cl ofasses land of surfaceland surface temperature, temperature, in whichin which the the classes of highclasses and of extreme high and high extreme temperatures high temperatures were identified were identified as the SUHIas the SUHI core areas.core areas.

Int. J. Environ. Res. Public Health 2016, 13, 317 7 of 17

3.2. Inequity of SUHI by Socio-Demographic Characteristics

3.2.1. Global Autocorrelation Analysis of SUHI Inequity Table3 displays the frequency of ORs which are greater than 1, by socio-demographic characteristics at the TPU level, where the proportion is the number of TPUs with ORs greater than 1, divided by the total number of TPUs in Hong Kong. From Table3, it can be seen that the age group of 60+ years was more likely to be exposed to SUHI areas in some TPUs. The age group of 60+ years had a higher proportion (64.9%) than age group 0 to 14 (33.3%), where the inequity of age group 0 to 14 was not significant, although the mean OR value is larger than 1. Income groups less than HK$4000 per month, HK$4000 to HK$10,000, HK$10,000 to HK$20,000, HK$20,000 to HK$40,000 were more likely to be exposed to the SUHI core areas, however, income group HK$20,000 to HK$40,000 per month had the highest proportion (66.7%). The groups with educational attainment of secondary and matriculation were more likely to be exposed to the SUHI core areas. Additionally, secondary and matriculation educational level have a higher proportion (64.9%) than pre-primary and primary groups (33.3%). Widowed, divorced and separated people are more likely to be exposed to the SUHI core areas. Widowed, divorced and separated people have a higher proportion (66.7%) than unmarried people (53.3%). All occupational groups including clerks, service workers, shop sales workers, craft and related workers, plant and machine operators, assemblers, and elementary occupations, skilled agricultural and fishery workers, and occupations not classified were also more likely to be exposed to the SUHI core areas. Clerks, service workers, shop sales workers had the highest proportion (63.0%), following by craft and related workers, plant and machine operators, assemblers (60.2%), and elementary occupations, skilled agricultural and fishery workers, and occupations not classified (52.9%).

Table 3. Frequency of Odds Ratios (ORs) greater than 1 by socio-demographic characteristics.

Minimum Maximum Characteristics Level Count (%) 1 Mean (95% CI) (95% CI) <14 11(33.3%) 1.04(1.00,1.08) 1.39(1.20,1.61) 1.12 Age >60 50(64.9%) 1.03(1.00,1.06) 2.58(1.37,4.85) 1.33 <4 K 56(57.7%) 1.07(1.00,1.14) 6.11(2.16,17.28) 1.70 Income (HK$ 4 K–10 K 67(60.9%) 1.09(1.00,1.19) 7.25(4.49,11.70) 2.01 per month) 10 K–20 K 61(61.0%) 1.07(1.00,1.15) 8.61(1.14,65.05) 2.02 20 K–40 K 44(66.7%) 1.11(1.02,1.21) 7.13(2.48,20.51) 1.75 Educational Pre-primary Primary 61(47.3%) 1.05(1.02,1.08) 7.68(5.89,10.01) 1.89 attainment Secondary Sixth form 56(60.9%) 1.03(1.00,1.06) 3.75(2.55,5.50) 1.36 Unmarried 8(53.3%) 1.07(1.02,1.12) 1.20(1.11,1.30) 1.13 Marital status Widowed Divorced 28(66.7%) 1.07(1.01,1.13) 1.95(1.60,2.38) 1.34 Separated Clerks, Service Workers, 51(63.0%) 1.04(1.00,1.07) 4.26(3.06,5.93) 1.47 Shop Sales Workers Craft and Related Workers, Plant and 53(60.2%) 1.06(1.02,1.10) 8.84(5.75,13.59) 1.82 Machine Operators, Occupation Assemblers Elementary Occupations, Skilled Agricultural and Fishery Workers, 46(52.9%) 1.04(1.00,1.07) 1.95(1.78,2.13) 1.24 Occupations not classified 1 The proportion is the number of TPUs with ORs greater than 1, divided by the total number of TPUs in Hong Kong. Int. J. Environ. Res. Public Health 2016, 13, 317 8 of 17

Table4 shows the values calculated by Global autocorrelation for Hong Kong. From the Univariate Moran’s I index, pre-primary and primary educational attainment groups showed statistically significantInt. J. Environ. clusters, Res. Public and Health had 2016 the, 13 largest, 317 positive index values (0.958). All groups showed significant8 of 19 spatial cluster patterns, except for the age groups of 60+ years, unmarried people, and craft and related Table 4 shows the values calculated by Global autocorrelation for Hong Kong. From the workers,Univariate plant andMoran’s machine I index, operators. pre-primary Assemblers and primary were not educational statistically attainment significant. groups showed statistically significant clusters, and had the largest positive index values (0.958). All groups showed Tablesignificant 4. Univariate spatial cluster Moran’s patterns, I statistics except for by the age, age income, groups of educational 60+ years, unmarried attainment, people, marital and status, craft andand occupation.related workers, plant and machine operators. Assemblers were not statistically significant.

TableCharacteristics 4. Univariate Moran’s I statistics by Level age, income, educational attainment, Moran’s I marital z-Value status, and occupation. <14 0.226 9.596 Age >60 0.176 7.492 Characteristics Level Moran’s I z-Value <14 <4 K0.226 0.393 16.1389.596 Age Income (HK$ >60 4 K–10 K0.176 0.347 14.7087.492 per month) <4K10 K–20 K0.393 0.565 24.11816.138 Income (HK$ per 4K–10K20 K–40 K0.347 0.455 18.43514.708 month) 10K–20K 0.565 24.118 Educational Pre-primary Primary 0.801 34.565 20K–40K 0.455 18.435 attainment Secondary Sixth form 0.360 14.471 Educational Pre-primary Primary 0.801 34.565 Unmarried 0.139 6.088 attainmentMarital status Secondary Sixth form 0.360 14.471 WidowedUnmarried Divorced Separated0.139 0.101 4.3606.088 Marital status Clerks,Widowed Service Divorced Workers, Separated Shop Sales Workers 0.101 0.490 20.549 4.360 Clerks, Service Workers, Shop Sales Workers 0.490 20.549 Craft and RelatedCraft andWorkers, Related Plant Workers, and Occupation 0.276 0.276 12.30312.303 PlantMachine and Operators, Machine Assemblers Operators, Assemblers Occupation ElementaryElementary Occupations,Occupations, Skilled Skilled Agricultural 0.260 10.955 andAgricultural Fishery Workers, and Fishery Occupations Workers, not classified0.260 10.955 Occupations not classified

3.2.2.3.2.2. Spatial Spatial Clustering Clustering of SUHIof SUHI Inequity Inequity by by AgeAge FigureFigure3 shows 3 shows the the inequity inequity of of SUHI SUHI exposure exposure byby ageage groups. groups. High-risk High-risk areas areas for forage age groups groups of of less thanless than 14 years 14 years were were located located in in Central Central District, District, Admiralty, Queen’s Queen’s Road Road East, East, Mid-level, Mid-level, Sau Mau Sau Mau Ping,Ping, Lam Lam tin, Shektin, Shek Po Po Tsuen, Tsuen, Chiu Chiu Keng Keng Wan Wan Shan,Shan, Ng Kwai Kwai Shan, Shan, Hang Hau Junk Junk Bay. Bay. High-risk High-risk areasareas for age for groupsage groups of moreof more than than 60 60 years years were were locatedlocated in in Cheung Cheung Lin Lin Shan, Shan, Hung Hung Hom, Hom, Hong Hong Kong Kong Coliseum,Coliseum, The The Arch, Arch, Tai Tai Kok Kok Tsui,Tsui, So So Uk, Uk, Lam Lam tin, tin, Yau YauTong, Tong, Lo Wai, Lo Sha Wai, Tau Sha Kok, Tau Tai Kok,Po Tsai, Tai Chiu Po Tsai, Keng Wan Shan, Ng Kwai Shan. Chiu Keng Wan Shan, Ng Kwai Shan.

FigureFigure 3.3. Cont.

