Geographic Datasets Research Articles

Calibration‐induced uncertainty of the EPIC model to estimate climate change impact on global maize yield

AbstractUnderstanding the interactions between agricultural production and climate is necessary for sound decision‐making in climate policy. Gridded and high‐resolution crop simulation has emerged as a useful tool for building this understanding. Large uncertainty exists in this utilization, obstructing its capacity as a tool to devise adaptation strategies. Increasing focus has been given to sources of uncertainties for climate scenarios, input‐data, and model, but uncertainties due to model parameter or calibration are still unknown. Here, we use publicly available geographical data sets as input to the Environmental Policy Integrated Climate model (EPIC) for simulating global‐gridded maize yield. Impacts of climate change are assessed up to the year 2099 under a climate scenario generated by HadEM2‐ES under RCP 8.5. We apply five strategies by shifting one specific parameter in each simulation to calibrate the model and understand the effects of calibration. Regionalizing crop phenology or harvest index appears effective to calibrate the model for the globe, but using various values of phenology generates pronounced difference in estimated climate impact. However, projected impacts of climate change on global maize production are consistently negative regardless of the parameter being adjusted. Different values of model parameter result in a modest uncertainty at global level, with difference of the global yield change less than 30% by the 2080s. The uncertainty subjects to decrease if applying model calibration or input data quality control. Calibration has a larger effect at local scales, implying the possible types and locations for adaptation.

Spatially-explicit models of global tree density.

Remote sensing and geographic analysis of woody vegetation provide means of evaluating the distribution of natural resources, patterns of biodiversity and ecosystem structure, and socio-economic drivers of resource utilization. While these methods bring geographic datasets with global coverage into our day-to-day analytic spheres, many of the studies that rely on these strategies do not capitalize on the extensive collection of existing field data. We present the methods and maps associated with the first spatially-explicit models of global tree density, which relied on over 420,000 forest inventory field plots from around the world. This research is the result of a collaborative effort engaging over 20 scientists and institutions, and capitalizes on an array of analytical strategies. Our spatial data products offer precise estimates of the number of trees at global and biome scales, but should not be used for local-level estimation. At larger scales, these datasets can contribute valuable insight into resource management, ecological modelling efforts, and the quantification of ecosystem services.

Identifying Environmental and Human Factors Associated With Tick Bites using Volunteered Reports and Frequent Pattern Mining

AbstractTick populations and tick‐borne diseases like Lyme borreliosis have been steadily increasing since the mid‐1990s. Realizing the threat that ticks pose to public health, two Dutch citizen science projects have collected tick bite reports since 2006. This unique volunteered geographical dataset, which currently has nearly 35,000 reports, was used to identify environmental and other circumstantial factors associated with tick bites. For this, we first enriched the tick bite reports with temperature, precipitation, vegetation and volunteered data associated with the location of the tick bite. Using this enriched dataset, we then derived a series of features to characterize the environmental and volunteer‐related conditions in which each tick bite occurred. Next, we discretized these features using the Jenks Natural Breaks algorithm and, after that, we mined frequent environmental patterns associated with tick bites using the AprioriClose algorithm. Finally, we checked that these patterns are specifically associated with the tick bites by comparing them with the frequent patterns mined from pseudo‐random locations. The frequent patterns were visualized using heat maps and ring maps and two representative patterns associated with tick bites were projected into geographic space to study their spatio‐temporal distribution. Our results show that factors linked to human activity are more relevant to model tick bites than seasonal accumulations of temperature, vegetation or precipitation. In particular, the number of warm and dry days per season are present in a significant number of patterns and the majority of tick bites are produced within a distance of half a kilometer of a forest, recreational or built‐up area. The study of patterns in the time‐series revealed that there are several persistent patterns consistently occurring each year and the validation process showed that the volunteer tick bites collection is capturing environmental conditions associated with tick bites, suggesting that these reports have a high scientific value. These results support the creation of a Dutch tick bite risk map that, in turn, will open the door to the design of public health interventions to reduce the incidence of Lyme disease.

