Multi-technique comparisons of 10?years of wet delay estimates on the west coast of Sweden

We present comparisons of 10-year-long time series of the atmospheric zenith wet delay (ZWD), estimated using the global positioning system (GPS), geodetic very long baseline interferometry (VLBI), a water vapour radiometer (WVR), radiosonde (RS) observations, and the reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF). To compare the data sets with each other, a Gaussian filter is applied. The results from 10 GPS–RS comparisons using sites in Sweden and Finland show that the full width at half maximum at which the standard deviation (SD) is a minimum increases with the distance between each pair. Comparisons between three co-located techniques (GPS, VLBI, and WVR) result in mean values of the ZWD differences at a level of a few millimetres and SD of less than 7?mm. The best agreement is seen in the GPS–VLBI comparison with a mean difference of ?3.4?mm and an SD of 5.1?mm over the 10-year period. With respect to the ZWD derived from other techniques, a positive bias of up to ~7?mm is obtained for the ECMWF reanalysis product. Performing the comparisons on a monthly basis, we find that the SD including RS or ECMWF varies with the season, between 3 and 15?mm. The monthly SD between GPS and WVR does not have a seasonal signature and varies from 3 to 7?mm.     

GIS-based Assessment and Management of Nitrogen and Phosphorus in R?nne? River Catchment, Sweden

Eutrophication in lakes, estuaries and rivers occur due to over-enrichment with nutrients. Amongst the various nutrients Nitrogen (N) and Phosphorous (P) are the elements which are considered as the major pollutants. The present study looks into the problem of managing nutrient losses both from point sources and diffuse sources. The study was carried out in the River R?nne? catchment in South of Sweden where N and P have been assessed by creation of a pollution inventory based on GIS technology. The nutrient loads from different land use types were estimated using export coefficients and evaluated using monitoring data. A comparative analysis is done on the measured and estimated N and P leaching from diffused sources and point sources. Protective zones were suggested to decrease the P export from agricultural land, and the distribution of construction cost was analyzed among administrative regions according to the proportion of catchments, cultivated land, nutrient leaching, protective zone and length of river section within each municipality. The estimated error of the nutrient amount from landuse using GIS is 9.75% and 12.71% for N and P, respectively. The diffused sources contribute 91.53% and 94.74% of the total N and total P respectively, so the creation of a protective zone could be an effective way to decrease the nutrient export. The study demonstrates the power of GIS as a tool to analyze problems related to environmental monitoring and planning at administrative level, which could help in decision making and support for better management of the water resources.     

How to assess the success of the open data ecosystem?

ABSTRACT

Open data are currently a hot topic and are associated with realising ambitions such as a more transparent and efficient government, solving societal problems, and increasing economic value. To describe and monitor the state of open data in countries and organisations, several open data assessment frameworks were developed. Despite high scores in these assessment frameworks, the actual (re)use of open government data (OGD) fails to live up to its expectations. Our review of existing open data assessment frameworks reveals that these only cover parts of the open data ecosystem. We have developed a framework, which assesses open data supply, open data governance, and open data user characteristics holistically. This holistic open data framework assesses the maturity of the open data ecosystem and proves to be a useful tool to indicate which aspects of the open data ecosystem are successful and which aspects require attention. Our initial assessment in the Netherlands indicates that the traditional geographical data perform significantly better than non-geographical data, such as healthcare data. Therefore, open geographical data policies in the Netherlands may provide useful cues for other OGD strategies.     

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

Abstract

For decades, uncertainty visualisation has attracted attention in disciplines such as cartography and geographic visualisation, scientific visualisation and information visualisation. Most of this research deals with the development of new approaches to depict uncertainty visually; only a small part is concerned with empirical evaluation of such techniques. This systematic review aims to summarize past user studies and describe their characteristics and findings, focusing on the field of geographic visualisation and cartography and thus on displays containing geospatial uncertainty. From a discussion of the main findings, we derive lessons learned and recommendations for future evaluation in the field of uncertainty visualisation. We highlight the importance of user tasks for successful solutions and recommend moving towards task-centered typologies to support systematic evaluation in the field of uncertainty visualisation.     

Living Dinosaurs – A Pessimistic View of the Digital Future?

Abstract

A dealer in second-hand maps, mostly Ordnance Survey maps, looks to the future of map dealers in the light of technological advances and the digital age.     

