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.     

Exploring the spatially varying innovation capacity of the US counties in the framework of Griliches’ knowledge production function: a mixed GWR approach

Griliches’ knowledge production function has been increasingly adopted at the regional level where location-specific conditions drive the spatial differences in knowledge creation dynamics. However, the large majority of such studies rely on a traditional regression approach that assumes spatially homogenous marginal effects of knowledge input factors. This paper extends the authors’ previous work (Kang and Dall’erba in Int Reg Sci Rev, 2015. doi: 10.1177/0160017615572888) to investigate the spatial heterogeneity in the marginal effects by using nonparametric local modeling approaches such as geographically weighted regression (GWR) and mixed GWR with two distinct samples of the US Metropolitan Statistical Area (MSA) and non-MSA counties. The results indicate a high degree of spatial heterogeneity in the marginal effects of the knowledge input variables, more specifically for the local and distant spillovers of private knowledge measured across MSA counties. On the other hand, local academic knowledge spillovers are found to display spatially homogenous elasticities in both MSA and non-MSA counties. Our results highlight the strengths and weaknesses of each county’s innovation capacity and suggest policy implications for regional innovation strategies.     

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.     

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.     

The utility of travelling when destinations are heterogeneous. How much better is the next destination as one travels further?

In many studies travel behaviour (for example, commuting) is analysed on the basis of a utility function with the distance (d) travelled as one of the arguments. An example is U=U(d,Y-cd,T-td) where Y and T denote money and time constraints, and c and t money and time costs per unit distance. This standard approach is not without problems, however, since it ignores the fundamental fact that most transport has a derived character: travelling kilometres is not an activity that gives utility per se, but only because these kilometres bring people to certain places they want to visit. In this paper we develop a method that provides a justification for utility functions such as shown here by showing that these can be made consistent with theories that take into account the derived character of transport.     

Using DORIS measurements for modeling the vertical total electron content of the Earth’s ionosphere

The Doppler orbitography and radiopositioning integrated by satellite (DORIS) system was originally developed for precise orbit determination of low Earth orbiting (LEO) satellites. Beyond that, it is highly qualified for modeling the distribution of electrons within the Earth’s ionosphere. It measures with two frequencies in L-band with a relative frequency ratio close to 5. Since the terrestrial ground beacons are distributed quite homogeneously and several LEOs are equipped with modern receivers, a good applicability for global vertical total electron content (VTEC) modeling can be expected. This paper investigates the capability of DORIS dual-frequency phase observations for deriving VTEC and the contribution of these data to global VTEC modeling. The DORIS preprocessing is performed similar to commonly used global navigation satellite systems (GNSS) preprocessing. However, the absolute DORIS VTEC level is taken from global ionospheric maps (GIM) provided by the International GNSS Service (IGS) as the DORIS data contain no absolute information. DORIS-derived VTEC values show good consistency with IGS GIMs with a RMS between 2 and 3 total electron content units (TECU) depending on solar activity which can be reduced to less than 2 TECU when using only observations with elevation angles higher than \(50^\circ \) . The combination of DORIS VTEC with data from other space-geodetic measurement techniques improves the accuracy of global VTEC models significantly. If DORIS VTEC data is used to update IGS GIMs, an improvement of up to 12  % can be achieved. The accuracy directly beneath the DORIS satellites’ ground-tracks ranges between 1.5 and 3.5 TECU assuming a precision of 2.5 TECU for altimeter-derived VTEC values which have been used for validation purposes.     

Education in cartography: what is the status of young people’s map-reading skills?

Due to recent technological progress, maps have become more popular than ever before. This is especially true for young people, who interact with these technologies on a daily basis. Therefore, it is essential that these potential map users possess sufficient knowledge and skills to process the content of cartographic products. A user study was conducted during which pupils (aged 11–18 years) and geography students (>18 years) had to solve a number of cartography questions using topographic maps. The data were analyzed statistically, taking into account a number of potentially influencing factors (user characteristics) on the participants’ results: age, gender, youth club membership, knowledge about the area, among others. The results show a rising trend in the pupils’ scores with increasing age, which can be explained by education in cartography at school. Geography students perform much better, but no influence of any other user characteristics was detected. For pupils, knowledge about the area and gender might be considered as influencing factors. However, the detected influence of gender depends on the scoring system.     