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FigureFigure 3. Spatial 3. Spatial cluster cluster residing residing in in SUHI SUHI corecore areaarea by age age at at TPU TPU level. level. (a) (agea) age < 14; < ( 14;b) age (b) > age 60. > 60. A localA autocorrelationlocal autocorrelation method method was was used used to identify to identi statisticallyfy statistically significant significant hot hot spots. spots. High-High High-Highareas indicateareas high indicate OR valueshigh OR near values high near OR high values; OR valu Low-Lowes; Low-Low areas areas indicate indicate low low OR OR values values near low low OR values;OR High-Low values; High-Low areas indicate areas indicate high OR high values OR valu neares low near OR low values; OR values; Low-High Low-High areas areas indicate indicate low OR valueslow near OR highvalues OR near values. high OR values.

3.2.3.3.2.3. Spatial Spatial Clustering Clustering of SUHIof SUHI Inequity Inequity by by IncomeIncome Figure 4 shows the inequity of SUHI exposure by income. High-risk areas for incomes less than Figure4 shows the inequity of SUHI exposure by income. High-risk areas for incomes less than HK$4000 per month were located in Mid-level, Pok Fu Lam, Shek O, Mong Kok, Yau Ma Tei, Tai Kok HK$4000Tsui, perNgong month Shuen were Chau, located Lo Wai, in Mid-level,Tsuen Wan, Pok Tai FuPo Lam,Tsai, Clear Shek Water O, Mong Bay, Kok, Siu Chik Yau MaSha,Tei, Tai Kok Tsui,Islands. Ngong High-risk Shuen Chau, areas Lofor Wai,incomes Tsuen between Wan, HK Tai$4000 Po Tsai,and HK$10,000 Clear Water per Bay,month Siu were Chik located Sha, in .Mid-level, High-risk Wah areasFu, Aberdeen, for incomes The Peak, between Mong HK$4000 Kok, The and Arch, HK$10,000 Yau Ma Tei, per Tai month Kok Tsui, were Ngong located in Mid-level,Shuen WahChau, Fu, Sheung Aberdeen, Shui, Tai The Po Peak, Tsai, MongClear Water Kok, Bay, The Siu Arch, Chik Yau Sha, Ma Po Tei, Toi TaiIslands. Kok High-risk Tsui, Ngong areas Shuen Chau,for Sheung incomes Shui, between Tai PoHK$10,000 Tsai, Clear and HK$20,000 Water Bay, per Siu month Chik were Sha, located Po Toi in Islands. Mid-level, High-risk Wah Fu, areas Tai for incomesKok betweenTsui, Sha HK$10,000Tau Kok, Tai and Po HK$20,000Tsai, Clear Water per month Bay, Tu wereng Lung located Chau, in Po Mid-level, Toi Islands. Wah High-risk Fu, Tai Kok Tsui,areas Sha Taufor incomes Kok, Tai between Po Tsai, HK$20,000 Clear Water and HK$40,000 Bay, Tung per Lung month Chau, were Po located Toi Islands. in Mid-level, High-risk Pok Fu areas Lam, Sheung Shui, Tai Po Tsai, , Siu Chik Sha, Po Toi Islands. for incomes between HK$20,000 and HK$40,000 per month were located in Mid-level, Pok Fu Lam, SheungInt. J. Environ. Shui, TaiRes. Public Po Tsai, Health Clear 2016, Water13, 317 Bay, Siu Chik Sha, Po Toi Islands. 10 of 19

Figure 4. Cont.

Figure 4. Spatial cluster residing in SUHI core area by income at TPU level. (a) income < $4000; (b) income from $4000 to $10,000; (c) income from $10,000 to $20,000; (d) income from $20,000 to $40,000.

3.2.4. Spatial Clustering of SUHI Inequity by Marital Status Figure 5 shows the inequity of SUHI exposure by marital status. High-risk areas for unmarried groups were located in East Tsim Sha Tsui, Tsim Sha Tsui Ferry Pier, Mongkok South, Tai Kok Tsui, Tai Po Tsai. High-risk areas for widowed, divorced and separated groups were located in Mid-level, Cheung Lin Shan, The Arch, Yau Ma Tei, Tai Kok Tsui, Tai Po Tsai.