A constructability assessment method (CAM) for sustainable division of land parcels

Historically the land development process has lacked a decision support structure for evaluating undeveloped parcels of land for compatibility with land use policy and engineering constraints. This paper demonstrates an applied multi-criteria decision support structure for characterizing the spatial distribution and classification of a parcel’s potential to support residential lot construction. This support structure is based on parcel attributes quantified in a typical site feasibility report, to include: potential house yield, wetlands area, soil types, streams (surface drains), and steep slope areas. The analytical capabilities of geographic information system (GIS) are employed in the decision support structure named the constructability assessment method (CAM). CAM integrates a dynamic multi-criteria attribute assessment method, based on the Analytical Hierarchy Process (AHP), for a given set of administrative requirements, and engineering constraints and judgment.The results of a case study using CAM characterized the approximate location of ideal lots for homes construction in an R-1 zoning district located on a 1290 acre land parcel in Loudoun County, VA, while avoiding existing hydric soils, floodplains, steep slopes, and forested areas. The number of ideal lots for a given set of engineering and administrative constraints represented a 65% reduction from the maximum lots permitted by regulatory constraints alone. The methodology used in this case study provides a consistent and repeatable land parcel analysis technique for undeveloped land parcels, and can be adapted and/or extended to a number of similar publicly available geographic datasets and constraint. In estimating optimal development density, CAM meets the needs of zoning administrators as well as the developers, thus offering a demand-driven market-based solution for sustainable land development.

Challenges and experiences in using heterogeneous, geo-referenced data for automatic creation of driving simulator environments

For the development of advanced driving assistance and automation systems the simulation plays an important role. Urban areas get increasing emphasis, especially in the context of future Car2X-communication. This article describes an approach developed in the project Virtual World. Its goal is to model such virtual three-dimensional (3D) environments and logical road descriptions automatically based on a tool chain from heterogeneous geographic datasets (e.g., cadastral data, road surveying, aerial pictures, and crowd-sourced data). As proof of concept, the urban area of Braunschweig, Germany, was chosen. The article focuses on the generation of a 3D city model with corresponding road network description suitable for driving and traffic simulations as used in research and industry. The article gives technical descriptions of the major work steps and discusses issues regarding the availability of data. It concludes with the current project outcome and further development in the project.

Detecting errors in coastal databases using Bayesian Networks

ABSTRACT Le Cozannet, G.; Bulteau, T., Garcin, M., Garnier, C., Muller, H., Hoareau, A. and Mallet, C., 2016. Detecting errors in coastal databases using Bayesian Networks. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 1162 - 1166. Coconut Creek (Florida), ISSN 0749-0208. With growing concerns regarding coastal planning, management and adaptation, coastal observatories are collecting an increasing amount of geographical datasets such as historical shorelines, descriptions of hydrodynamic processes, coastal geomorphology or hydrogeology. Evaluating the integrity of such databases is difficult in practice due to the volume and heterogeneity of data, as well as the number of persons involved in data collection and management. Here, we adress the question of error detection in coastal databases. We test the utility of Bayesian Networks to guide user...

Privacy Considerations for Duplicate Points in Masked Geodata

GIScience 2016 Short Paper Proceedings Privacy Considerations for Duplicate Points in Masked Geodata D. E. Seidl 1 Department of Geography, San Diego State University – UC Santa Barbara, 5500 Campanile Drive, San Diego, CA 92182 Email: dseidl@mail.sdsu.edu Abstract Reversal of a geomasking procedure can stem from the decryption of just a few points in a data set. This study explores the risks to privacy from the existence of duplicate data coordinates, and how such points are treated differently according to masking technique. Analysis of duplicates is conducted on a sample of urban foreclosure data, though the presence of duplicates should be considered on a case-by-case basis before releasing a masked data set. A nearest neighbour distance calculation for multi-unit parcels is recommended for weighting displacement distances in masking procedures on geodata with duplicate coordinates. 1. Introduction Geomasking techniques, which alter point distributions to protect privacy, are seeing increased use in public-facing applications. The citizen science site, iNaturalist, for instance, allows users to upload species observations with coordinates randomized within a 0.2 by 0.2 degree area (http://www.inaturalist.org/pages/help) (retrieved May 10, 2016). With wider use of masked geographic data, there is greater potential for users to decrypt masking techniques and ascertain original locations. The presence of duplicate sets of points at the same coordinates can pose differential risks to privacy when those points are masked. Early on in geomasking studies, researchers warned that releasing multiple versions of masked data could result in the reversal of the masking procedure (Armstrong, Rushton, and Zimmerman 1999). Zimmerman and Pavlik (2008) later demonstrated that the release of multiple masked data sets increases reverse engineering probability, since randomized points converge around original locations. Others have noted that if an adversary is able to determine the distance threshold used in masking point data, it might be possible to re- identify original locations (Zhang et al. 2015). A less-explored possibility is that multiple releases of points within a single data set—i.e. duplicate points—could reveal additional information about housing type, which could subsequently be used to uncover household identities. This is particularly true if that housing type is rare, or an anomaly in the study area. This study explores the privacy risks associated with the presence of duplicate points in a geographic data set and summarizes how the treatment of duplicate points varies by masking technique. For example, data representing different households may be matched to the same latitude and longitude if the data subjects live in the same apartment building or residential parcel. Foreclosure data are an example of how sensitive data about disparate households may be tied to the same coordinates. Multiple foreclosures in the same building will generally geocode to the same location. The treatment of duplicate points by a masking technique can adversely impact cluster detection, or increase the risk of household re-identification. Figure 1 demonstrates how maintaining a set of duplicates together when masked impacts the privacy risk when a map user is informed of auxiliary housing geodata. Duplicate points in the masked data would suggest a common origin in the same housing parcel, and if there is only a single multi-unit residential parcel nearby, an adversary may make an educated guess

Ease vs. Noise: On the Conflicting Effects of Transportation Infrastructure

Many transport technologies cause a gnot ]in ]my ]backyard h (NIMBY) reaction of locals in that they often oppose the nearby location of necessary infrastructure despite benefiting from greater mobility. We employ quasi ]experimental research methods to disentangle the offsetting noise and accessibility effects of one technology of mass transit, metro rail, analyzing land price effects of the opening of the first German electrified metro rail line in Berlin in 1902. Examining a long ]run micro ]geographic data set, we find that, ceteris paribus, a 1 km reduction in distance from the nearest station increases land prices (house prices) by 21% (5%), while a 10 db increase in noise depreciates land prices (house prices) by 5% (1%). We show that these effects are underestimated by 40% (access) to 80% (noise) if they are not estimated conditional on each other. A complementary analysis of recent property transactions data suggests that preferences for accessibility have remained quite stable over the 20th century, while noise sensitivity increased substantially. The case for constructing underground as opposed to elevated rail lines is therefore stronger today than a century ago.

Managing and Updating Geographical Data: Issues Along the Hierarchical Chain?

GIScience 2016 Short Paper Proceedings Managing and Updating Geographical Data: Issues Along the Hierarchical Chain? K. Ooms 1 , J. Crompvoets 2 , P. De Maeyer 1 , P. Lambert 3 , E. Mannens 3 , N. Van de Weghe 1 , S. Verstockt 3 , P. Viaene 1 Department of Geography, Ghent University, Krijgslaan 281 (S8), 9000 Ghent, Belgium Email: {kristien.ooms;philippe.demaeyer;nico.vandeweghe;pepijn.viaene}@ugent.be KU Leuven Public Governance Institute, Parkstraat 45 (box 3609), 3000 Leuven, Belgium Email: joep.crompvoets@soc.kuleuven,be Data Science Lab, Ghent Univeristy - iMinds, Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium Email: {peter.lambert;erik.mannens;steven.verstockt}@ugent.be Abstract This article describes the needs and challenges (technical, juridical and governance) related to exchanging, managing and updating a large scale geographic reference dataset. The focus is placed on a specific case, namely the large scale reference frame of Flanders, the GRB. Furthermore, new challenges and needs for the future are considered. 1. Introduction Nowadays most countries have a detailed digital geographic dataset covering their territory (Carpenter & Snell 2013). To manage this dataset, it is integrated in the country’s hierarchical administrative structure, with specific rules regarding who can use, manage and update the information. This is embedded in framework, which is often referred to as a Spatial Data Infrastructure (SDI) (e.g. Crompvoets et al. 2004). These SDIs have proven to be especially challenging when considering the interplay between the different governments and other actors within a country, each with their own authorities and responsibilities (Jacoby et al. 2002; Warnest 2005). Nevertheless, the development of (national) SDIs is not new, but a shift has been noticed in its main goal from data to data use (Williamson et al. 2006). 1.1 GRB – the Large Scale Reference Frame in Flanders In Flanders, the dataset which serves as the large scale reference frame is called GRB (Grootschalig Referentiebestand). Its specifications are imposed by the Flemish Government (objects to be included, metadata, finances, use, management, maintenance, etc.) (Ministerie van de Vlaamse Gemeenschap 2004; AGIV 2014), and is managed by Informatie Vlaanderen (IV), a governmental institution. Recently, access to this detailed dataset has been opened up to a wider public, including architects, notaries, surveyors, intercommunal, federal government, etc. (Informatie Vlaanderen 2015). Since January 1 st 2015, the GRB became obligatory as a reference frame for its users to facilitate the exchange of large scale geographical data. This means that the users of the reference frame will superimpose their own (thematic) data layers on top of this reference frame. 1.2 Top Level Updates vs Low Level Mismatches When dealing with geographic information that covers a territory, keeping it up-to-date across all related instances can be problematic (Jing et al. 2014). The Flemish Government is responsible for maintaining the dataset of the GRB. Updates can be divided into two groups: (1) a change in the real situation (splitting or merging a parcel, change in road infrastructure, adding a building, etc.); (2) quality improvements (e.g. more accurate measurements,

The Geography of Linguistic Diversity and the Provision of Public Goods

This paper theoretically analyzes and empirically investigates the importance of local interaction between individuals of different linguistic groups for the provision of public goods at the national level. Depending on whether local interaction mitigates or reinforces antagonism towards other groups, the micro-founded theory we develop predicts that a country's provision of public goods (i) decreases in its overall linguistic fractionalization, and (ii) either increases or decreases in how much individuals locally learn about other groups. After constructing a 5 km by 5 km geographic dataset on language use for 223 countries, we compute measures of overall fractionalization and local learning, and investigate their relation to public good provision at the country level. While overall fractionalization worsens outcomes, we find a positive causal relation between local learning and public goods. Local mixing therefore mitigates the negative impact of a country's overall linguistic fractionalization. An IV strategy shows that this result is not driven by the possible endogenous spatial distribution of language speakers within countries.

Hybrid terrain rendering based on the external edge primitive

ABSTRACTHybrid terrain models combine large regular data sets and high-resolution irregular meshes [triangulated irregular network (TIN)] for topographically and morphologically complex terrain features such as man-made microstructures or cliffs. In this paper, a new method to generate and visualize this kind of 3D hybrid terrain models is presented. This method can integrate geographic data sets from multiple sources without a remeshing process to combine the heterogeneous data of the different models. At the same time, the original data sets are preserved without modification, and, thus, TIN meshes can be easily edited and replaced, among other features. Specifically, our approach is based on the utilization of the external edges of convexified TINs as the fundamental primitive to tessellate the space between both types of meshes. Our proposal is eminently parallel, requires only a minimal preprocessing phase, and minimizes the storage requirements when compared with the previous proposals.

Indexing large geographic datasets with compact qualitative representation

ABSTRACTThis paper develops a new mechanism to efficiently compute and compactly store qualitative spatial relations between spatial objects, focusing on topological and directional relations for large datasets of region objects. The central idea is to use minimum bounding rectangles (MBRs) to approximately represent region objects with arbitrary shape and complexity and only store spatial relations that cannot be unambiguously inferred from the relations of corresponding MBRs. We demonstrate, both in theory and practice, that our approach requires considerably less construction time and storage space, and can answer queries more efficiently than the state-of-the-art methods.

Graphical Inference in Geographical Research

Graphical inference, a process refined by Buja et al., can be a useful tool for geographers as it provides a visual and spatial method to test null hypotheses. The core idea is to generate sample datasets from a null hypothesis to visually compare with the actual dataset. The comparison is performed from a line‐up of graphs where a single graph of the actual data is hidden among multiple graphs of sample data. If the real data is discernible, the null hypothesis can be rejected. Here, we illustrate the utility of graphical inference using examples from climatology, biogeography, and health geography. The examples include inferences about location of the mean, change across space and time, and clustering. We show that graphical inference is a useful technique to answer a broad range of common questions in geographical datasets. This approach is needed to avoid the common pitfalls of “straw man” hypotheses and “p‐hacking” as datasets become increasingly larger and more complex.

Anomalous behavior detection in single-trajectory data

This paper presents an original approach to dynamic anomalous behavior detection in individual trajectory using a recursive Bayesian filter. The anomalous pattern detection is of great interest for navigation, driver assistance systems, surveillance as well as crisis management. In this work, we focus on the GPS trajectories of automobiles finding where the driver’s behavior shows anomalies. Such anomalous behaviors can happen in many cases, especially when the driver encounters orientation problems, i.e., taking a wrong turn, performing a detour, or losing the way. First, three high-level features, i.e., turns and their density, detour factor, and route repetition are extracted from the given trajectory geometry, for which a long-term perspective is required to observe data sequences of a significant length instead of individual time stamps. We therefore employ high-order Markov chains with a ‘dynamic memory’ to model the trajectory integrating these long-term features. The Markov model is processed by a proposed recursive Bayesian filter to infer an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The filter performs unsupervised detection in single trajectories based on local features only. No training process is required to characterize the anomalous behaviors. By analyzing the detection results of individual trajectories, collective behaviors can be derived indicating traffic issues such as congestions and turn restrictions. Experiments are performed on volunteered geographic information (VGI) data, self-acquired trajectories, and open trajectory datasets to demonstrate the potential of the proposed approach.