How Reliable are Citizen‐Derived Scientific Data? Assessing the Quality of Contrail Observations Made by the General Public

Citizen science projects encourage the general public to participate in scientific research. Participants can contribute large volumes of data over broad spatial and temporal frames; however, the challenge is to generate data of sufficient quality to be useable in scientific research. Most observations made by citizen‐scientists can be independently verified by “experts”. However, verification is more problematic when the phenomena being recorded are short‐lived. This article uses a GIS methodology to verify the quality of contrail observations made by the general public as part of the OPAL Climate Survey. We verify observations using datasets derived from a variety of different sources (experts, models and amateur enthusiasts) with different spatial and temporal properties which reflect the complex 3D nature of the atmosphere. Our results suggest that ～70% of citizen observations are plausible, based on favorable atmospheric conditions and the presence or absence of aircraft; a finding which is in keeping with other, more conventional citizen science projects. However, questions remain as to why the quality of the citizen‐based observations was so high. Given the lack of supporting data on observers, it is impossible to determine whether the dataset was generated by the activities of many participants or a small but dedicated number of individual observers.     

How much variation in land surface phenology can climate oscillation modes explain at the scale of mountain pastures in Kyrgyzstan?

Climate oscillation modes can shape weather across the globe due to atmospheric teleconnections. We built on the findings of a recent study to assess whether the impacts of teleconnections are detectable and significant in the early season dynamics of highland pastures across five rayons in Kyrgyzstan. Specifically, since land surface phenology (LSP) has already shown to be influenced by snow cover seasonality and terrain, we investigated here how much more explanatory and predictive power information about climatic oscillation modes might add to explain variation in LSP. We focused on seasonal values of five climate oscillation indices that influence vegetation dynamics in Central Asia. We characterized the phenology in highland pastures with metrics derived from LSP modeling using Landsat NDVI time series together with MODIS land surface temperature (LST) data: Peak Height (PH), the maximum modeled NDVI and Thermal Time to Peak (TTP), the quantity of accumulated growing degree-days based on LST required to reach PH. Next, we calculated two metrics of snow cover seasonality from MODIS snow cover composites: last date of snow (LDoS), and the number of snow covered dates (SCD). For terrain features, we derived elevation, slope, and TRASP index as linearization of aspect. First, we used Spearman’s rank correlation to assess the geographical differentiation of land surface phenology metrics responses to environmental variables. PH showed weak correlations with TTP (positive in western but negative in eastern rayons), and moderate relationships with LDoS and SCD only in one northeastern rayon. Slope was weakly related to PH, while TRASP showed a consistent moderate negative correlation with PH. A significant but weak negative correlation was found between PH and SCAND JJA, and a significant weak positive correlation with MEI MAM. TTP showed consistently strong negative relationships with LDoS, SCD, and elevation. Very weak positive correlations with TTP were found for EAWR DJF, AMO DJF, and MEI DJF in western rayons only. Second, we used Partial Least Squares regression to investigate the role of oscillation modes altogether. PLS modelling of TTP showed that thermal time accumulation could be explained mostly by elevation and snow cover metrics, leading to reduced models explaining 55 to 70% of observed variation in TTP. Variable selection indicated that NAO JJA, AMO JJA and SCAND MAM had significant relationships with TTP, but their input of predictive power was neglible. PLS models were able to explain up to 29% of variability in PH. SCAND JJA and MEI MAM were shown to be significant predictors, but adding them into models did not influence modeling performance. We concluded the impacts of climate oscillation anomalies were not detectable or significant in mountain pastures using LSP metrics at fine spatial resolution. Rather, at a 30 m resolution, the indirect effects of seasonal climatic oscillations are overridden by terrain influences (mostly elevation) and snow cover timing. Whether climate oscillation mode indices can provide some new and useful information about growing season conditions remains a provocative question, particularly in light of the multiple environmental challenges facing the agropastoralism livelihood in montane Central Asia.     

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.     

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.     

Is the long-term variation of the estimated GPS differential code biases associated with ionospheric variability?

The global positioning system (GPS) differential code biases (DCB) provided by the International GNSS Service (IGS) show solar-cycle-like variation during 2002–2013. This study is to examine whether this variation of the GPS DCBs is associated with ionospheric variability. The GPS observations from low earth orbit (LEO) satellites including CHAMP, GRACE and Jason-1 are used to address this issue. The GPS DCBs estimated from the LEO-based observations at different orbit altitudes show a similar tendency as the IGS DCBs. However, this solar-cycle-like dependency is eliminated when the DCBs of 13 continuously operating GPS satellites are constrained to zero-mean. Our results thus revealed that ionospheric variation is not responsible for the long-term variation of the GPS DCBs. Instead, it is attributed to the GPS satellite replacement with different satellite types and the zero-mean condition imposed on all satellite DCBs.     

