Concept and Spatial Analysis Method of Urban Environmental Traffic Capacity

Journal of Transportation Engineering, Volume 135, Issue 11, pp. 873-879, November 2009

Tie-zhu Li, Jin-shan Lin, Meng-ting Wu, and Xi-wei Wang

“A method to determine the intensity of traffic is a key issue for the control of air pollution from urban transport in order to meet air quality requirements. In this study, by combining the definitions of environmental capacity and traffic capacity, the concepts of traffic-related environmental capacity and environmental traffic capacity are discussed. A mesh analysis method for environmental traffic capacity in urban areas has been developed by integrating the spatial segmentation method applied for the analysis of urban traffic system with the environmental spatial segmentation method. To calculate the spatial transfer coefficients of air pollutants between grids, a method of backing virtual point sources was established and applied to this case. The linear optimization model of traffic-related environmental capacity was established on a basis of the diffusion and transmission theories for air pollutants. Taking peak hours as an analytical period, we have established an urban environmental traffic capacity analysis model aimed at controlling various air pollutants emitted by vehicles. A case study shows that the model can analyze and calculate vehicle traffic volumes, which satisfy air quality requirements within specified temporal and spatial ranges.”

Spatial-Temporal Analysis of Dead Crow Reports Associated with a West Nile Virus Epidemic

Asian Journal of Arts and Sciences, Vol. 1, No. 1, pp. 29-41, 2010

Cheng-Yu Lee, Bryan K. Epperson, Edward D. Walker, and Kimberly Signs

“We apply the Space-Time AutoRegressive Moving Average (STARMA) modeling methods in an investigation of the spreading dynamics of a West Nile virus (WNV) epidemic in crows in the Detroit Metro area in 2002. The data fit very closely those expected from a purely STAR (Space-Time AutoRegressive) process having low spatial and temporal orders. The model can be used to characterize the past and possibly even predict the future dynamics of spreading behavior and, most importantly, to provide information about the factors which govern the spreading behavior. Use of the STARMA model allows estimation of the rate of spread of WNV at different spatial scales and thus characterization of the spatial and temporal scales expected. Determination of spatial-temporal autoregressive parameters using STARMA holds considerable promise for characterizing emerging infectious diseases.”

Examining Tidal Inundation and Salt Marsh Vegetation Distribution Patterns using Spatial Analysis (Botany Bay, Australia)

Journal of Coastal Research, Volume 26, Issue 1, pp. 94 – 102, 2010

Deanne Hickey and Eleanor Bruce

“In predicting the impact of human disturbance on coastal wetland environments and understanding ecological response to changing intertidal conditions, there is a need to understand the relationship between species distribution and elevation dependent tidal inundation. The intent of this paper is to examine the relationship between vegetation distribution patterns and the extent of tidal inundation modelled using fine scale elevation data. Field surveys were undertaken at Towra Point, Botany Bay, Australia using high precision global positioning system (GPS) and theodolite survey to record species location and obtain horizontal mapping accuracy required for modelling localised wetland topography. Species survival and growth is governed by physiological factors that are determined by tidal inundation frequency and extent. The coupling of local tidal parameters with a detailed wetland elevation survey enabled the modelling of inundation extent. Species distributional ranges at Towra Point corresponded with modelled tidal extent, and results indicated the presence of species zonation. Fine scale representation of localised terrain features within the surface model also demonstrated the impact of altered topography on vegetation distribution patterns. The spatial analysis methods applied in this study depicted variations in inundation patterns under the differing tidal phases that influence physiological conditions throughout the wetland.”