Improving the Availability of Space Research Spatial Data*

Simple or complex: Relative impact of data availability and model purpose on the choice of model types for population viability analyses

Faced with a lack of fine grain data availability, in rapidly emerging urban centers of developing nations, the study explored a mapping methodology to create thematic map of public urban green space (UGS). Using GIS, a thematic map of Nagpur city, India was prepared. The objective was to prepare spatial data that are relevant for planners and policy makers, with detailed UGS typologies and to update the status of overall availability and distribution of hierarchical recreational green spaces in the city. The spatial and non-spatial data with added attributes gathered through fieldwork resulted in a holistic dataset, with high accuracy of thematic map (0.93 kappa coefficient). The recorded status of different typologies as well as the distribution of recreational UGS shows disparity in the distribution of UGS. The eastern part of the city grossly lacks UGS provisions, which is compensated by the western part with larger availability of natural green spaces. The mapping methodology is novel and effective for recording qualitative status, analyzing their spatial distribution and prioritizing the provisions of UGS. Future research integrating these spatial data with more qualitative research can provide a holistic view on benefits of UGS provisions and thus facilitate effective UGS governance aiming towards better green infrastructure and hence broader urban sustainability.

In the Amazonia region, general policy based on sustainable development must be adopted to counterbalance natural conservation and the needs of people. To discuss and monitor the regional development requires a significant variety of spatial data. However, making data available is not simple and various solutions usually are presented in non-standardized and non-interchangeable databases. This paper presents an overview of spatial data available for the Amazonia region, and initiatives of data dissemination systems, considering various geographical scales and data access. From this general perspective, we discuss some ideas about the importance of spatial data for sustainability and suggest interactive systems that could contribute to local development and environmental conservation. The limitation on the promotion of spatial information and data sharing in Amazonia is neither computational nor technological. Rather, it is a matter of informing those who generate the data about the benefits of knowledge as a public good and promoting collaborative work. Creating a convergent spatial consciousness about the territory, and bringing together every environmental player, is a step towards the discussion and planning of the sustainable development of the Amazonia.

The utility of spatial data to delineate river riparian functions and management zones: A review

GIScience 2016 Short Paper Proceedings Deriving Locational Reference through Implicit Information Retrieval T. Hervey, W. Kuhn Department of Geography and Center for Spatial Studies, University of California, Santa Barbara, CA 93106 Email: {thomas.hervey; kuhn} @geog.ucsb.edu Abstract The often fragmented process of online spatial data retrieval remains a barrier to domain scientists interested in spatial analysis. Although there is a wealth of hidden spatial information online, scientists without prior experience querying web APIs (Application Programming Interface) or scraping web documents cannot extract this potentially valuable implicit information across a growing number of sources. In an attempt to broaden the spectrum of exploitable spatial data sources, this paper proposes an extensible, locational reference deriving model that shifts extraction and encoding logic from the user to a preprocessing mediation layer. To implement this, we develop a user interface that: collects data through web APIs and scrapers, determines locational reference as geometries, and re-encodes the data as explicit spatial information, usable with spatial analysis tools, such as those in R or ArcGIS. 1. Introduction GIS advancements have produced a growingly complex general-purpose toolbox rather than functionality tailored to domain-specific questions. Frequently, domain scientists including Green (2015: 717) highlight the salient lagging data and tool limitations associated with GIS. As Kuhn (2012: 2267) notes, it is essential to rethink the fundamentals of spatial information while promoting clarity that cuts across technical boundaries and broadens spatial literacy for non- experts. Contributing to the work by Kuhn and Ballatore (2015: 219) and Vahedi et al. (2016) to design an intuitive GIS language for question-driven spatial studies, we focus on bridging the gaps between data discovery and spatial analysis tools by broadening the spectrum of exploitable spatial data sources. Compared to the vast amount of implicit spatial data (hidden location attributes often in the form of metadata, auxiliary place names, and geotagged attributes (Heinzle and Sester 2003: 335)), there remains a relatively limited quantity of online explicit spatial data (georeferenced geometry-based features (Brisaboa et al. 2011: 358)). When available, explicit data are typically served from a limited number of administrative portals or require intensive energy and time from a user searching, exporting, encoding and cleaning before being usable (Munson 2013: 65). These preprocessing requirements limit the feasibility of question-driven spatial analysis (Vahedi et al. 2016) and force domain scientists to base their studies on data availability. It is clear that implicit spatial data is an attractive alternative. However, as the numerous research challenges associated with GIR (geographic information retrieval) suggest (Jones and Purves 2008: 219), current methods do not provide adequate solutions to navigate, gather or utilize the mass of heterogeneous implicit spatial data spread across the array of online repositories. Custom-constructed web API requests and scrapers can help retrieve and process this unpublished information. Yet, without technical expertise to build new or use existing

