Towards Real‐Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.     

An indicator based approach for assessing household’s perceptions of landslide risk in Murree hills of Pakistan

Natural Hazards - The Murree region in Pakistan is frequently affected by landslides and causes damages to lands, houses, life lines and even loss of livestock and human life. We have attempted in...     

Mapping the change of coral reefs using remote sensing and in situ measurements: a case study in Pangkajene and Kepulauan Regency,Spermonde Archipelago,Indonesia

As elsewhere in Indonesia, local inhabitants in the Pangkajene and Kepulauan (PANGKEP) Regency, Spermonde Archipelago area and along the south-west coast of Sulawesi traditionally regard the coral reefs as their livelihood source. Since human activities as well as natural disturbances pose major threats to the coral reefs, these livelihoods may also be at risk. Currently, no comprehensive information on the status and condition of coral reefs in this area is available for this resource management. We determined the changes of coral reef habitat over a period of 20 years from 1994 to 2014 using a satellite Landsat multi-temporal image substantiated with in situ measurement data collected in 2014. The spectral value of coral reefs was extracted from multi-temporal Landsat imagery data, while the diffuse attenuation coefficient of water was obtained by using statistical analysis between the ratio of live coral cover and the spectral value of the visible bands. By using the unsupervised classification integrated with the data ground truth, it is stated that there has been a decline in live coral cover over a period of 20 years from 7716 ha in 1994 to 4236 ha in 2014, with a degradation rate of 174 ha/year. Based on the results, the ratio of the coral cover in the coral reef transects varied from the average of 24% for live corals to 96% for coral rubbles, implying the degraded status of coral reefs in the study area.     

Analytical derivation of water retention for random monodisperse granular media

The mechanics of water retention in unsaturated granular media is of critical importance to a broad range of disciplines including soil science, geotechnical engineering, hydrology and agriculture. Fundamental to water retention is the relationship between degree of saturation and suction, referred to as the water retention curve (WRC). The majority of WRC models are empirically based and seldom incorporate physically meaningful parameters. This study presents an analytical model for the WRC that considers separate contributions from fully filled pores and partially filled pores containing liquid bridges. A recently established unique k-gamma pore volume distribution function for randomly assembled monodisperse granular materials is adopted to determine the contributions of fully filled pores. Calculation of the contribution of residual pore water retained in partially filled pores is undertaken by representing pores as individual cells shaped as platonic shapes of various sizes and determining the volume of all liquid bridges suspended between particles within the pore cells. Weighting factors for the various cell types are obtained from the pore volume distribution to determine the relative contribution of different pore cell geometries to the total residual pore water. The combined model accurately describes experimental data for monodisperse spherical glass beads for both wetting and drying, even though the underlying assumptions do not reflect exactly the complex, interconnected and highly irregular geometry of the pore space. A single parameter provides the lateral shift between the wetting and drying curves. The results of this study provide a geometric understanding of the mechanisms of water retention in granular media.     

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain,China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.