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Previous housing price studies based on hedonic price modeling have mainly focused on applying various factors, including built environment variables in the analysis, without establishing a comprehensive theoretical framework as a basis for the model formulation. To address this gap, this study introduces a more systematic framework for decomposing housing prices into land prices as determined by built form, neighborhood socioeconomic characteristics and individual dwellings' physical conditions. Following this logic, this study experiments with the related variables through regression analysis, including consideration of spatial lags, as well as develops a housing price model using a random forests (RF) algorithm. A comprehensive time-series database of housing transaction data for the City of Toronto is used. Modeling results show that neighborhood socioeconomic factors contribute the most to the explanation of housing prices, while housing characteristics and accessibility measures are also significantly influential. The RF model achieves an overall accuracy of 85%, a relatively good performance in reproducing observed prices. The findings provide insights for planners concerning factors influencing housing prices and, hence, residential location decision-making. [ABSTRACT FROM AUTHOR]

The Yellow River Basin (YRB) is an important ecological barrier and an important economic zone in China. Under the new requirements of realizing ecological protection and high-quality development of the YRB, the coordinated development of basin economic development and ecological environment is an urgent research topic. Taking 36 cities along the Yellow River Basin as samples, this paper constructs an evaluation index system of economic development and ecological environment. The coupling coordination model and geographical weighted regression were adopted to analyze the coupling coordination relationship between economic development and ecological environment from 2008 to 2017, and the influencing factors were analyzed. The results showed that (1) the coupling coordination of urban economic development and ecological environment along the YRB showed significant regional heterogeneity, forming a low-value sag of the Loess Plateau; (2) the regional hot spots in the downstream of the YRB continuously extended inland, while these in the midstream converged; (3) the coupling coordination degree between economic development and ecological environment is affected by factors such as population size, openness, and advanced industrial structure, and the intensity varies significantly among regions. [ABSTRACT FROM AUTHOR]

Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments.

Historical-geographical (chorographic) descriptions provide some of the earliest formal documentation about landscape. We propose a methodological approach aimed at reconstructing a spatial-explicit picture of the agroforestry system of a eighteenth-century landscape, detecting the main land-use drivers, and analysing existing legacies of past agro-forestry productivity in the present landscape. The study area was the Bologna Apennines, and our data source was a chorographic dictionary from 1781–83. We obtained a matrix of 240 administrative units per 18 agro-forestry products with related productivity indices. Multivariate analysis showed that environmental constraints influenced products and productivity. Agricultural areas (and related products) mainly shaped the hillside, while forests and semi-natural areas (and related products) characterized the mountainside. Such former clustering is still recognizable: agricultural land mostly changed to artificial land-cover, whereas semi-natural areas and forests still exist. The proposed approach confirms that chorography can be a useful tool as a primary source in landscape research. [ABSTRACT FROM AUTHOR]

This study analyzed the low irrigation water use efficiency (IWUE) on the Sanjiang Plain. To do so, we established IWUE as the dependent variable and rainfall, evapotranspiration, and ten other factors as independent variables in a stepwise regression model to globally analyze the factors that influence IWUE. Based on this model, we locally analyzed the factors that influence IWUE with a geographical weighted regression (GWR). The results showed that the average temperature and crop yield were not significantly correlated with IWUE; however, rainfall, evapotranspiration, the canal system lining rate, the irrigation facility intact rate, and the irrigation equipment rate were positively correlated with IWUE. Additionally, the paddy field area ratio, canal system complexity, and irrigation water quantity were negatively correlated with IWUE. IWUE and its influencing factors exhibited different degrees of spatial autocorrelation on the Sanjiang Plain, and the spatial heterogeneities of three variables (IWUE, evapotranspiration, and the canal system lining rate) were particularly high. In the local analysis, the spatial distributions of the regression coefficients of each influencing factor illustrated the influence of each factor on the IWUE in different territories. These results provide a scientific basis for improving the IWUE on the Sanjiang Plain. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987-2000, 2000-2010, and 1987-2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran's I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R and corrected Akaike's Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning. [ABSTRACT FROM AUTHOR]

