European air quality maps 2005 including uncertainty analysis

The objective of this report is (a) the updating and refinement of European air quality maps based on annual statistics of the 2005 observational data reported by EEA Member countries in 2006, and (b) the further improvement of the interpolation methodologies. The paper presents the results achieved and an uncertainty analysis of the interpolated maps and builds upon earlier reports from Horalék et al. (2005; 2007)

Comparing Spatial Interpolation Techniques of Local Urban Temperature for Heat-related Health Risk Estimation in a Subtropical City

INTRODUCTION: The threat of elevated temperatures and more intense and prolonged heat waves coupled with urban heat islands presents a significant risk to human health. City planners and policymakers need tools that predict how overheating risk varies within a city under different climate change and mitigation scenarios. A key driver of determining overheating risk is exposure to local urban temperatures and the extent to which such exposure may be modified by built environments where the majority of people spend their time. Due to the dispersion of monitoring stations, techniques are needed to extrapolate from single point measurements and their modifying determinants. This research aims to compare nine GIS spatial interpolation techniques of estimating street-level temperature in a subtropical city. METHODS: Taipei city, Taiwan, is located in a subtropical zone with one of the highest population densities in the world. Taipei experienced warmer winters and hotter summers in recent 10 years with average temperature from 16.4 to 30.1 °C, and expected to rise from 0.8(RCP2.6) to 3.2(RCP8.5)°C in 2081-2100. In this study, data from the Taiwan Central Weather Bureau weather stations and the Taiwan Environmental Protection Administration air monitoring sites were used. Nine interpolation techniques were applied. These were validated by using records from two sources to cross-validate by comparing Standardised mean error and Standardised Root-Mean-Square error. RESULTS: Kriging techniques have better prediction performance than four non-geostatistical interpolation techniques. The performance of OCK techniques indicated the built environment, such as the nearby village park area or home density, can be important modifiers of external temperature in cities. DISCUSSION: Local urban climates are complex systems; selecting a robust interpolation technique that accounts for underlying drivers is essential for policymakers. This research provides the basis to further estimate overheating risk by estimating local outdoor street-level temperature and the modifying effects of the built environment

Optimizing public transit quality and system access: the multiple-route, maximal covering/shortest-path problem

Public transit service is a promising travel mode because of its potential to address urban sustainability. However, current ridership of public transit is very low in most urban regions -- particularly those in the United States. Low transit ridership can be attributed to many factors, among which poor service quality is key. Transit service quality may potentially be improved by decreasing the number of service stops, but this would be likely to reduce access coverage. Improving transit service quality while maintaining adequate access coverage is a challenge facing public transit agencies. In this paper we propose a multiple-route, maximal covering/shortest-path model to address the trade-off between public transit service quality and access coverage in an established bus-based transit system. The model is applied to routes in Columbus, Ohio. Results show that it is possible to improve transit service quality by eliminating redundant or underutilized service stops.

Evaluation of MARS for the spatial distribution modeling of carbon monoxide in an urban area

Spatial distribution modeling of CO in Tehran can lead to better air pollution management and control, and it is also suitable for exposure assessment and epidemiological studies. In this study MARS (Multi–variate Adaptive Regression Splines) is compared with typical interpolation techniques for spatial distribution modeling of hourly and daily CO concentrations in Tehran, Iran. The measured CO data in 2008 by 16 monitoring stations were used in this study. The Generalized Cross Validation (GCV) and Cross Validation techniques were utilized for the parameter optimization in the MARS and other techniques, respectively. Then the optimized techniques were compared based on the mean absolute of percentage error (MAPE). Although the Cokriging technique presented less MAPE than the Inverse Distance Weighting, Thin Plate Smooth Splines and Kriging techniques, MARS exhibited the least MAPE. In addition, the MARS modeling procedure is easy. Therefore, MARS has merit to be introduced as an appropriate method for spatial distribution modeling. The number of air pollution monitoring stations is very low (16 stations for 22 zones) and the distribution of stations is not suitable for spatial estimation, hence the level of errors was relatively high (more than 60%). Consequently, hourly and daily mapping of CO provides a limited picture of spatial patterns of CO in Tehran, but it is suitable for estimation of relative CO levels in different zones of Tehran. Hence, the map of mean annual CO concentration was generated by averaging daily CO distributions in 2008. It showed that the most polluted regions in Tehran are the central, eastern and southeastern parts, and mean annual CO concentration in these parts (zones 6, 12, 13, 14 and 15) is between 4.2 and 4.6 ppm

Sharpening land use maps and predicting the trends of land use change using high resolution airborne image: A geostatistical approach

High quality land use/land cover (LULC) data with fine spatial resolution and frequent temporal coverage are indispensable for revealing detail information of the Earth’s surface, characterizing LULC of the area, predicting the plausible land use changes, and assessing the viability and impacts of any development plans. While airborne imagery has high spatial resolution, it only provides limited temporal coverage over time. The LULC data from historical remote sensing images, such as those from Landsat, have frequent coverages over a long temporal period, but their spatial resolutions are low. This paper presents a spatio-temporal Cokriging method to sharpen LULC data and predict the trends of land use change. A set of time-series coarse resolution LULC maps and one frame of high spatial resolution airborne imagery of the Upper Mill Creek Watershed were used to illustrate the utility of our method. By explicitly describing the spatio-temporal dependence within and between different datasets, modelling the Anderson classification codes using spatial, temporal, and cross-covariance structures, and transforming the Anderson integer classification code to class probability, our method was able to resolve the differences between multi-source spatio-temporal LULC data, generate maps with sharpened and detailed land features, characterize the spatial and temporal LULC changes, reveal the trend of LULC change, and create a quality dataset invaluable for monitoring, assessing, and modelling LULC changes

Vulnerability Assessment of Groundwater to NO3 Contamination Using GIS, DRASTIC Model and Geostatistical Analysis

The study employed Geographical Information System (GIS) technology to investigate the vulnerability of groundwater to NO3 content in Buncombe County, North Carolina in two different approaches. In the first study, the spatial distribution of NO3 contamination was analyzed in a GIS environment using Kriging Interpolation. Cokriging interpolation was used to establish how NO3 relates to land cover types and depth to water table of wells in the county. The second study used DRASTIC model to assess the vulnerability of groundwater in Buncombe County to NO3 contamination. To get an accurate vulnerability index, the DRASTIC parameters were modified to fit the hydrogeological settings of the county. A final vulnerability map was created using regression based DRASTIC, a statistic method to measure how NO3 relates to each of the DRASTIC variables. Although the NO3 concentration in the county didn’t exceed the USEPA standard limit (10mg/L), some areas had NO3 as high as 8.5mg/L

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

Estimation of Housing Price Variations Using Spatio-Temporal Data

This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices overmultiple years. Using themodel, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK)method is used to predict housing prices. The results are compared to the cokrigingwith external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.This work was conducted within the framework of a research project granted by CEMIX-6/16 and financed by Banco Santander