<p>Flood hazard prediction is a critical component of flood risk assessment, flood risk management plans, and implementation of flood mitigation measures. In the EU, there is currently a growing interest in floods caused by extreme heavy rainfall, commonly known as pluvial floods. Due to the rapid development of computational and remote sensing technology, as well as the public availability of high-resolution spatial data, pluvial floods are now simulated using integrated hydrological-hydraulic approaches consisting of time-dependent 2D numerical models and so-called rain-on-grid approaches with spatially variable infiltration. In this paper, we will present the recent progress and methodological framework for pluvial flood hazard assessment in the city of Poreč in the northern coastal part of Croatia, focusing on the interpretation and modification of spatial input data, precipitation data processing, and numerical modelling of pluvial flooding. We show what spatial data were collected and improved, what spatial data were generated, how the precipitation data were processed for this purpose, and discuss some modelling aspects specific to pluvial flooding in urban areas. Finally, we present the results of the pluvial flood hazard assessment for the city of Poreč and its catchment area and provide some recommendations for further research.</p>

Event Abstract Back to Event Effective capacity-building for spatial analyses: Risk mapping for emerging infectious diseases in Indonesia Caitlin N. Pfeiffer1*, Mark A. Stevenson1, Farida Zenal2 and Luuk Schoonman2 1 Asia Pacific Centre for Animal Health, Faculty of Veterinary Science, University of Melbourne, Australia 2 Food and Agriculture Organization of the United Nations (Indonesia), Indonesia Emerging infectious diseases are an important threat to human and animal health worldwide, and many of these diseases are zoonoses. To understand the likelihood of disease emergence, spatial analyses are extremely valuable to facilitate targeting surveillance and risk mitigation activities to high-risk geographic areas. However, collecting and collating relevant existing data to facilitate spatial analyses can be challenging. Furthermore, relevant expertise in the spatial analytical methods may not be present in the countries at greatest risk for infectious disease emergence. Indonesia is known to be at increased risk for emerging vector-borne diseases and spill-over of diseases from wildlife into human populations1. The aim of this analytical and capacity-building project was to better understand the spatial variation in emerging infectious disease risk across the country, through upskilling of in-country staff to identify, gain access to, manage and analyse relevant existing data. Relevant spatial risk data included human demographic and health information, animal population distribution and health status, historical disease outbreak locations, and known disease drivers such as environmental data including temperature, rainfall, elevation, land use change over time and greenness indices. The project will produce risk maps that can be used to effectively communicate disease emergence risk with decision-makers, to facilitate policy decisions that can safeguard human, animal and environmental health. To facilitate the in-country analysis and interpretation of the data, a series of capacity building training activities were conducted in 2019. Five workshops were delivered in Indonesia to a team of trainees comprised of Government of Indonesia employees, academics from Indonesian universities and FAO Indonesia staff. The training courses focussed on the development of technical skills, providing practical exposure to analysis of spatial data, use of advanced software including R and WinBUGS, and the principles of risk mapping. Crucially, the delivery of this material as a series of workshops staggered over a three-month period allowed participants to build their skills and confidence in the relevant techniques over time. An important feature of this capacity-building activity was engagement of staff from multiple ministries of the Government of Indonesia (a One Health approach). This provided several important benefits, including insights about the nature and quality of existing data and knowledge of local factors that could potentially influence the spread of emerging infectious diseases. Additionally, the continued attendance of the same group of trainees facilitated the development of collaborative working relationships between staff from different Government of Indonesia ministries, providing a rich foundation for future collaborative and One Health work. Relevant local examples were used throughout the training activities which enhanced the trainees’ engagement with the technically challenging content. Evaluation at the end of each workshop indicated progressive skill development and increasing theoretical understanding throughout the series of courses. This series of workshops serves as a model for empowering more countries to deliver technically advanced analyses, to better protect human and animal health. The One Health approach was very beneficial for these spatial analyses. Expertise from a variety of disciplines contributed to a thorough understanding of each dataset and facilitated the selection of appropriate techniques. As sophisticated spatial analyses become increasingly feasible (through the collection and availability of data and increasing availability of software), it is important to consider how capacity to perform these analyses can be fostered in countries with previously limited technical expertise. Expert input is needed to determine which analytical techniques might provide answers to important research and policy questions. Expertise in training and adult education is also needed, to support technical staff in developing the knowledge and skills needed to competently perform the analyses. Substantial focus on the policy relevance, effective visualisation and communication of analyses, rather than just their technical features, will be required. The delivery of effective capacity-building activities, such as the example described in this work, will help ensure spatial analyses can be meaningfully implemented in the parts of our world where they are most needed. Acknowledgements This project is part of the Emerging Pandemic Threats Phase 2 (EPT-2) programme, funded by the U.S. Agency for International Development (USAID). We thank FAO Indonesia for their support of the program and all of the participating trainees for their dedication to the workshop material.