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FigureFigure 4. 4.Spatial Spatial cluster cluster residing residing inSUHI in SUHI core core area byarea income by income at TPU at level. TPU (alevel.) income (a) income < $4000; < ( b$4000;) income Figure 4. Spatial cluster residing in SUHI core area by income at TPU level. (a) income < $4000; from(b) income $4000 to from $10,000; $4000 (toc) $10,000; income ( fromc) income $10,000 from to $10,000 $20,000; to ($20,000;d) income (d) income from $20,000 from $20,000 to $40,000. to $40,000. (b) income from $4000 to $10,000; (c) income from $10,000 to $20,000; (d) income from $20,000 to $40,000. 3.2.4. Spatial Clustering of SUHI Inequity by Marital Status 3.2.4.3.2.4. Spatial Spatial Clustering Clustering of of SUHI SUHI InequityInequity byby Marital Status Status Figure 5 shows the inequity of SUHI exposure by marital status. High-risk areas for unmarried FigureFigure5 shows 5 shows the the inequity inequity of of SUHI SUHI exposure exposure byby maritalmarital status. High-risk High-risk areas areas for for unmarried unmarried groups were located in East Tsim Sha Tsui, Tsim Sha Tsui Ferry Pier, Mongkok South, Tai Kok Tsui, groupsgroups were were located located in in East East TsimTsim ShaSha Tsui, Tsim Sha Sha Tsui Tsui Ferry Ferry Pier, Pier, Mongkok Mongkok South, South, Tai Tai Kok Kok Tsui, Tsui, Tai Po Tsai. High-risk areas for widowed, divorced and separated groups were located in Mid-level, Tai Po Tsai. High-risk areas for widowed, divorced and separated groups were located in Mid-level, TaiCheung Po Tsai. Lin High-risk Shan, The areas Arch, for Yau widowed, Ma Tei, Tai divorced Kok Tsui, and Tai separated Po Tsai. groups were located in Mid-level, CheungCheung Lin Lin Shan, Shan, The The Arch, Arch, Yau Yau Ma Ma Tei, Tei, Tai Tai Kok Kok Tsui,Tsui, TaiTai PoPo Tsai.Tsai.

Figure 5. Cont.

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Figure 5. Cont. Int. J. Environ.Int. J. Environ. Res. Res. Public Public Health Health2016 2016, 13, 13, 317, 317 11 of 19 11 of 17

Figure 5. Cont.

FigureFigure 5. SpatialSpatial 5. Spatial cluster cluster cluster residing residing residing in in in SUHI SUHI core core area byby marital marital status status at atTPU TPU level. level. (a) unmarried;( (aa)) unmarried; unmarried; (b) widowed(b) widowed and and divorced divorced and and separated.separated. separated.

