Selecting suitable sites for mountain ginseng(Panax ginseng)cultivation by using geographically weighted logistic regression

With the well-being trends to pursue a healthy life, mountain ginseng(Panax ginseng) is rising as one of the most profitable forest products in South Korea. This study was aimed at evaluating a new methodology for identifying suitable sites for mountain ginseng cultivation in the country. Forest vegetation data were collected from 46 sites and the spatial distribution of all sites was analyzed using GIS data for topographic position, landform, solar radiation, and topographic wetness. The physical and chemical properties of the soil samples, including moisture content, p H, organic matter, total nitrogen, exchangeable cations, available phosphorous, and soil texture, were analyzed. The cultivation suitability at each site was assessed based on the environmental conditions using logistic regression(LR) and geographically weighted logistic regression(GWLR) and the results of both methods were compared. The results show that the areas with northern aspect and higher levels of solar radiation, moisture content, total nitrogen, and sand ratio are more likely to be identified as suitable sites for ginseng cultivation. In contrast to the LR, the spatial modeling with the GWLR results in an increase in the model fitness and indicates that a significant portion of spatialautocorrelation in the data decreases. A higher value of the area under the receiver operating characteristic(ROC) curve presents a better prediction accuracy of site suitability by the GWLR. The geographically weighted coefficient estimates of the model are nonstationary, and reveal that different site suitability is associated with the geographical location of the forest stands. The GWLR increases the accuracy of selecting suitable sites by considering the geographical variations in the characteristics of the cultivation sites.

Urban Expansion and Spatiotemporal Relationships with Driving Factors Revealed by Geographically Weighted Logistic Regression

Urbanization improves our lives but also threatens human health and sustainable development. Revealing the spatiotemporal pattern of urban expansion and spatiotemporal relationships with driving forces, especially in terms of the ubiquitous and fast growing small city, is a crucial prerequisite to solving these problems and realizing sustainable development. Kunshan, China was used as a case study here. Eleven variables from four aspects covering physical, socioeconomic, accessibility and neighborhood were selected, and logistic regression and geographically weighted logistic regression modeling were employed to explore spatiotemporal relationships from 1991-2014. Results reveal that urban expansion in Kunshan shows an accelerating tendency with annual expansion from 2000-2014 four times higher than for 1991-2000. More importantly, the annual expansion rate of Kunshan of 28.42% in 2000-2014 is higher than that of a large city. Urban expansion and related factors have spatiotemporal varying relationships. From a global perspective, the closer to a city, town, main road and the higher the GDP, the more likely a region will undergo urbanization. Interestingly, the effect of population on urban expansion is decreasing, especially in developed areas, and the effect of distance to lake is enhanced. From a local perspective, the magnitude and even the sign of the coefficients vary across the study area. However, the range of the coefficient of GWLR is around that of the corresponding variable in LR, and the sign of most variables in GWLR is consistent with that of corresponding variables in LR. GWLR surpasses LR with the same explanatory variables in revealing regional differences and improving model reliability. Based on these findings, more attention should be given to small cities in China. Promoting the connotation of city culture and public services to realize New-type Urbanization and regional diversity policy in order to manage urban expansion scientifically are also recommended.

Exploring the drivers of urban expansion in a medium-class urban agglomeration in India using the remote sensing techniques and geographically weighted models

Rapid urbanization urges the immediate attention of policymakers to ensure sustainable city development.Under-standing the urban growth drivers is essential to address effective strategies for urbanization-related challenges.This work aims to study Raiganj’s urban development and the factors associated with this expansion.This study employed global logistic regression(LR)and geographical weighted logistic regression(GWLR)to explore the role of different factors.The results showed that the role of the central business district(covariate>-1),commercial market(covariate>-3),and police station(covariate>-4)were significant to the development of new built-up areas.In the second period,major roads(covariate>-2)and new infrastructures(covariate>-4)became more relevant,particularly in the eastern and southern areas.GWLR was more accurate in assessing the different fac-tors’impact than LR.The results obtained are essential to understanding urban expansion in India’s medium-class cities,which is critical to effective policies for sustainable urbanization.

Geographical variations in risk factors associated with HIV infection among drug users in a prefecture in Southwest China

Background:Previous studies have shown inconsistent or even contradictory results for some risk factors associated with HIV infection among drug users,and these may be partially explained by geographical variations.Methods:Data were collected from 11 methadone clinics in the Liangshan Yi Autonomous Prefecture from 2004 to 2012.A non-spatial logistical regression model and a geographically weighted logistic regression model were fitted to analyze the association between HIV infection and specific factors at the individual level.Results:This study enrolled 6,458 patients.The prevalence of HIV infection was 25.1%.The non-spatial model indicated that being divorced was positively associated with HIV infection.The spatial model also showed that being divorced was positively associated with HIV infection,but only for 49.4%of individuals residing in some northern counties.The non-spatial model suggested that service sector work was negatively associated with HIV infection.However,the spatial model indicated that service work was associated with HIV infection,but only for 23.0%of patients living in some western counties.The non-spatial model did not show that being married was associated with HIV infection in our study field,but the spatial model indicated that being married was negatively associated with HIV infection for 12.0%of individuals living in some western counties.For other factors,the non-spatial and spatial models showed similar results.Conclusion:The spatial model may be useful for improving understanding of geographical heterogeneity in the relationship between HIV infection and individual factors.Spatial heterogeneity may be useful for tailoring intervention strategies for local regions,which can consequently result in a more efficient allocation of limited resources toward the control of HIV transmission.