Identifying spatial data availability and spatial data needs for Chagas disease mitigation in South America

Background: With an estimated 10.4 million incident tuberculosis (TB) cases in 2015, and the current trajectory of 1.5%/year incidence rate decline, new TB vaccines are urgently needed to help meet the WHO goal of tuberculosis elimination by 2050. However, insufficient epidemiological evidence exists to inform TB vaccine development strategies and to assist clinical trial site selection and design. Research to meet these data needs is critical to accelerate TB vaccine development. To maximise the future impact of new TB vaccines, estimates of the population-level impact of vaccine characteristics and implementation strategies are needed to inform design of TB vaccine target product profiles. To accelerate and de-risk clinical trials, appropriate epidemiological data are required to inform trial site selection, sample size calculations and recruitment strategy. However, data availability at trial sites is limited, and prospective studies are resource-intensive, so new methods are needed to collect appropriate data to inform TB vaccine trial design.
\nTo inform data-driven development strategies for new TB vaccines, the aims of this thesis were to 1) estimate the epidemiological impact of new TB vaccine characteristics and implementation in China to inform design of target product profiles; and 2) to develop a novel epidemiological spatial mapping tool capable of informing clinical trial design for new TB vaccines in low-income, high-burden settings.
\nMethods: A deterministic, age-structured, Mycobacterium tuberculosis transmission model was developed and calibrated to age-stratified epidemiological and demographic data from China. This was employed to estimate the population-level epidemiological impact of new TB vaccines over the 2025-2050 time horizon, through an exploration of potential vaccine characteristics and implementation strategies. 
\nA new methodology for empirical data collection to determine spatial distribution of TB notifications was developed. The electronic PArticipant Locator application (ePAL app) combined 3,243 community-identified points of interest with high resolution satellite maps, within an electronic tablet-based case report form. The app was integrated in to the National Tuberculosis Programme in Blantyre, Malawi, for collection of demographics, health status and coordinates of place of residence for patients ≥18 years initiating TB treatment. Accuracy of ePAL-recorded co-ordinates was evaluated against GPS coordinates obtained at the participant’s place of residence. 
\nResults: Mathematical modelling predicted a shift towards a reactivation-driven, ageing TB epidemic in China by 2050. Vaccines protective against disease, effective post-infection and providing at least 5 years protection were essential for achieving higher levels of impact. Vaccination of older adults provided greater population-level impact than vaccinating adolescents for all equivalent vaccines explored, even if much lower coverage were achieved in older adult vaccination. Recommendations for post-infection vaccines were robust to substantial reductions in efficacy and duration of protection in older adults, whereas for pre-infection vaccines in some of these scenarios adolescent vaccination may be equivalent or preferred. Vaccinating older adults with post-infection vaccines provided substantially higher impact than pre-infection vaccines.
\n1,899 TB patients were registered using ePAL in the 12-month study period, with high patient acceptance (98.7%, 1,899/1,924). ePAL achieved clinic-based collection of patient location of residence accurate to a median of 84 metres (IQR: 35-317 metres) in a high population density urban setting without a municipal address system. Advantages of the ePAL system included real-time availability of high-resolution spatial data, low set up costs, and ease of use by health staff as part of routine TB registration. Data were used to identify areas with high TB burden, potentially suitable for TB vaccine trials.
\nConclusions: The research presented in this thesis informs the development of appropriate TB vaccines and target populations to maximise future population-level impact. A prevention of disease vaccine efficacious post-infection and delivered to older adults would contribute towards maximising population-level impact in China. Adolescent-targeted tuberculosis vaccines are likely to have low impact in ageing, reactivation-driven epidemics like China, which suggests a modification of the current strategic focus on adolescents among certain funders. Clinical trials should assess disease endpoints, include M.tb-infected and older adult populations, and extend beyond the usual 2-3 years follow up. To support design of disease endpoint trials, ePAL may provide an accurate, easily implementable, low-cost tool for identification of areas of high TB burden in settings without addresses.