3.2.5.3.2.5. Spatial Spatial Clustering Clustering of ofSUHI SUHI Inequity Inequity by by OccupationOccupation 3.2.5. Spatial Clustering of SUHI Inequity by Occupation FigureFigure 6 shows 6 shows the the inequity inequity of of SUHI SUHI exposure exposure byby occupation.occupation. High-risk High-risk areas areas for clerks,for clerks, service service Figure6 shows the inequity of SUHI exposure by occupation. High-risk areas for clerks, service workers,workers, shop shop sales sales workers workers were were located located in in Mid-level,Mid-level, Pok Pok Fu Fu Lam, Lam, The The Peak, Peak, Shek Shek O, Tai O, KokTai KokTsui, Tsui, workers,Ngong shop Shuen sales Chau, workers Container were Terminal, located inKwai Mid-level, Shing Estate, Pok Ha Fu Kwai Lam, Chung, The Peak, Tsing Shek Yi, Kei O, TaiLing Kok Ha Tsui, Ngong Shuen Chau, Container Terminal, Kwai Shing Estate, Ha Kwai Chung, , Kei Ling Ha NgongHoi, Shuen Tai Po Chau, Tsai, Clear Container Water Bay, Terminal, Tung Lung Kwai Chau Shing, Hang Estate, Hau. Ha High-risk Kwai Chung, areas for Tsing craft and Yi, Kei related Ling Ha Hoi, Tai Po Tsai, Clear Water Bay, , Hang Hau. High-risk areas for craft and related Hoi,workers, Tai Po Tsai, plant Clear and machine Water Bay, operators, Tung Lungand assemble Chau,r Hang occupation Hau. groups High-risk were areas located for in craft Hong and Kong related workers, plant and machine operators, and assembler occupation groups were located in Hong Kong workers,West, plant Mid-level, and machine Pok Fu Lam, operators, The Peak, and The assembler Arch, Tai occupationKok Tsui, Ngong groups Shuen were Chau, located Tsing in Yi, Hong Shek Kong West, Mid-level, Pok Fu Lam, The Peak, The Arch, Tai Kok Tsui, Ngong Shuen Chau, Tsing Yi, Shek West,Wan, Mid-level, Kei Ling Pok Ha FuHoi, Lam, Ma On The Shan, Peak, Tai The Po Arch,Tsai, Clear Tai Kok Water Tsui, Bay, Ngong Chiu Keng Shuen Wan Chau, Shan, Tsing , Yi, Shek Wan,Hang Kei LingHau. High-riskHa Hoi, Maareas On for Shan, elementary Tai Po occupation Tsai, Clears, skilled Water agricultural Bay, Chiu and Keng fishery Wan workers, Shan, andPo Lam, Wan, Kei Ling Ha Hoi, Ma On Shan, Tai Po Tsai, Clear Water Bay, Chiu Keng Wan Shan, Po Lam, Hangoccupations Hau. High-risk not classified areas for were elementary located in occupation Hong Kongs, West,skilled Chai agricultural Wan Au, andTsui fisheryLok Estate, workers, Chai and Hang Hau. High-risk areas for elementary occupations, skilled agricultural and fishery workers, and occupationsWan, Pok not Fu Lam,classified The Peak, were Shek located O, The in Arch, Hong Yau Kong Ma Tei, West, Tai KokChai Tsui, Wan Lai Au, chi Kok,Tsui Shek Lok KipEstate, Mei, Chai occupations not classified were located in Hong Kong West, Chai Wan Au, Tsui Lok Estate, Chai Wan, Wan,Ngong Pok Fu Shuen Lam, Chau, The Peak, Lam tin, Shek Tai O, Mo The Shan, Arch, Containe Yau Mar Terminal, Tei, Tai Lo Kok Wai, Tsui, Tso KungLai chi Tam, Kok, Tsuen Shek Wan, Kip Mei, Pok Fu Lam, The Peak, Shek O, The Arch, Yau Ma Tei, Tai Kok Tsui, Lai chi Kok, Shek Kip Mei, Ngong NgongSheung Shuen Fa Chau, Shan, Tsing Lam Yi,tin, Shek Tai MoWan, Shan, Tai Lang Containe Shui, Keir Terminal, Ling Ha Hoi, Lo Wai,Ma On Tso Shan, Kung Tai Tam, Po Tsai, Tsuen Chiu Wan, ShuenKeng Chau, Wan Lam Shan, tin, Ng Tai Kwai Mo Shan, Shan, Po Container Lam, Hang Terminal, Hau, Junk Lo Bay. Wai, Tso Kung Tam, Tsuen Wan, Sheung Sheung Fa Shan, Tsing Yi, Shek Wan, Tai Lang Shui, Kei Ling Ha Hoi, Ma On Shan, Tai Po Tsai, Chiu Fa Shan, Tsing Yi, Shek Wan, Tai Lang Shui, Kei Ling Ha Hoi, Ma On Shan, Tai Po Tsai, Chiu Keng Keng Wan Shan, Ng Kwai Shan, Po Lam, Hang Hau, Junk Bay. Wan Shan, Ng Kwai Shan, Po Lam, Hang Hau, Junk Bay.

Figure 6. Cont.

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Int. J. Environ. Res. Public Health 2016, 13, 317 12 of 17 Figure 6. Cont.

Figure 6. Spatial cluster residing in SUHI core area by occupation at TPU level. (a) clerks, service Figure 6. Spatial cluster residing in SUHI core area by occupation at TPU level. (a) clerks, service workers, shop sales workers; (b) craft and related workers, plant and machine operators, assemblers; workers, shop sales workers; (b) craft and related workers, plant and machine operators, assemblers; (c) elementary occupations, skilled agricultural and fishery workers, occupations not classified. (c) elementary occupations, skilled agricultural and fishery workers, occupations not classified.

