Claire Jarvis

A Comparison among Strategies for Interpolating Maximum and Minimum Daily Air Temperatures. Part II: The Interaction between Number of Guiding Variables and the Type of Interpolation Method

Abstract In a comparative experiment, the sequence of daily maximum and minimum temperatures for 1976 was interpolated over England and Wales to a resolution of 1 km using partial thin plate splines, ordinary kriging, trend surface, and an automatic inverse-distance-weighted method of interpolation. A “level playing field” for comparing the estimation accuracies was established through the incorporation of a consistent set of guiding variables in all interpolators. Once variables were included to guide the interpolators, differences in estimation accuracy among partial thin plate splines, ordinary kriging, and inverse distance weighting results were not significant although the performance of trend surface analysis was poorer. Best accuracies were achieved using partial thin plate splines, with jackknife cross-validation root-mean-square errors of 0.8°C for an annual series of daily maximum temperatures and 1.14°C for daily minimum temperatures. The results from this study suggest that sole reliance on th...

A Comparison among Strategies for Interpolating Maximum and Minimum Daily Air Temperatures. Part I: The Selection of “Guiding” Topographic and Land Cover Variables

Abstract This paper explores the derivation and selection of a comprehensive set of continuous topographic and land cover–related variables to guide the interpolation of daily maximum and minimum temperatures over England and Wales, for an entire annual cycle to a resolution of 1 km. The work draws on and updates historical topoclimatic modeling through use of digital elevation data and land cover data, using the modeling capabilities of geographical information systems. The influential guiding variables under a variety of dominant weather patterns were identified and used to assist with the interpolation of an annual sequence of daily maxima and minima for 1976. North map coordinate (“northing”), elevation, and coastal and urban effects were found to be particularly significant variables in explaining the variation in U.K. daily minimum temperature. Urban factors have not previously been thoroughly investigated, despite the high density of population in England and Wales. Analysis of the residuals from d...

Integrating building footprints and LiDAR elevation data to classify roof structures and visualise buildings

Abstract Three-dimensional urban models are increasingly needed for applications as varied as urban planning and design, microclimate investigation and tourism. Light Detection And Ranging (LiDAR) data are considered to be highly suitable for the three-dimensional reconstruction of urban features such as buildings. Ongoing research is determining how best to integrate LiDAR elevation data with already available vector-based data. This paper reports research on combining building footprints and LiDAR to visualise an urban area (Portbury near Bristol, England) with an emphasis on representing buildings in a GIS environment. The main emphasis here is on retaining a vector model that is suitable for representing regular man-made structures. A major difference between this and earlier work is that before visualisation, this work classifies roof types of buildings as either flat or pitched. We compared LiDAR data at three point densities in terms of successful building type detection and visualisation: 1 (low), 16 (medium) and 40 (high) points per m 2 . There are important data acquisition cost issues at each of these resolutions. High density LiDAR yielded the highest overall accuracy of building type detection and proved useful for identifying roof features, yet lower densities proved more useful for revealing overall roof morphology.

Podcasts in Support of Experiential Field Learning

The paper introduces a library of video podcasts (vodcasts) used to support the learning and teaching of geographical methods and techniques relating to physical geography and GIScience in particular. While the notion of video learning is not new, the accessibility of the multimedia snippets via lightweight mp4 players in a field context allows a geographical freedom to learning that goes beyond previous classroom-based work. Evaluation, conducted through the use of reflective diaries and focus groups, identified that all students thought the podcast reference library was a valuable learning resource, of particular appeal to students with a visual approach to learning.

Artificial neural networks as a tool for spatial interpolation

This paper describes the spatial interpolation of daily minimum air temperature using a feed-forward back-propagation neural network. Simple network configurations were trained to predict minimum temperature using as inputs: (1) date and terrain variables; (2) temperature observations at a number of neighbouring locations; (3) date, terrain variables and neighbouring temperature observations. This is the first time that trend and spatial association are explicitly considered together when interpolating using a neural network. The internal weights given to different inputs to the network were analysed to estimate the degree of spatial correlation between neighbouring stations in addition to the most influential variables contributing to the underlying trend. The spatial distribution of daily minimum temperature was estimated with the greatest accuracy by a network trained on the most comprehensive data set (3). The best model for the prediction of temperature accounts for 93% of the variance, measured by the correlation between independent estimated and observed values over a full year. This is comparable to accuracies reported in the literature using other approaches such as ordinary kriging of the residuals of multi-variate linear regression or partial thin plate splines. An advantage of this method is that the guiding variables are not assumed necessarily to be linearly related with the data being interpolated, and combinative effects are taken into account. Analysis of the internal network weights confirms that the networks are able to select adaptively between trend and covariance components of the interpolation function. Example interpolated daily minimum temperature surfaces for a 100 km x 100 km area in Yorkshire, UK, were generated using the selected network architectures to illustrate the results achievable with an ANN.