Geospatial data are today more and more widespread. Many different institutions, such as Geographical Institutes, Public Administrations, collaborative communities (e.g., OSM) and web companies, make available nowadays a large number of maps. Besides this cartography, projects of digitizing, georeferencing and web publication of historical maps have increasingly spread in the recent years. In spite of these variety and availability of data, information overload makes difficult their discovery and management: without knowing the specific repository where the data are stored, it is difficult to find the information required and problems of interconnection between different data sources and their restricted interoperability limit a wide utilization of available geo-data. <br><br> This paper aims to describe some actions performed to assure interoperability between data, in particular spatial and geographic data, gathered from different data providers, with different features and referring to different historical periods. The article summarizes and exemplifies how, starting from projects of historical map digitizing and Historical GIS implementation, respectively for the Lombardy and for the city of Parma, the interoperability is possible in the framework of the ENERGIC OD project. The European project ENERGIC OD, thanks to a specific component – the virtual hub – based on a brokering framework, copes with the previous listed problems and allows the interoperability between different data sources.

Event Abstract Back to Event Reconstruction of the mouse brain regions from the spatial gene expression data Alexey Tsvetkov1* 1 Russian Scientific Centre, Kurchatov Institute, Nano-, Bio-, Info-, Cognitive (NBIC) Center Understanding the structural basis of nervous system still remains one of the primary challenges in neuroscience. New perspectives for the studies of brain anatomy are emerging with the availability of large-scale spatial gene expression data. Exploration of these data promises to deliver new insights into the understanding of relations between genes and brain structure. We performed an analysis of the gene expression for more than 4000 genes in the mouse brain with the spatial resolution of 200 micrometers. These data are freely available from the Allen Brain Atlas [1] project. After data filtering and scaling, distance matrices between all brain voxels (volumetric pixels) were calculated for a number of common distance measures. We also proposed and utilized a novel distance measure based on the locality of gene expression. Then the brain volume was hierarchically clustered with a variety of techniques [2]. In order to verify the resulting brain parcellations we compared them by means of a quantitative measure to the histologically-defined anatomical reference atlas and found significant overlap between automatically derived spatial clusters and anatomical structures. We also compared brain regions by the number of genes expressed. Here the number of simultaneously activated genes was considered to be proportional to the amount of accumulated evolutionary changes. This allows us to identify dentate gyrus and adjoining hippocampal regions as the structures with the richest expression and, hence, less conservative in the evolutionary sense. Other regions of the cerebral cortex show intermediate levels of expression variability while other brain structures have accumulated fewer changes in the process of evolution, with olfactory bulbs and cerebellum being most conservative. This work was supported by Russian FTP "Scientific and scientific-pedagogical personnel". [1] Ng L., Bernard A., Lau C. et. al., "An anatomic gene expression atlas of the adult mouse brain" (2009) Nature Neuroscience, 12 (3): 356-362. [2] Xu R. and Wunsch D., "Survey of clustering algorithms" (2005) IEEE Transactions on Neural Networks, 16 (3): 645-678. Keywords: General neuroinformatics Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Tsvetkov A (2011). Reconstruction of the mouse brain regions from the spatial gene expression data. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00043 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Alexey Tsvetkov, Russian Scientific Centre, Kurchatov Institute, Nano-, Bio-, Info-, Cognitive (NBIC) Center, Moscow, alexis.tsvetkov@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Alexey Tsvetkov Google Alexey Tsvetkov Google Scholar Alexey Tsvetkov PubMed Alexey Tsvetkov Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Increasing availability of geo-spatial data, enabled by improvement of technologies in the field of spatial data collection, provides unprecedented opportunities in knowledge creation and distribution. Beside the integration of distributed spatial data, available in Spatial Data Infrastructures (SDIs), proper processing of data is crucial for knowledge discovery. This work proposes an ontology based spatial knowledge discovery framework based on Open Geospatial Consortium (OGC) standards. Data processing in geo-spatial domain is done using spatial clustering algorithm. It is implemented as the web processing service of this framework. Prediction of urban house price is taken as the case study.

