Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Data modelling for emergency response

Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)

Improving obesogenic environmental assessments with advanced geospatial methods

This thesis explores the intricate connections between the envir- onment and obesity. It develops and applies advanced geospatial methods to enhance the assessment of obesogenic environments and obesity risks. Its primary objective is to evaluate obesogenic environments and explore potential associations between environ- mental factors and obesity, crucial for effective obesity prevention. The thesis is structured around four key objectives. The first sub-objective involves an investigation into the current literature on the measurement of the built environment. Street View Imagery (SVI) and advanced urban visual intelligence technologies have transformed Built Environment Auditing (BEA) substantially, enabling large-scale auditing at a detailed geographical level. A me- ticulous review of 96 articles published before September 15, 2023, reveals key areas for improvement in SVI-based BEA. Recommend- ations include standardized datasets for more accurate audits, the integration of multi-source SVI for comprehensive assessments, and the design of auditing tools tailored to developing countries. Ad- dressing these areas enhances the potential of SVI in environmental auditing, as they contribute to a better understanding of the built environment’s health impact and facilitate informed decision-making in urban planning and public health initiatives. The second sub-objective focuses on analyzing exposure to in- creasing PM2.5 pollution, associated with rising morbidity and mor- tality. An ensemble machine learning model, integrating multi-source geospatial data, is presented to map hourly street-level PM2.5 concen- trations in the city of Nanjing, China, at a 100 m spatial resolution. The study concludes that mapping these concentrations reveals spati- otemporal trends, supporting the establishment of exposome studies. The third sub-objective addresses the development of a framework to evaluate Physical Activity (PA) opportunities (bikeability) in urban environments, aiming to enhance sustainable urban transportation planning. A framework is proposed that comprises safety, comfort, accessibility, and vitality sub-indices. It uses open-source data, ad- vanced deep neural networks, and GIS spatial analysis, to eliminate subjective evaluations and enhance efficiency. Experimental results in the city of Xiamen, China, demonstrate the framework’s effectiveness in identifying areas for improvement and enhancing cycling mobility. The fourth sub-objective investigates the associations between PA opportunities, specifically walkability, and obesity. Using a cross- sectional cohort from Nanjing, China. A Logistic regression model with a double robust estimator estimates the effects of walkability on obesity risks. A newly developed walkability index shows a sig- nificant negative association with obesity, particularly when using a data-based-buffer derived from web-mapping navigation that better represents individual activity spaces. These findings provide evidence for developing explicit strategies for obesity prevention. In summary, this thesis contributes to addressing the knowledge gap in health geography between obesogenic environments and obesity risks, employing advanced geospatial methods. The integration of multisource geospatial data, machine learning methods like deep learning in a GIS environment, and spatial statistics presents a major step forward

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals