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Land Surface Temperature (LST) is a critical component to understand the impact of urbanization on the urban thermal environment. Previous studies were inclined to apply only one snapshot to analyze the pattern and dynamics of LST without considering the non-stationarity in the temporal domain, or focus on the diurnal, seasonal, and annual pattern analysis of LST which has limited support for the understanding of how LST varies with the advancing of urbanization. This paper presents a workflow to extract the spatio-temporal pattern of LST through time series clustering by focusing on the LST of Wuhan, China, from 2002 to 2017 with a 3-year time interval with 8-day MODerate-resolution Imaging Spectroradiometer (MODIS) satellite image products. The Latent pattern of LST (LLST) generated by non-parametric Multi-Task Gaussian Process Modeling (MTGP) and the Multi-Scale Shape Index (MSSI) which characterizes the morphology of LLST are coupled for pattern recognition. Specifically, spatio-temporal patterns are discovered after the extraction of spatial patterns conducted by the incorporation of k-means and the Back-Propagation neural networks (BP-Net). The spatial patterns of the 6 years form a basic understanding about the corresponding temporal variances. For spatio-temporal pattern recognition, LLSTs and MSSIs of the 6 years are regarded as geo-referenced time series. Multiple algorithms including traditional k-means with Euclidean Distance (ED), shape-based k-means with the constrained Dynamic Time Warping (cDTW) distance measure, and the Dynamic Time Warping Barycenter Averaging (DBA) centroid computation method (k-cDBA) and k-shape are applied. Ten external indexes are employed to evaluate the performance of the three algorithms and reveal k-cDBA as the optimal time series clustering algorithm for our study. The study area is divided into 17 geographical time series clusters which respectively illustrate heterogeneous temporal dynamics of LST patterns. The homogeneous geographical clusters correspond to the zoning custom of urban planning and design, and thus, may efficiently bridge the urban and environmental systems in terms of research scope and scale. The proposed workflow can be utilized for other cities and potentially used for comparison among different cities.

Purpose The purpose of this paper is to develop and test a model that investigates volition and self-efficacy as antecedents, and work engagement and job satisfaction as outcomes of perceived employability. It also evaluates the moderating role of job insecurity on the relationships between perceived employability and the two employee outcomes. Design/methodology/approach The data were collected via a random sampling survey on living conditions of Hong Kong citizens in 2014. The final sample consists of 414 Chinese working adults. The authors employ structural equation modeling and moderated regression analysis to test the hypotheses. Findings Results show that volition and self-efficacy are positively related to perceived employability, and perceived employability in turn positively relates to work engagement and job satisfaction. Besides, perceived employability fully mediates the effect of volition and partially mediates the effect of self-efficacy, on the two outcome variables. The authors also find that job insecurity acts as a significant moderator on the relationships between perceived employability and the outcomes. Research limitations/implications Limitations of this study include self-reported data, cross-sectional research design, and selected respondents with a large proportion of recent immigrants. By delineating the process through which perceived employability affects employees’ work engagement and job satisfaction, this study provides some implications for research and practice. Originality/value This study introduces a conceptual model that includes both antecedents and consequences of perceived employability. It examines the relationships among volition, perceived employability, and work engagement, which has not been studied before. By identifying job insecurity as an important moderator, it reveals a boundary condition of perceived employability on employee outcomes.

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There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.