Space–time multiple regression model for grid-based population estimation in urban areas

We can collect, store, and analyze a huge amount of information about human mobility and social interaction activities due to the emergence of information and communication technologies and location-enabled mobile devices under cyber physical system frameworks. The high spatial resolution of population data on a multi-temporal scale is required by transport planners, human geographers, social scientists, and emergency management teams. In this study, we build a space-time multiple regression model to estimate grid-based (500 m × 500 m) spatial resolution at multi-temporal scale (30-min intervals) population data based on the space-time relationship among geospatially enabled person trip (PT) survey data and incorporate both mobile call (MC) and geotagged Twitter (GT) data. Since using geospatially enabled PT survey data as dependent variables enables us to acquire actual population amounts, which strongly depend on MCs and social interaction activities. Although many grids have a strong correlation between PT and MC/GT, some show fewer correlation results, especially where the grids have factories, schools, and workshops in which fewer MCs are found but a large population is presented. Although GT data are sparser than MCs, people from amusement and tourist areas can be detected by GT data. The space-time multiple regression model can also estimate the different amounts of populations based on human travel behavior that changes over space and time. According to accuracy assessments, the night-time estimated results, especially between 00:00 and 06:30, strongly correlate with national census data except in places where the grids have railway and subway stations.     

A GIS Approach to Estimation of Building Population for Micro-spatial Analysis

Population data used in GIS analyses is generally assumed to be homogeneous and planar (i.e. census tracts, townships or prefectures) due to the public unavailability of building population data. However, information on building population is required for micro-spatial analysis for improved disaster management and emergency preparedness, public facility management for urban planning, consumer and retail market analysis, environment and public health programs and other demographic studies. This article discusses a GIS approach using the Areametric and Volumetric methods for estimating building population based on census tracts and building footprint datasets. The estimated results were evaluated using actual building population data by visual, statistical and spatial means, and validated for use in micro-spatial analysis. We have also implemented a standalone GIS tool (known as 'PopShape GIS') for generating new building footprint with population attribute information based on user-defined criteria.     

Sampling schemes for quality estimation of a flowing material

Four sampling schemes dealing with sampling of a continuous material flowing past the sampling point during a fixed time interval (0,T) are studied. Two of the schemes are the ones used in standard practice while the other two are the ones proposed as being possible sampling schemes to be coupled with a metal analyser developed by the CSIRO Division of Mineral Physics.Specific comparisons of the sampling schemes are made for a mathematical model of the functions describing a continuous stream based on simple and realistic assumptions. Discussion is provided on the advantages and disadvantages of the two new sampling schemes proposed for use with the analyser.     

Observed characteristics of flow,water mass,and turbulent mixing in the Preparis Channel

Preparis Channel is the very important exchange path of energy and materials between the northern Bay of Bengal and Andaman Sea(AS). A set of hydrographic measurements, a microstructure profiler, and a deep mooring were used to determine the characteristics of water masses, turbulent mixing, and flows in the Preparis Channel. The unprecedented short-term mooring data reveal that a deep current in the deep narrow passage(below 400 m) of the Preparis Channel flows toward the Bay of Bengal(BoB) wit...