Social Determinants of Diabetes-Related Preventable Hospitalization in Taiwan: A Spatial Analysis.

Diabetes-Related Preventable Hospitalization (DRPH) has been identified as an important indicator of efficiency and quality of the health system and can be modified by social determinants. However, the spatial disparities, clustering, and relationships between DRPH and social determinants have rarely been investigated. Accordingly, this study examined the association of DRPH with area deprivation, densities of certificated diabetes health-promoting clinics (DHPC) and hospitals (DHPH), and the presence of elderly social services (ESS) using both statistical and spatial analyses. Data were obtained from the 2010-2016 National Health Insurance Research Database (NHIRD) and government open data. Township-level ordinary least squares (OSL) and geographically weighted regression (GWR) were conducted. DRPH rates were found to be negatively associated with densities of DHPC (ß = -66.36, p = 0.029; 40.3% of all townships) and ESS (ß = -1.85, p = 0.027; 28.4% of all townships) but positively associated with area deprivation (ß = 2.96, p = 0.002; 25.6% of all townships) in both OLS and GWR models. Significant relationships were found in varying areas in the GWR model. DRPH rates are high in townships of Taiwan that have lower DHPC densities, lower ESS densities, and greater socioeconomic deprivation. Spatial analysis could identify areas of concern for potential intervention.

Relationship between landslides and mountain development-Integrating geospatial statistics and a new long-term database.

Development in mountainous areas is inevitable in countries with high population densities, but the actual relationship between development and landslides remains uncertain. Clarifying the key current or historical factors resulting in landslides is crucial for hazard prevention and mitigation. This study focused on the Shihmen Reservoir catchment in Taiwan. Two combinations of explanatory variables in five different years (1946, 1971, 2001, 2004, and 2012) collected from a geodatabase and digital archives were used to conduct proximity and discrete logistic regression analyses. The results demonstrate that landslides increased dramatically from 1946 to 2012 in the catchment area. The proximity and overlapping of human development with landslides increased. However, the logistic regression results indicated that variation in susceptibility to landslides was due to natural causes, with the exception of historical deforestation and newly constructed road systems. Therefore, well-recovered historical woodland sites might currently be landslide-prone areas. We suggest that cumulative historical events should be considered as explanatory variables in future landslide prediction analysis.

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

A Comparative Analysis of Machine Learning with WorldView-2 Pan-Sharpened Imagery for Tea Crop Mapping.

Tea is an important but vulnerable economic crop in East Asia, highly impacted by climate change. This study attempts to interpret tea land use/land cover (LULC) using very high resolution WorldView-2 imagery of central Taiwan with both pixel and object-based approaches. A total of 80 variables derived from each WorldView-2 band with pan-sharpening, standardization, principal components and gray level co-occurrence matrix (GLCM) texture indices transformation, were set as the input variables. For pixel-based image analysis (PBIA), 34 variables were selected, including seven principal components, 21 GLCM texture indices and six original WorldView-2 bands. Results showed that support vector machine (SVM) had the highest tea crop classification accuracy (OA = 84.70% and KIA = 0.690), followed by random forest (RF), maximum likelihood algorithm (ML), and logistic regression analysis (LR). However, the ML classifier achieved the highest classification accuracy (OA = 96.04% and KIA = 0.887) in object-based image analysis (OBIA) using only six variables. The contribution of this study is to create a new framework for accurately identifying tea crops in a subtropical region with real-time high-resolution WorldView-2 imagery without field survey, which could further aid agriculture land management and a sustainable agricultural product supply.