Web-based GIS Approaches to Enhance Public Participation in Wind Farm Planning

Transactions in GIS, April 2011, Volume 15, Issue 2

Robert Berry, Gary Higgs, Richard Fry, and Mitch Langford

“Planning information pertaining to the potential visual impacts of proposed construction developments is particularly important in the case of wind farm planning, given the high levels of concern amongst members of the public regarding the perceived negative visual impacts of wind turbines on the landscape. Previous research has highlighted the shortcomings associated with traditional visualization techniques used to assess these impacts, and also the means by which such information is then disseminated to the wider public during the consultation stages of the wind farm planning process. This research is concerned with examining the potential of Web-based mapping and digital landscape visualization techniques for addressing some of these shortcomings. This article reports the findings of a Web-based survey study designed to evaluate the potential of online GIS-based approaches for improving the effectiveness and dissemination of wind farm visualizations and enhancing public participation in the wind farm planning process. Results from the survey study add to the research literature by demonstrating how innovative Web-based approaches have real potential for augmenting existing methods of information provision and public participation in the planning process. The findings of this study are also potentially transferrable to other landscape planning scenarios.”

What Motivates Governments to Adopt the Geospatial Web 2.0?

Proceedings of Spatial Knowledge and Information – Canada (SKI-Canada) 2011, March 3-6 in Fernie, BC, Canada

Nama Budhathoki and R E. Sieber

“The Geospatial Web 2.0 (Geoweb) has the potential to transform the ways governments conduct their operations. The Geoweb can be used to mobilize citizens for measuring, monitoring, and managing geo-referenced phenomena. Considerable research is underway on understanding citizen’s motivations to volunteer geographic information. We explore what motivates governments to adopt these technologies and content.”

Analysing Spatial Point Patterns in ‘R’

Learn how to analyse spatial point patterns using 'R'

These workshop notes, written in 2010, cover statistical methods available in public domain software.

The workshop uses the statistical package ‘R’ and is based on ‘spatstat’, an add-on library for ‘R’ for the analysis of spatial data.

Topics covered include:

  • statistical formulation and methodological issues
  • data input and handling
  • R concepts such as classes and methods
  • nonparametric intensity estimates
  • goodness-of-fit testing for Complete Spatial Randomness
  • maximum likelihood inference for Poisson processes
  • model validation for Poisson processes
  • distance methods and summary functions such as Ripley’s K function
  • non-Poisson point process models
  • simulation techniques
  • fitting models using summary statistics
  • Gibbs point process models
  • fitting, simulating and validating Gibbs models
  • multitype and marked point patterns
  • exploratory analysis of marked point patterns
  • multitype Poisson process models and maximum likelihood inference
  • multitype Gibbs process models and maximum pseudolikelihood
  • line segment data.

This workshop requires ‘R’ version 2.10.0 or later, and ‘spatstat’ version 1.21-2 or later.

Effects of Ignition Location Models on the Burn Patterns of Simulated Wildfires

Environmental Modelling & Software, Volume 26 Issue 5, May 2011

Avi Bar Massada, Alexandra D. Syphard, Todd J. Hawbaker, Susan I. Stewart, and Volker C. Radeloff

“Fire simulation studies that use models such as FARSITE often assume that ignition locations are distributed randomly, because spatially explicit information about actual ignition locations are difficult to obtain. However, many studies show that the spatial distribution of ignition locations, whether human-caused or natural, is non-random. Thus, predictions from fire simulations based on random ignitions may be unrealistic. However, the extent to which the assumption of ignition location affects the predictions of fire simulation models has never been systematically explored. Our goal was to assess the difference in fire simulations that are based on random versus non-random ignition location patterns. We conducted four sets of 6000 FARSITE simulations for the Santa Monica Mountains in California to quantify the influence of random and non-random ignition locations and normal and extreme weather conditions on fire size distributions and spatial patterns of burn probability. Under extreme weather conditions, fires were significantly larger for non-random ignitions compared to random ignitions (mean area of 344.5 ha and 230.1 ha, respectively), but burn probability maps were highly correlated (r = 0.83). Under normal weather, random ignitions produced significantly larger fires than non-random ignitions (17.5 ha and 13.3 ha, respectively), and the spatial correlations between burn probability maps were not high (r = 0.54), though the difference in the average burn probability was small. The results of the study suggest that the location of ignitions used in fire simulation models may substantially influence the spatial predictions of fire spread patterns. However, the spatial bias introduced by using a random ignition location model may be minimized if the fire simulations are conducted under extreme weather conditions when fire spread is greatest.”

Loading Architecture for a Sensor Web Browser on Digital Earth

Proceedings of Spatial Knowledge and Information – Canada (SKI-Canada) 2011, March 3-6 in Fernie, BC, Canada

Chih-Yuan Huang Steve Liang

“The world-wide sensor web observes real world phenomena at a particular moment in time with a large number of geo-referenced sensors. Sensor web needs a sensor web browser for accessing distributed and heterogeneous sensor networks in a coherent frontend. The Digital Earth provides a geo-referenced three-dimensional environment for intuitively browsing and displaying sensor observations. However, the major challenge is to load the vast amount of sensor observations from servers to a sensor web browser while minimizing the delay that a user experiences. This research uses two techniques to address the challenge. First, the browser caches transmitted data onto the local hard drive to reduce redundant internet bandwidth consumption. Second, this work designs a loading architecture to decouple sensor data loading, rendering, and browsing. The proposed scheme is implemented in the GeoCENS sensor web browser. To the best of our knowledge, with the proposed loading architecture, GeoCENS is the first Digital Earth-based sensor web browser.”