Global solar radiationRgis an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records ofRgis a serious limitation in many countries,Rgis estimated using models. For crop-model application, empiricalRgmodels that use commonly measured meteorological variables, such as temperature and precipitation, are generally preferred. Although a large number of models of this kind exist, few have been evaluated for conditions in the United States. This study evaluated the performances of 16 empirical, temperature- and/or precipitation-basedRgmodels for the southeastern United States. By taking into account spatial distribution and data availability, 30 locations in the region were selected and their daily weather data spanning eight years obtained. One-half of the data was used for calibrating the models, and the other half was used for evaluation. For each model, location-specific parameter values were estimated through regressions. Models were evaluated for each location using the root-mean-square error and the modeling efficiency as goodness-of-fit measures. Among the models that use temperature or precipitation as the input variable, the Mavromatis model showed the best performance. The piecewise linear regression–based Wu et al. model (WP) performed best not only among the models that use both temperature and precipitation but also among the 16 models evaluated, mainly because it has separate relationships for low and high radiation levels. The modeling efficiency of WP was from ~5% to more than 100% greater than those of the other models, depending on models and locations.

Remote sensing (RS) has long been a useful tool in global and regional applications. The Water Footprint (WF) of a crop is defined as the volume of water consumed for its production, where green and blue WF stand for rain and irrigation water usage. RS provides new tools for global WF assessment and represents an innovative approach to regional and global irrigation mapping, enabling the estimation of green and blue water use. This paper presents an overview of the EU COST Action ES 1106 Assessment of European agriculture water use and trade under climate change (EURO-AGRIWAT), regarding the evaluation of the potential of remote sensing to improve the WF and Virtual Water Trade (VWT) assessment. The main objective is the analysis of the role of satellite data in the suitable models and indices concerned with the analysis of WF and VWT. The main tasks include: an inventory of the existing and near future satellite data records for several European regions that could be used for the WF and VWT assessment; the study of satellite data resolution requirements, in time and space; the analysis of the assimilation of satellite data into models for the determination of green and blue water use; conclusions and recommendations concerning the possibility to integrate remote sensing into WF and VWT accounting. The combination of RS data to assess the volume of irrigation applied, and the green and blue WF faces several limitations with respect to discrepancies in spatial and temporal resolution and data availability, which will be studied.

Digital twins, a nascent yet potent computer technology, can substantially advance sustainable ocean management by mitigating overfishing and habitat degradation, modeling, and preventing marine pollution and supporting climate adaptation by safely assessing marine geoengineering alternatives. Concomitantly, digital twins may facilitate multi-party marine spatial planning. However, the potential of this emerging technology for such purposes is underexplored and yet to be realized, with just one notable project entitled European Digital Twins of the Ocean. Here, we consider the promise of digital twins for ocean sustainability across four thematic areas. We further emphasize implementation barriers, namely, data availability and quality, compatibility, and cost. Regarding oceanic data availability, we note the issues of spatial coverage, depth coverage, temporal resolution, and limited data sharing, underpinned, among other factors, by insufficient knowledge of marine processes. Inspired by the prospects of digital twins, and informed by impending difficulties, we propose to improve the availability and quality of data about the oceans, to take measures to ensure data standardization, and to prioritize implementation in areas of high conservation value by following the ‘nested enterprise’ approach.