By incorporating temporal effects into the geographically weighted regression (GWR) model, an extended GWR model, geographically and temporally weighted regression (GTWR), has been developed to deal with both spatial and temporal nonstationarity simultaneously in real estate market data. Unlike the standard GWR model, GTWR integrates both temporal and spatial information in the weighting matrices to capture spatial and temporal heterogeneity. The GTWR design embodies a local weighting scheme wherein GWR and temporally weighted regression (TWR) become special cases of GTWR. In order to test its improved performance, GTWR was compared with global ordinary least squares, TWR, and GWR in terms of goodness-of-fit and other statistical measures using a case study of residential housing sales in the city of Calgary, Canada, from 2002 to 2004. The results showed that there were substantial benefits in modeling both spatial and temporal nonstationarity simultaneously. In the test sample, the TWR, GWR, and GTWR models, respectively, reduced absolute errors by 3.5%, 31.5%, and 46.4% relative to a global ordinary least squares model. More impressively, the GTWR model demonstrated a better goodness-of-fit (0.9282) than the TWR model (0.7794) and the GWR model (0.8897). McNamara's test supported the hypothesis that the improvements made by GTWR over the TWR and GWR models are statistically significant for the sample data.

Understanding the complexity of urban expansion requires an analysis of the factors influencing the spatial and temporal processes of rural–urban land conversion. This study aims at building a statistical land conversion model to assist in understanding land use change patterns. Specifically, GIS coupled with a logistic regression model and exponential smoothing techniques is used for exploring the effects of various factors on land use change. These factors include population density, slope, proximity to roads, and surrounding land use, and their influence on land use change is studied for generating a predictive model. Methods to reduce spatial autocorrelation in a logistic regression framework are also discussed. Primarily, an optimal sampling scheme that can eliminate spatial autocorrelation while maintaining adequate samples to allow the model to achieve the comparable accuracy as the spatial autoregressive model is developed. Since many of the previous studies on modeling the spatial complexity of urban growth ignored temporal complexity, a modified exponential smoothing technique is employed to produce a smoothed model from a series of bi‐temporal models obtained from different time periods. The proposed model is validated using the multi‐temporal land use data in New Castle County, DE, USA. It is demonstrated that our approach provides an effective option for multi‐temporal land use change modeling and the modeling results help interpret the land use change patterns.

In this research, three hedonic pricing models, including an ordinary least squares (OLS) model, a Euclidean distance–based (ED-based) geographically weighted regression (GWR) model, and a travel time–based GWR model supported by a big data set of millions of smartcard transactions, have been developed to investigate the spatial variation of Housing Development Board (HDB) public housing resale prices in Singapore. The results help identify factors that could significantly affect public housing resale prices, including the age and the floor area of the housing units, the distance to the nearest park, the distance to the central business district (CBD), and the distance to the nearest Mass Rapid Transit (MRT) station. The comparison of the three models also explicitly shows that the two GWR models perform much better than the traditional linear hedonic regression model, given the identical variables and data used in the calibration. Furthermore, the travel time–based GWR model has better model fit compared to the ED-based GWR model in the case study. This study demonstrates the potential value of the big data–based GWR model in housing research. It could also be applied to other research fields such as public health and criminal justice.

This paper attempts to demonstrate that addressing the dictionary perturbations and the individual representation of the coupled images can generally result in positive effects with respect to sparse-representation-based spatiotemporal reflectance fusion (SPTM). We propose to adapt the dictionary perturbations with an error-bound-regularized method and formulate the dictionary perturbations to be a sparse elastic net regression problem. Moreover, we also utilize semi-coupled dictionary learning (SCDL) to address the differences between the high-spatial-resolution and low-spatial-resolution images, and we propose the error-bound-regularized SCDL (EBSCDL) model by also imposing an error bound regularization. Two data sets of Landsat Enhanced Thematic Mapper Plus data and Moderate Resolution Imaging Spectroradiometer acquisitions were used to validate the proposed models. The spatial and temporal adaptive reflectance fusion model and the original SPTM were also implemented and compared. The experimental results consistently show the positive effect of the proposed methods for SPTM, with smaller differences in scatter plot distribution and higher peak-signal-to-noise ratio and structural similarity index measures.

Modeling land-use change is a prerequisite to understanding the complexity of land-use-change patterns. This paper presents a novel method to model urban land-use change using support-vector machines (SVMs), a new generation of machine learning algorithms used in classification and regression domains. An SVM modeling framework has been developed to analyze land-use change in relation to various factors such as population, distance to roads and facilities, and surrounding land use. As land-use data are generally unbalanced, in the sense that the unchanged data overwhelm the changed data, traditional methods are incapable of classifying relatively minor land-use changes with high accuracy. To circumvent this problem, an unbalanced SVM has been adopted by enhancing the standard SVMs. A case study of Calgary land-use change demonstrates that the unbalanced SVMs can achieve high and reliable performance for land-use-change modeling.