3.2.6. Spatial Clustering of SUHI Inequity by Educational Attainment Figure 77 showsshows thethe inequityinequity ofof SUHISUHI exposureexposure byby educationaleducational attainment.attainment. High-riskHigh-risk areasareas forfor pre-primary and primary groups groups were were located located in in Ba Basaltsalt Island, Island, Town Town Island Island,, Clear Clear Water Water Bay, Soko Island. High-risk High-risk areas areas for for secondary secondary and and matriculation matriculation groups groups were were located located in in Queen’s Queen’s Road Road East, East, Wan Chai, Mid-level, Pok Fu Lam, The Peak, Hung Hom, Hom, East East Tsim Tsim Sha Sha Tsui, Tsui, Hong Kong Kong Coliseum, Coliseum, The Arch, Yau Ma Tei, Tai KokKok Tsui,Tsui, LaiLai chichi Kok,Kok, Ngong Shuen Chau, Lam tin, Container Container Terminal, Terminal, Tso KungKung Tam,Tam, Tsuen Tsuen Wan, Wan, Kwai Kwai Shing Shing Estate, Estate, Ha Ha Kwai Kwai Chung, Chung, Tsing Tsing Yi, Kei Yi, Ling Kei HaLing Hoi, Ha Clear Hoi, WaterClear WaterBay, Chiu Bay, Keng Chiu Wan Keng Shan, Wan Ng Shan, Kwai Ng Shan, Kwai Po Shan, Lam, Po Hang Lam, Hau, Hang Junk Hau, Bay. Junk Bay.

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Figure 7. Spatial cluster residing in SUHI core area by educational attainment at TPU level. Figure 7. Spatial cluster residing in SUHI core area by educational attainment at TPU level. (a) pre-primary and primary; (b) secondary and matriculations. (a) pre-primary and primary; (b) secondary and matriculations.

4.4. Discussion Discussion ThisThis study study investigated investigated whether whether socio-demographic socio-demographic characteristics of of a a TPU TPU are are indicative indicative of ofthe the exposureexposure to to excessive excessive heat heat combining combining thermal thermal satellitesatellite image and and census census data. data. The The urban urban heat heat island island corecore areas areas co-occurred co-occurred at at the the TPU TPU scale scale with with lowlow socio-demographicsocio-demographic characteristics characteristics as as measured measured by by vulnerablevulnerable age, age, secondary secondary and and matriculation matriculation education,education, widowed, widowed, divorced divorced and and separated separated people. people. AllAll income income groups groups of of less less than than $40,000 $40,000 andand occupationoccupation groups including including clerks clerks,, service service workers, workers, shop shop salessales workers, workers, craft craft and and related related workers, workers, plantplant andand machinemachine operators, assemblers, assemblers, and and elementary elementary occupations,occupations, skilled skilled agricultural agricultural and and fishery fishery workers,workers, and occupations not not classified classified showed showed similar similar exposureexposure to to SUHI SUHI core core areas. areas. Chan Chanet et al. al.[ [16]16] statedstated that low low socio-economic socio-economic status status groups groups were were more more sensitivesensitive than than other other groups groups to to high high temperature temperature effectseffects in Hong Kong Kong an andd other other previous previous studies studies investigated how many socially vulnerable hotspot areas were located in the LST hotspot areas. investigated how many socially vulnerable hotspot areas were located in the LST hotspot areas. However, our study highlighted the spatial clusters of SUHI inequity by age, income, educational However, our study highlighted the spatial clusters of SUHI inequity by age, income, educational attainment, marital status, and occupation within the TPU spatial units. Our study extended the attainment, marital status, and occupation within the TPU spatial units. Our study extended the findings from previous research by incorporating the spatial perspective of these inequities. findings from previous research by incorporating the spatial perspective of these inequities.