What governs the presence of residual vegetation in boreal wildfires?

Wildfires are frequent boreal forest disturbances in Canada, and emulating their patterns with forest harvesting has emerged as a common forest management goal. Wildfires contain many patches of residual vegetation of various size, shape, and composition; understanding their characteristics provides insights for improved emulation criteria. We studied the occurrence of residual vegetation within eleven boreal wildfire events in a natural setting; fires ignited by lightning, no suppression efforts, and no prior anthropogenic disturbances. Relative importance of the measurable geo-environmental factors and their marginal effects on residual presence are studied using Random Forests. These factors included distance from natural firebreaks (wetland, bedrock and non-vegetated areas, and water), land cover, and topographic variables (elevation, slope, and ruggedness index). We present results at spatial resolutions ranging from four to 64 m while emphasizing four and 32 m since they mimic IKONOS- and Landsat-type images. Natural firebreak features, especially the proximity to wetlands, are among the most important variables that explain the likelihood residual occurrence. The majority of residual vegetation areas are concentrated within 100 m of wetlands. Topographic variables, typically important in rugged terrain, are less important in explaining the presence of residuals within our study fires.     

Does plot size affect the performance of GIS-based species distribution models?

Species distribution models are used extensively in predicting the distribution of vegetation across a landscape. Accuracy of the species distribution maps produced by these models deserves attention, since low accuracy maps may lead to erroneous conservation decisions. While plot size is known to influence measures of species richness, its effect on our ability to predict species distribution ranges has not been tested. Our aim is to test whether the accuracy of the distribution maps produced depend on the size of the plot (quadrat) used to collect biological data in the field. In this study, the presences of four plant species were recorded in five sizes of circular plots, with radii ranging from 8 to 100 m. Logistic regression-based models were used to predict the distributions of the four plant species based on empirical evidence of their relationship with eight environmental predictors: distance to river, slope, aspect, altitude, and four principle component axes derived using reflectance values from Aster images. We found that plot size affected the probability of recording the four species, with reductions in plot size generally increasing the frequency of recorded absences. Plot size also significantly affected the likelihood of correctly predicting the distribution of species whenever plot size was below the minimum size required to consistently record species’ presence. Furthermore, the optimal plot size for fitting species distribution models varied among species. Finally, plot size had little impact on overall accuracy, but a strong, positive impact on Kappa accuracy (which provides a stronger measure of model accuracy by accounting for the effects of chance agreements between predictions and observations). Our results suggest that optimal plot size must be considered explicitly in the creation of species distribution models if they are to be successfully adopted into conservation efforts.     

How Large Was the Earth in the Sixteenth Century? The Length of the Degree of Latitude in the Iberian Cartography of the Renaissance

In this paper, the results of a cartometric analysis of a number of Portuguese and Spanish charts of the sixteenth and seventeenth centuries are presented and conclusions are drawn concerning the length of the degree of latitude adopted in the Iberian nautical cartography of the Renaissance. Under the light of what we know about the navigational and charting methods of the time, a new interpretation of the textual and cartographic sources is proposed. Rather than being the result of technical considerations or precise measurements, the adoption of the various standards during this long period appears to be no more than the echoes of the traditional models of the Earth and of the political disputes between Portugal and Spain.     

Monitoring mangrove forests: Are we taking full advantage of technology?

Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research.     

The end of a paradigm: is Zipf’s law universal?

It is largely accepted among geographers and economists that the City Size Distribution (CSD) is well described by a power law, i.e., Zipf’s law. This opinion is shared by this community in a manner it could be treated as a paradigm. In reality, however, Zipf’s law is not always observed (even as an approximation), and we prefer to adopt a classification of the CSD into three classes. In this work, we present the characteristics of these classes and give some examples for them. We use the Israeli system of cities as an interesting case study in which the same ensemble of cities passes from one class to another. We relate this change to the urbanization process that occurred in Israel from the 1960s onwards.     

Spatial analysis in epidemiology: Nascent science or a failure of GIS?