How to compare movement? A review of physical movement similarity measures in geographic information science and beyond

In geographic information science, a plethora of different approaches and methods is used to assess the similarity of movement. Some of these approaches term two moving objects similar if they share akin paths. Others require objects to move at similar speed and yet others consider movement similar if it occurs at the same time. We believe that a structured and comprehensive classification of movement comparison measures is missing. We argue that such a classification not only depicts the status quo of qualitative and quantitative movement analysis, but also allows for identifying those aspects of movement for which similarity measures are scarce or entirely missing.

In this review paper we, first, decompose movement into its spatial, temporal, and spatiotemporal movement parameters. A movement parameter is a physical quantity of movement, such as speed, spatial path, or temporal duration. For each of these parameters we then review qualitative and quantitative methods of how to compare movement. Thus, we provide a systematic and comprehensive classification of different movement similarity measures used in geographic information science. This classification is a valuable first step toward a GIS toolbox comprising all relevant movement comparison methods.     

How to Apply the Geospatial Data Abstraction Library (GDAL) Properly to Parallel Geospatial Raster I/O?

Input/output (I/O) of geospatial raster data often becomes the bottleneck of parallel geospatial processing due to the large data size and diverse formats of raster data. The open‐source Geospatial Data Abstraction Library (GDAL), which has been widely used to access diverse formats of geospatial raster data, has been applied recently to parallel geospatial raster processing. This article first explores the efficiency and feasibility of parallel raster I/O using GDAL under three common ways of domain decomposition: row‐wise, column‐wise, and block‐wise. Experimental results show that parallel raster I/O using GDAL under column‐wise or block‐wise domain decomposition is highly inefficient and cannot achieve correct output, although GDAL performs well under row‐wise domain decomposition. The reasons for this problem with GDAL are then analyzed and a two‐phase I/O strategy is proposed, designed to overcome this problem. A data redistribution module based on the proposed I/O strategy is implemented for GDAL using a message‐passing‐interface (MPI) programming model. Experimental results show that the data redistribution module is effective.     

UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy?

High quality data on plant species occurrence count among the essential data sources for ecological research and conservation purposes. Ecologically valuable small grain mosaics of heterogeneous shrub and herbaceous formations however pose a challenging environment for creating such species occurrence maps. Remote sensing can be useful for such purposes, it however faces several challenges, especially the need of ultra high spatial resolution (centimeters) data and distinguishing between plant species or genera. Unmanned aerial vehicles (UAVs) are capable of producing data with sufficient resolution; their use for identification of plant species is however still largely unexplored. A fusion of spectral data with LiDAR-derived vertical information can improve the classification accuracy, such a solution is however costly. A cheaper alternative of vertical data acquisition can be represented by the use of the structure-from-motion photogrammetry (SfM) utilizing the images taken for (multi/hyper)spectral analysis. We investigated the use of such a fusion of UAV-borne multispectral and SfM-derived vertical information acquired from a single sensor for classification of shrubland vegetation at species level and compared its accuracy with that derived from multispectral information only. Multispectral images were acquired using Tetracam Micro-MCA6 camera in the west of Czechia in a shrubland landscape protected within the NATURA 2000 network. Using (i) multispectral imagery only and (ii) multispectral-SfM fusion, we classified the vegetation into six classes representing four woody plant species and two meadow types. Our results prove that the multispectral-SfM fusion performs significantly better than multispectral only (88.2% overall accuracy, 85.2% mean producer’s accuracy and 85.7% mean user’s accuracy for fusion instead of 73.3%, 75.1% and 63.7%, respectively, for multispectral). We concluded that the fusion of multispectral and SfM information acquired from a single UAV sensor is a viable method for shrub species mapping.     

Brazilian spatial dynamics in the long term (1872–2000): “path dependency” or “reversal of fortune”?

This paper analyzes the spatial dynamics of Brazilian regional inequalities between 1872 and 2000 using contemporary tools. The first part of the paper provides new estimates of income per capita in 1872 by municipality using census and electoral information on income by occupation. The level of analysis is the Minimum Comparable Areas 1872–2000 developed by Reis et al. (áreas mínimas comparáveis para os períodos intercensitários de 1872 a 2000, 2007). These areas are the least aggregation of adjacent municipalities required to allow consistent geographic area comparisons between census years. In the second section of the paper, Exploratory Spatial Data Analysis, Markov chains and stochastic kernel techniques (spatially conditioned) are applied to the dataset. The results suggest that, in broad terms, the spatial pattern of income distribution in Brazil during that period of time has remained stable.