Application of high-dimensional uniform manifold approximation and projection (UMAP) to cluster existing landfills on the basis of geographical and environmental features.

Due to extreme conditions, which are influenced by the location of landfills, the release of pollutants has been recently proven to be more severe in estuary landfills, as these landfill locations are affected by both sea-water and river-water interactions. To identify geographic and environmental features linked to the extreme conditions of certain landfills, a high-dimensional clustering method combining Uniform Manifold Approximation and Projection (UMAP) with the Louvain algorithm is proposed. A case study was conducted using 17 noteworthy features that transform to Landfill Suitability Index (LSI) applied to hundreds of landfill sites in Taiwan. This study clustered landfills into 10 clusters and identified several clusters with significant extreme locations, including estuary landfills (7.9 %), fault-water-body landfills (8.2 %), and densely-populated-water-body landfills (17.6 %). Furthermore, a critical discovery of endangered Platalea minor habitats near these estuary landfills was made. Additionally, this work identified "healthy" landfills (11.2 %) that are minimally affected by the considered features. These findings demonstrate the promising potential of our framework for managers to systematically improve landfill management strategies. Moreover, our framework was tested by incorporating rainfall and flooding features in relation to climate change scenarios. To address the demand for land release from occupied landfills in Taiwan, there is a pressing need to expedite the transition to a circular economy, and our framework can provide further assistance in this regard. This approach is promising, as it provides a new method to evaluate the environmental risks linked to landfills and also identifies potential opportunities related to landfill mining. Finally, this work was extended to include a case study in England, which has 19,801 landfills and a dataset containing 15 relevant landfill features; in this case study, our framework identified 110 landfill clusters, and several placed in extreme locations, demonstrating that our framework is flexible for use in other regions outside of Taiwan.

Measuring the performance of police forces in Taiwan using data envelopment analysis.

Recently, the National Police Agency of Taiwan has begun to measure performance levels for police forces across the entire island. The performance benchmarks resulting from this strategy have become the basis for allocation of government funding and other scarce resources. In this work, data envelopment analysis (DEA) was used to construct a scalar measure of efficiency for all police precincts. Further analyses on the effect of degree of urbanization and possible weight restrictions on the outputs upon the efficiency scores of each decision making unit (DMU) were conducted. In addition, a three-stage DEA model was applied to explicitly incorporate environmental factors into the DEA model. The results show that the external environmental factors specified in this study may have some effects on the efficiency scores of each district, albeit not statistically significant. Most decision making units are technically efficient; the average technical efficiency of all DMUs was as high as 98.46% when external environmental factors were accounted for.