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Although this study has revealed the spatial cluster of some disadvantaged groups and some socially vulnerable hotspots areas in Hong Kong, some limitations of both data and techniques persist as follows: (i) this study used the aggregate census data in TPU spatial units. However, data of individual-level information such as individual activities due to work, recreation and living are not available. Compared to previous studies using census units (e.g., census tract, block group), this study used finer geographical units with the aid of PDMM, which considers land use type for population distribution. The appropriate resolution/unit with respect to the areal coverage and data aggregation issue in the analysis of spatial inequity will be further studied in future research; (ii) according to the results of spatial autocorrelation, some vulnerable areas are in remote areas, and this might be possibly attributed to a combination between diversities of land use types resulting in spatial discrepancy between the SUHI core areas (i.e., SUHI core areas and non-core areas present in a TPU) and the differences in population distribution. Although it seems that the proportions of mean population in the SUHI core areas versus non-SUHI core areas in target groups (e.g., age groups of less than 14 years) are greater than the proportions of mean population in SUHI core areas versus non-SUHI core areas in reference group (e.g., ages between 14 and 60 years old). A testing of causal hypotheses through analyzing different factors caused by SUHI and population distribution may explain these abnormal distributions; (iii) it is difficult to eliminate the potential bias of the logistic regression modelling for each type of demographic variable (e.g., age) by inputting the remaining variables (e.g., education attainment, income) as confounding factors, since the attribute values for those variables are aggregated values rather than at the individual level; (iv) due to data availability, the LST data used in this study were derived from a daytime image. The nighttime temperature difference between urban and rural areas will usually be more significant than the daytime, but a nighttime image covering the entire area of Hong Kong was not available. More detailed study of using a time-series of Landsat and HJ-1A/1B thermal satellite images and years of population and census data to analyze a time-series of spatial inequality will be conducted in future research. In addition, LST data will be studied and used to derive air temperature image covering the territories. More comprehensive datasets including urban morphological and dynamic traffic datasets will be coupled in the future study. It should be noted that this study is fairly novel in the methodology employed (e.g., spatial autocorrelation) for investigating environmental and socio-demographic inequities (geographic unit, PDMM, statistical analysis methods, definition of social deprivation). Therefore, the results provided in this study would be highly applicable in other study disciplines and areas.

5. Conclusions In conclusion, a spatial autocorrelation method was used to analyze the relationships between socio-demographic variables and spatial patterns of SUHI exposure. Based on an LST image from 2005, the study revealed that the populations likely to be exposed to intense SUHI areas are comprised largely of disadvantaged socio-demographic groups, including age groups of 60+ years, secondary and matriculation educational level, widowed, divorced and separated people, low and middle income groups, and occupation groups of workers in Hong Kong. This study also indicated spatial patterns of inequity hotspots in Hong Kong. The results of this study and methods used herein may provide valuable decision-making information to government, planning authorities, and various other stakeholders, who can respond to mitigate potential inequities for disadvantaged communities, as well as to prioritize for heat prevention and intervention works. The study can also be extended for future city planning, such as tree-planting, to help reduce heat exposure and mitigate the urban heat island effect. Policy-makers can also adapt the urban infrastructure and community services to assist vulnerable groups in SUHI exposure. Int. J. Environ. Res. Public Health 2016, 13, 317 15 of 17

Acknowledgments: This work was supported in part by the grant HNGQJC2015-03 from National Geographic Conditions Monitoring of Hunan Province, the grant of Early Career Scheme (project id: 25201614), the grant HKU9/CRF/12G of Collaborative Research Fund from the Research Grants Council of Hong Kong; the grant G-YM85 from the Hong Kong Polytechnic University; and the project “National Geographical State Monitoring of Urban Group, National Key Technology R&D Program (2012BAJ15B00)”, sponsored by the Ministry of Science and Technology of the People’s Republic of China. The authors thanks the Hong Kong Census and Statistics Department for the census data, the Hong Kong Planning Department for the Tertiary Planning Units data, the Hong Kong Observatory for the weather and climate data, and NASA LP DAAC for the Landsat satellite imagery. Author Contributions: Man Sing Wong and Bin Zou conceived the design, contributed to manuscript revision. Fen Peng performed the study and drafted the manuscript. Wen Zhong Shi and Gaines J. Wilson contributed to manuscript revision. All authors read and approved the final manuscript. Conflicts of Interest: The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

SUHI Urban Heat Island TM Thematic Mapper LST Land Surface Temperature PDMM Population Dynamic Mapping Model TPU Tertiary Planning Units ORs Odds Ratios OLS Ordinary Least Squares Models GIS Geographical Information System LULC land use and land cover HKO Hong Kong Observatory HKSAR Hong Kong Special Administrative Region

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