SWE-FE: Extending Folksonomies to the Sensor Web

In: Collaborative Technologies and Systems (CTS), 2010 International Symposium, 17-21 May 2010

Rezel, R.  and Liang, S.

“This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment.”

Correlating Multimodal Physical Sensor Information with Biological Analysis in Ultra Endurance Cycling

Sensors 2010, 10(8), 7216-7235

Gregory C. May, Aiden R. Doherty, Alan F. Smeaton, and Giles D. Warrington

“The sporting domain has traditionally been used as a testing ground for new technologies which subsequently make their way into the public domain. This includes sensors. In this article a range of physical and biological sensors deployed in a 64 hour ultra-endurance non-stop cycling race are described. A novel algorithm to estimate the energy expenditure while cycling and resting during the event are outlined. Initial analysis in this noisy domain of ‘sensors in the field’ are very encouraging and represent a first with respect to cycling.”

Environmental Health Impacts of Mobility and Transport in Hai Phong, Vietnam

Stochastic Environmental Research and Risk Assessment, Published online 10 February 2010

Stijn Dhondt, Quynh Le Xuan, Hieu Vu Van, and Luc Hens

“Traffic is an essential part of modern society and mobility is part of its socio-economic setting. However, signs of counter productivity arise as the current mobility patterns substantially affect our health, including the consequences from traffic accidents, air pollution—which causes even more victims than traffic accidents—and traffic-noise. The use of private motorised vehicles also contributes to sedentarism, climate change and psychological effects. This paper reviews these mobility related health effects and applies them to the situation in Hai Phong, a Vietnamese port-city in fast development and facing growing mobility patterns. In his Master Plan the city developed a view on its development together with the transportation infrastructure up to 2020. Together with the fast changing mobility patterns, such as a modal change from bicycles to motorcycles and cars, this lead to an increase in motorized vehicles and non-negligible environmental health risks. Applying the methodology of a Health Impact Assessment as used in previous studies the current health burden is estimated, focussing on particulate matter (PM) and noise. For PM10 1287 deaths per year were calculated for the current situation, where the estimated number of deaths by 2020 doubled up to 2741. Hospital admissions due to PM10 raised from 44,954 now to 51,467 in 2020 and for PM2.5 the restricted-activity days were calculated, accounting for 852,352 per year. For noise only calculations for the current state (2007) were performed. The total estimated DALYs due to noise was 4758.”

Space-Time Kernels

ISPRS Commission II Mid-Term Symposium: Theory, Data Handling and Modelling in GeoSpatial Information Science – 26-28 May 2010, Hong Kong

Jiaqiu Wang, Tao Cheng, and James Haworth

“Kernel methods are a class of algorithms for pattern recognition. They play an important role in the current research area of spatial and temporal analysis since they are theoretically well-founded methods that show good performance in practice. Over the years, kernel methods have been applied to various fields including machine learning, statistical analysis, imaging processing, text categorization, handwriting recognition and many others. More recently, kernel-based methods have been introduced to spatial analysis and temporal analysis. However, how to define kernels for space-time analysis is still not clear. In the paper, we firstly review the relevant kernels for spatial and temporal analysis, then a space-time kernel function (STK) is presented based on the principle of convolution kernel for space-time analysis. Furthermore, the proposed space-time kernel function (STK) is applied to model space-time series using support vector regression algorithm. A case study is presented in which STK is used to predict China’s annual average temperature. Experimental results reveal that the space-time kernel is an effective method for space-time analysis and modelling.”

Headwater Streams: Neglected Ecosystems in the EU Water Framework Directive. Implications for Nitrogen Pollution Control

Environmental Science & Policy, In Press, Corrected Proof, Available online 26 May 2010

Luis Lassaletta, Héctor García-Gómez, Benjamín S. Gimeno, and José V. Rovira

“The European Union Water Framework Directive (WFD) aims to achieve the “good status” of waters by 2015, through monitoring and control of human impacts on “bodies of surface water” (BSWs), discrete elements for quality diagnosis and management. Headwater streams, however, are frequently neglected as they are not usually recognised as BSW. This poses limitations for the management of river catchments, because anthropogenic impacts on headwaters can constrain the quality of downstream rivers. To illustrate this problem, we compared nitrate levels and land use pressures in a small agricultural catchment with those recorded in the catchment in which it is embedded (Ega), and in the Ebro River Basin (NE Spain) comprising both. Agriculture greatly influenced water nitrate concentration, regardless of the size of the catchments: R2 = 0.91 for headwater catchments (0.1–7.3 km2), and R2 = 0.82 for Ebro tributary catchments (223–3113 km2). Moreover, nitrate concentration in the outlet of a non-BSW small river catchment was similar to that of the greater downstream BSW rivers. These results are of interest since, despite representing 76% of the length of the Ega catchment hydrographical network, only 3.1% of the length of the headwater streams has been identified as BSWs. Human activities affecting headwater streams should therefore be considered if the 2015 objective of the WFD is to be achieved.”

Crop Production and Road Connectivity in Sub-Saharan Africa: A Spatial Analysis

World Bank Policy Research Working Paper No. WPS 5385, July 2010

Dorosh, Paul; Wang, Hyoung-Gun; You, Liang; and Schmidt, Emily

“This study examines the relationship between transport infrastructure and agriculture in Sub-Saharan Africa using new data obtained from geographic information systems (GIS). First, the authors analyze the impact of road connectivity on crop production and choice of technology. Second, they explore the impact of investments that reduce road travel times. Finally, they show how this type of analysis can be used to compare cost-benefit ratios for alternative road investments in terms of agricultural output per dollar invested. The authors find that agricultural production is highly correlated with proximity (as measured by travel time) to urban markets. Likewise, adoption of high-productive/high-input technology is negatively correlated with travel time to urban centers. There is therefore substantial scope for increasing agricultural production in Sub-Saharan Africa, particularly in more remote areas. Total crop production relative to potential production is 45 percent for areas within four hours’ travel time from a city of 100,000 people. In contrast, it is just 5 percent for areas more than eight hours away. Low population densities and long travel times to urban centers sharply constrain production. Reducing transport costs and travel times to these areas would expand the feasible market size for these regions. Compared to West Africa, East Africa has lower population density, smaller local markets, lower road connectivity, and lower average crop production per unit area. Unlike in East Africa, reducing travel time does not significantly increase the adoption of high-input/high-yield technology in West Africa. This may be because West Africa already has a relatively well-connected road network.”