Shallow water bathymetry is critical in understanding and managing marine ecosystems. Bathymetric inversion models using airborne/satellite multispectral data are an efficient way to retrieve shallow bathymetry due to the affordable cost of airborne/satellite images and less field work required. With the increasing availability and popularity of unmanned aerial vehicle (UAV) imagery, this paper explores a new approach to obtain bathymetry using UAV visual-band (RGB) images. A combined approach is therefore proposed for retrieving bathymetry from aerial stereo RGB imagery, which is the combination of a new stereo triangulation method (an improved projection image based two-medium stereo triangulation method) and spectral inversion models. In general, the inversion models require some bathymetry reference points, which are not always feasible in many scenarios, and the proposed approach employs a new stereo triangulation method to obtain reliable bathymetric points, which act as the reference points of the inversion models. Using various numbers of triangulation points as the reference points together with a Geographical Weighted Regression (GWR) model, a series of experiments were conducted using UAV RGB images of a small island, and the results were validated against LiDAR points. The promising results indicate that the proposed approach is an efficient technique for shallow water bathymetry retrieval, and together with UAV platforms, it could be deployed easily to conduct a broad range of applications within marine environments. [ABSTRACT FROM AUTHOR]

Abstract Background Drug-resistant tuberculosis (DR-TB) is a major public health problem caused by various factors. It is essential to systematically investigate the epidemiological and, in particular, the ecological factors of DR-TB for its prevention and control. Studies of the ecological factors can provide information on etiology, and assist in the effective prevention and control of disease. So it is of great significance for public health to explore the ecological factors of DR-TB, which can provide guidance for formulating regional prevention and control strategies. Methods Anti-TB drug resistance data were obtained from the World Health Organization/International Union Against Tuberculosis and Lung Disease (WHO/UNION) Global Project on Anti-Tuberculosis Drug Resistance Surveillance, and data on ecological factors were collected to explore the ecological factors for DR-TB. Partial least square path modeling (PLS-PM), in combination with ordinary least squares (OLS) regression, as well as geographically weighted regression (GWR), were used to build a global and local spatial regression model between the latent synthetic DR-TB factor ("DR-TB") and latent synthetic risk factors. Results OLS regression and PLS-PM indicated a significant globally linear spatial association between "DR-TB" and its latent synthetic risk factors. However, the GWR model showed marked spatial variability across the study regions. The "TB Epidemic", "Health Service" and "DOTS (directly-observed treatment strategy) Effect" factors were all positively related to "DR-TB" in most regions of the world, while "Health Expenditure" and "Temperature" factors were negatively related in most areas of the world, and the "Humidity" factor had a negative influence on "DR-TB" in all regions of the world. Conclusions In summary, the influences of the latent synthetic risk factors on DR-TB presented spatial variability. We should formulate regional DR-TB monitoring planning and prevention and control strategies, based on the spatial characteristics of the latent synthetic risk factors and spatial variability of the local relationship between DR-TB and latent synthetic risk factors.

Regional ecological quality is largely affected by human activities, which restricts the sustainable development of regional economy. Taking the China-Laos railway economic belt as an example, we investigated the effects of human activities on ecological quality. The remote sensing images of 1999, 2009 and 2019 were selected to calculate remote sensing ecological index (RSEI). Spatial autocorrelation statistics and local G statistics were used to explore the spatial-temporal variations of ecological quality in the study area. Combined with the population density in the same period, a geographically weighted regression model was constructed to quantitatively analyze the ecological effects of different human activity intensities in the study area. The results showed that the ecological quality in the study area presented a trend of increasing firstly and then decreasing later from 1999 to 2019, and that the mean value of RSEI varied from 0.645 (1999) to 0.738 (2009) and then decreasing to 0.721 in 2019. Specially, the ecological quality fluctuated more apparently in the midlands. The results of fitting population density and ecological quality based on geographical weighted regression model (GWR) were significantly better than that of least square method. R 2 of different periods based on GWR was higher than 0.7 and the fitting effect was stable. The fitting degree of GWR in 2019 was the best ( R 2 was 0.785), and R 2 in 1999 and 2009 were 0.726 and 0.754, respectively. The ecological quality along the China-Laos railway south area (such as Vientiane) was more sensitive to human activities, with most of these places belonged to moderately sensitive regions. For the highly, moderately and lowly ecological sensitive regions, every 10, 100, 1000-fold increases in population density would lead to a decrease of 0.2, 0.4 and 0.6 to the mean value of RSEI in turn. The development of economic belt would increase population density. During the planning and layout of economic belt, human activities should be controlled to avoid the deterioration of ecological quality in the potential and current sensitive regions.