From stand-alone programs towards grid-aware services and components: a case study in agricultural modelling with interpolated climate data

Abstract We identify three key issues to be taken into account when designing the next generation of software environments for agricultural modelling. There is a burgeoning need to support collaborative research in a search for answers to big research questions, to integrate the work of data providers and model developers and to provide more generic systems. We describe the concepts of software design of a framework, designed with these points in mind, which facilitates the integration of point-based agricultural models with methods to interpolate climate data. Our approach allows the inter-working of model and interpolation through Fortran functions that are invoked from a central framework. We advocate that the framework code remains open to collaborators, such that it may be adapted to different classes of application, whilst recognising that some module developers need to retain their control on individual elements of the software. The rationale presented within the paper continues a major move away from the stand-alone programs that still dominate agricultural models and interpolation methods. Secondly, the paper considers how these approaches are extendable to exploit opportunities in the emerging Web Service and Grid context. The emerging technology of the Grid allows geographically distributed resources in hardware, software, data and network to be co-ordinated to meet the needs of “virtual organisations.” We explore how the modularity of our existing code can be exploited in the Grid environment, whilst noting the pre-requisite of a co-operative culture in which both software developers and data providers seek to deliver services to the widest possible community of users.

Acknowledging the 'forgotten' and the 'unknown': The role of video podcasts for supporting fi eld-based learning

Abstract A library of video podcasts has been constructed for geographers and those of cognate disciplines, providing detailed information and operational demonstrations for a range of field apparatus relating to matters such as soil properties, water quality and field surveying. Evaluation, conducted through the use of reflective diaries and focus groups, identified that all students consulted thought the podcast reference library was a positive pedagogic development They also thought the process was capable of building confidence in many and of particular appeal to students with a more visual approach to learning.

Infometric and statistical diagnostics to provide artificially-intelligent support for spatial analysis: the example of interpolation

The wider uptake of GIS tools by many application areas outside GIScience means that many newer users of GIS will have high-level knowledge of the wider task, and low-level knowledge of specific system commands as given in reference manuals. However, these newer users may not have the intermediate knowledge that experts in GI science have gained from working with GI systems over several years. Such intermediate knowledge includes an understanding of the assumptions implied by the use of certain functions, and an appreciation of how to combine functions appropriately to create a workflow that suits both the data and overall goals of the geographical analysis task. Focusing on the common but non-trivial task of interpolating spatial data, this paper considers how to help users gain the necessary knowledge to complete their task and minimise the possibility of methodological error. We observe that both infometric (or cognitive) knowledge and statistical knowledge are usually required to find a solution that jointly and efficiently meets the requirements of a particular user and data set. Using the class of interpolation methods as an example, we outline an approach that combines knowledge from multiple sources and argue the case for designing a prototype ‘intelligent’ module that can sit between a user and a given GIS. The knowledge needed to assist with the task of interpolation is constructed as a network of rules, structured as a binary decision tree, that assist the user in selecting an appropriate method according to task-related knowledge (or ‘purpose’) and the characteristics of the data sets. The decision tree triggers exploratory diagnostics that are run on the data sets when a rule requires to be evaluated. Following evaluation of the rules, the user is advised which interpolation method might be and should not be considered for the data set. Any parameters required to interpolate the particular data set (e.g. a distance decay parameter for Inverse Distance Weighting) are also supplied through subsequent optimisation and model selection routines. The rationale of the decision process may be examined, so the ‘intelligent interpolator’ also acts as a learning tool.

Smoothing/filtering LiDAR digital surface models. Experiments with loess regression and discrete wavelets

This paper reports on the smoothing/filtering analysis of a digital surface model (DSM) derived from LiDAR altimetry for part of the River Coquet, Northumberland, UK using loess regression and the 2D discrete wavelet transform (DWT) implemented in the S-PLUS and R statistical packages. The chosen method of analysis employs a simple method to generate ‘noise’ which is then added to a smooth sample of LiDAR data; loess regression and wavelet methods are then used to smooth/filter this data and compare with the original ‘smooth’ sample in terms of RMSE. Various combinations of functions and parameters were chosen for both methods. Although wavelet analysis was effective in filtering the noise from the data, loess regression employing a quadratic parametric function produced the lowest RMSE and was the most effective.

Going mobile: perspectives on aligning learning and teaching in geography

There has been little consideration to date regarding how we might best adjust our assessment protocols so that the overall learning experience remains appropriately aligned to both content and teaching approach when adopting location-specific mobile learning. This paper explores the success of a novel strategy to design an assessment regime that captures a field experience in Dublin as a whole for both staff and students alike. Evaluation identified that the use of a combined mediascape-essay approach as a major component of the assessment successfully captured the main elements of the learning and teaching experience and facilitated deeper learning and creativity.