This paper summarizes contributions of GIS in epidemiology, and identifies needs required to support spatial epidemiology as science. The objective of spatial epidemiology is to identify disease causes and correlates by relating spatial disease patterns to geographic variation in health risks. GIS supports disease mapping, location analysis, the characterization of populations, and spatial statistics and modeling. Although laudable, these accomplishments are not sufficient to fully identify disease causes and correlates. One reason is the failure of present-day GIS to provide tools appropriate for epidemiology. Two needs are most pressing. First, we must reject the static view: meaningful inference about the causes of disease is impossible without both spatial and temporal information. Second, we need models that translate space-time data on health outcomes and putative exposures into epidemiologically meaningful measures. The first need will be met by the design and implementation of space-time information systems for epidemiology; the second by process-based disease models.     

Comparing the fractality of European urban neighbourhoods: do national contexts matter?

The objective of this paper is to show that morphological similarities between built-up urban surfaces are greater across borders than within cities in Europe: living, architectural and planning trends are international. The spatial arrangement of built-up areas is analysed here by means of fractal indices using a set of 97 town sections selected from 18 European urban agglomerations. The fractal dimension is estimated by correlation techniques. Results confirm that morphological similarities are higher across countries/cities than within. Moreover, two types of fractal laws are considered: one uses the basic fractal scaling law; the other introduces a prefactor a that is often called a “form factor” in the fractal literature. Differences in the results obtained by both laws are explained empirically as well as theoretically, and suggestions are made for further measurements.     

How sensitive are estimates of carbon fixation in agricultural models to input data?

Background

Accurate, high-resolution mapping of aboveground carbon density (ACD, Mg C ha^-1) could provide insight into human and environmental controls over ecosystem state and functioning, and could support conservation and climate policy development. However, mapping ACD has proven challenging, particularly in spatially complex regions harboring a mosaic of land use activities, or in remote montane areas that are difficult to access and poorly understood ecologically. Using a combination of field measurements, airborne Light Detection and Ranging (LiDAR) and satellite data, we present the first large-scale, high-resolution estimates of aboveground carbon stocks in Madagascar.

Results

We found that elevation and the fraction of photosynthetic vegetation (PV) cover, analyzed throughout forests of widely varying structure and condition, account for 27-67% of the spatial variation in ACD. This finding facilitated spatial extrapolation of LiDAR-based carbon estimates to a total of 2,372,680 ha using satellite data. Remote, humid sub-montane forests harbored the highest carbon densities, while ACD was suppressed in dry spiny forests and in montane humid ecosystems, as well as in most lowland areas with heightened human activity. Independent of human activity, aboveground carbon stocks were subject to strong physiographic controls expressed through variation in tropical forest canopy structure measured using airborne LiDAR.

Conclusions

High-resolution mapping of carbon stocks is possible in remote regions, with or without human activity, and thus carbon monitoring can be brought to highly endangered Malagasy forests as a climate-change mitigation and biological conservation strategy.     

How fast is a cow? Cross‐Scale Analysis of Movement Data

Data representing the trajectories of moving point objects are becoming increasingly ubiquitous in GIScience, and are the focus of much methodological research aimed at extracting patterns and meaning describing the underlying phenomena. However, current research within GIScience in this area has largely ignored issues related to scale and granularity – in other words how much are the patterns that we see a function of the size of the looking glass that we apply? In this article we investigate the implications of varying the temporal scale at which three movement parameters, speed, sinuosity and turning angle are derived, and explore the relationship between this temporal scale and uncertainty in the individual data points making up a trajectory. A very rich dataset, representing the movement of 10 cows over some two days every 0.25 s is investigated. Our cross‐scale analysis shows firstly, that movement parameters for all 10 cows are broadly similar over a range of scales when the data are segmented to remove quasi‐static subtrajectories. However, by exploring realistic values of GPS uncertainty using Monte Carlo Simulation, it becomes apparent that fine scale measurement of all movement parameters is masked by uncertainties, and that we can only make meaningful statements about movement when we take these uncertainties into account.     

Does environmental data increase the accuracy of land use and land cover classification?