Evolutionary plant radiations: where, when, why and how?

Evolutionary plant radiations: where, when, why and how?

Modelling, encoding and transforming of open geographic data to examine interoperability between GIS applications

The interoperability of geographic data sets is required between geo-information system applications to use geographic data sets effectively. Thus, international standardisation organisation/technical committee 211 standards supporting model-driven architecture were examined to test the ability of producing interoperable geographic data sets. With Turkey case, unified modelling language application schemas were designed for base geographic data themes and encoded to data interchange model based on geography markup language. To test the applicability of the open data models, extract-transform-load (ETL) tools were developed and applied for case applications such as topographic map and web urban atlas. This study gives a methodology and indicates that ETL tools should be created to enable multiple uses of geo-data sets without spending time and labour. However, data model design should be refined and kept as simple as possible because data transformation is laborious to use the models in the applications.

On redundant topological constraints

Redundancy checking is an important task in the research of knowledge representation and reasoning. In this paper, we consider redundant qualitative constraints. For a set Γ of qualitative constraints, we say a constraint (xRy) in Γ is redundant if it is entailed by the rest of Γ. A prime subnetwork of Γ is a subset of Γ which contains no redundant constraints and has the same solution set as Γ. It is natural to ask how to compute such a prime subnetwork, and when it is unique. We show that this problem is in general intractable, but becomes tractable if Γ is over a tractable subalgebra S of a qualitative calculus. Furthermore, if S is a subalgebra of the Region Connection Calculus RCC8 in which weak composition distributes over nonempty intersections, then Γ has a unique prime subnetwork, which can be obtained in cubic time by removing all redundant constraints simultaneously from Γ. As a by-product, we show that any path-consistent network over such a distributive subalgebra is minimal and globally consistent in a qualitative sense. A thorough empirical analysis of the prime subnetwork upon real geographical data sets demonstrates the approach is able to identify significantly more redundant constraints than previously proposed algorithms, especially in constraint networks with larger proportions of partial overlap relations.

Estimating the count of completeness errors in geographic data sets by means of a generalized Waring regression model

In this article, we propose a statistical model for estimating the probable number of completeness errors (omissions plus commissions) in a cell (a map tile or cluster) of a data set to guide updating or improvement efforts. The number of completeness errors is a count data variable related to some exogenous covariates that may also be known for each cell (e.g. count of features, rural or urban typology, etc.) and to other unknown variation sources. We propose and adjust a generalized Waring regression model for counting these errors in cells of 1 × 1 km2 on the Topographic Map of Andalusia (Spain). This model is compared with the Poisson regression model and the negative binomial regression model and performs better. The empirical relationship established by the model indicates that the number of completeness errors is related to the following exogenous covariates: the number of cartographic features of the data set, the fact that the cell covers a littoral or urban zone and the spatial division of the contracted suppliers. For cells having less than 5 errors, most of the variability corresponds to unknown external factors (liability), but when the number of errors rises, the greater part of the variability is due to unknown internal characteristics of each cell (proneness). With these estimations, the producer can derivate statistical summaries and spatial representations and develop better planning of production activities such as actualization.

Developing an open geographic data model and analysis tools for disaster management: landslide case

Abstract. Disaster management aims to reduce catastrophic losses of disasters. Geographic information technologies support disaster management activities for effective and collaborative data management considering the complex nature of disasters. This study with an original conceptual approach aims to develop interoperable geographic data model and analysis tools to manage geographic data sets coming from different sources. For landslide disaster, 39 scenario-based activities were analysed with the required data according to user needs in a cycle of activities at mitigation, preparedness, response, and recovery phases. An interoperable geographic data model for disaster management (ADYS), enabling up-to-date exchange of geographic data, was designed, compliant with the standards of ISO/TC211 Geographic Information/Geomatics, Open Geospatial Consortium (OGC), and the Turkish National GIS (TUCBS). An open source and free analysis toolbox was developed and tested in the case study of activities such as landslide hazard analysis and a disaster warning system to support the Provincial Disaster Management Centres of Turkey. Open data models and analysis tools make effective activity management and data sharing possible. However, transforming data sets into data exchange formats is laborious.

Ocena primernosti uporabe prostorskih podatkovnih nizov

Ocena primernosti uporabe prostorskih podatkovnih nizov