Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. London from 1980London from to 1998 results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19-year set of house price data in

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Marginal land use changes can abruptly result in non-marginal and irreversible changes in ecosystem functioning and the economic values that the ecosystem generates. This challenges the traditional ecosystem services (ESS) mapping approach, which has often made the assumption that ESS can be mapped uniquely to land use and land cover data. Using a functional fragmentation measure, we show how landscape pattern changes might lead to changes in the delivery of ESS. We map changes in ESS of dry calcareous grasslands under different land use change scenarios in a case study region in Switzerland. We selected three ESS known to be related to species diversity including carbon sequestration and pollination as regulating values and recreational experience as cultural value, and compared them to the value of two production services including food and timber production. Results show that the current unceasing fragmentation is particularly critical for the value of ESS provided by species-rich habitats. The article concludes that assessing landscape patterns is key for maintaining valuable ESS in the face of human use and fluctuating environment.

Hedonic house-price models have long been used in urban studies to investigate important factors characterizing cities (eg, the demand for amenities or housing submarkets). Traditionally, the formulation of hedonic models has been solved using global spatial econometric techniques. The development of local regression methods brought new insights into urban planning as the relationships between house prices and their determinants can be estimated locally and therefore mapped across space. Such maps provide planners and policy makers with valuable location-specific information to support their decision-making processes. A feature that is frequently overlooked when performing spatial local analysis is testing the statistical significance of local parameter estimates over space. This can be done by mapping the /-value of parameter estimates (^-surfaces). In this study we propose the use of a mixed geographically weighted regression (mixed-GWR) technique to estimate a hedonic house-price model in Zurich. Mixed-GWR is an extended version of GWR by which some parameters are allowed to vary over space, while others remained fixed. To obtain spatially explicit results in a more meaningful way, we propose the use of i-surfaces to explore the statistical significance of selected local parameter estimates over space. We also follow the Bonferroni correction to overcome the problem of multiple hypothesis testing in local regression modelling. Results reveal interesting patterns in the spatial variability of local estimates for planners. For instance, areas are identified over which public policies such as house taxing have little or no effect on house pricing. Similarly, economic distortions in the housing market can be examined through the variability of residents' willingness to pay for larger dwellings. Also, urban development processes such as densification of cities can be supported by spatially exploring relevant socioeconomic variables.

Hedonic price modelling has long been a powerful tool to estimate house prices in the real estate market. Increasingly, traditional global hedonic price models that largely ignore spatial effects are being superseded by models that deal with spatial dependency and spatial heterogeneity. In addition, many novel methods integrating spatial economics, statistics and geographical information science (GIScience) have been developed recently to incorporate temporal effects into hedonic house price modelling. Here, a local spatial modelling technique, geographically weighted regression (GWR), which accounts for spatial heterogeneity in housing utility functions is applied to a 19-year set of house price data in London (1980London ( -1998 in order to explore spatiotemporal variations in the determinants of house prices. Further, based on the local parameter estimates derived from GWR, a new method integrating GWR and time series (TS) forecasting techniques, GWR-TS, is proposed to predict future local parameters and thus future house prices. The results obtained from GWR demonstrate variations in local parameter estimates over both space and time. The forecasted future values of local estimates as well as house prices indicate that the proposed GWR-TS method is a useful addition to hedonic price modelling.