Optical image classification converts spectral data into thematic information from the spectral signature of each object in the image. However, spectral separability is influenced by intrinsic characteristics of the targets, as well as the characteristics of the images used. The classification process will present more reliable results when aspects associated with natural environments (climate, soil, relief, water, etc.) and anthropic environments (roads, constructions, urban area) begin to be considered, as they determine and guide land use and land cover (LULC). The objectives of this study are to evaluate the integration of environmental variables with spectral variables and the performance of the Random Forest algorithm in the classification of Landsat-8 OLI images, of a watershed in the Eastern Amazon, Brazil. The classification process used 96 predictive variables, involving spectral, geological, pedological, climatic and topographic data and Euclidean distances. The selection of variables to construct the predictive models was divided into two approaches: (i) data set containing only spectral variables, and (ii) set of environmental variables added to the spectral data. The variables were selected through nonlinear correlation analysis, with the Randomized Dependence Coefficient and the Recursive Feature Elimination (RFE) method, using the Random Forest classifier algorithm. The spectral variables NDVI, bands 2, 4, 5, 6 and 7 of the dry season and band 4 of the rainy season were selected in both approaches (i and ii). The Euclidean distance from the urban area, Arenosol soil class, annual precipitation, precipitation in February and precipitation of the wettest quarter were the variables selected from the auxiliary data set. This study showed that the addition of environmental data to the spectral data reduces the limitation of the latter, regarding the discrimination of the different classes of LULC, in addition to improving the accuracy of the classification. The addition of soil classes to spectral variables provided a reduction in errors for vegetation classification (Evergreen Forest and Cerrado Sensu Stricto), as it was able to inform about nutrient availability and water storage capacity. The study demonstrates that the addition of environmental variables to the spectral variables can be an alternative to improve monitoring in areas of ecotone in Neotropical regions.     

How large is Aroostook County? Exploring the historical mutability of US county area measurements

Area measurements play a prominent role in many types of geographical and historical analyses. The accuracy and precision of such measurements, however, are dependent on a host of input factors, including the type of instrumentation used as well as the scale and projection of the source map and its level of cartographic generalization. As a consequence of variability in these inputs, published area measures change over time. This paper reviews the record of land area measurements for counties published by the US Census Bureau between 1880 and 2000, documents the major changes in measurement instrumentation and procedures employed, and analyzes patterns of the greatest variability in reported values. Two states are used as case studies (Iowa and Maine). The results suggest that the greatest variability occurs in those areas with irregular borders, thus reflecting the interaction of the source maps with human measurement error and the encoding capabilities of the area measurement instruments. The observations also indicate that variability in area measurements for counties has decreased over time.     

Dynamics of inequality and growth in Europe: may spatial models solve the puzzle?

Journal of Geographical Systems - The link between income inequality and economic growth remains poorly understood. The global economic crisis challenged numerous growth studies by highlighting...     

Do choropleth maps linked with parallel coordinates facilitate an understanding of multivariate spatial characteristics?

Our study has three objectives. We want to investigate (1) whether choropleth maps linked with parallel coordinates help people understand the locations of vulnerable places and the factors making these places vulnerable, (2) whether sparklines that imitate the polylines from a parallel coordinate plot support the understanding of the information provided in that plot, and (3) whether a multiple-view geovisualization approach might be intuitive and useful also for nonexperts. Although we base our work on the functionalities available in the tool called ‘ViewExposed,’ we intend to outline more general conclusions on whether multiple linked views facilitate the understanding of multivariate spatial characteristics. An empirical study with 53 individuals was conducted to obtain insights on these objectives. Our task-based assessment considered the ways in which participants understood the dynamic linking capabilities. Some of the key findings are as follows: (1) even nonexpert users are able to use parallel coordinates linked with choropleth maps; however, only those with extensive inference-making abilities will do so efficiently; (2) parallel coordinates sparklines work as an independent component facilitating the understanding of multivariate data; and (3) the intuitive nature of a multiple-view geovisualization tool is not enough to encourage people to use it.     

Economic convergence or divergence? Modeling the interregional dynamics of EU regions, 1985–1999

In this paper we start from a continuous time framework derived from the classical predator-prey model in order to analyze the recent dynamics of regional evolution in the EU. The model describes a system of interrelated units obeying a complex functional dynamics that at any moment may encompass divergent forces. After briefly reviewing the modeling framework presented elsewhere (Arbia and Paelinck 2002), we consider the dynamics of per-capita income in 119 NUTS-2 European regions in the years 1985–1999, and fit the model using Simultaneous Dynamic Least Squares (Paelinck 1996b). The model is shown to fit the empirical data well. The – potential divergence – leads up to a formidable coordination task for public authorities responsible for regional policies, as they are supposed – if their aim is absolute convergence in an economic sense, meaning equalizing incomes per head – to produce at any time a consistent set of regional policies for all the regions concerned, and not only for the less developed ones.