The size of the Africa Geospatial Analytics market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.99% during the forecast period. Recent developments include: November 2022: A Memorandum of Understanding (MOU) was signed by SaskTel and Axiom Exploration Group to jointly explore opportunities to assist organizations throughout Saskatchewan in enhancing and modernizing their operations through the gathering and analysis of geospatial and other geophysical data., September 2022: A two-day conference on Data Analytics and visualization was held by Women in GIS Kenya in association with Pathways International, Esri Eastern Africa, Nakala Analytics, and the University of Nairobi, Department of Geospatial and Space Technology.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: High costs associated with geospatial technologies. Notable trends are: Commercialization of Spatial Data.

Geospatial Analytics in Insurance Market Outlook

According to our latest research, the geospatial analytics in insurance market size was valued at USD 2.9 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.8% expected from 2025 to 2033. By the end of the forecast period, the market is projected to reach USD 9.1 billion, driven by increasing adoption of advanced analytics for risk assessment and claims management across the global insurance sector. The rapid integration of geospatial data with core insurance processes is enabling insurers to enhance operational efficiency, improve customer experience, and reduce fraud, which are among the key growth factors fueling this market’s expansion.

The primary growth driver for the geospatial analytics in insurance market is the rising need for precise risk assessment and mitigation strategies. Insurance companies are increasingly leveraging geospatial data to analyze environmental risks such as floods, wildfires, and storms, which significantly impact underwriting and pricing decisions. By integrating satellite imagery, aerial photography, and geographic information systems (GIS), insurers can more accurately evaluate property locations, historical claim patterns, and susceptibility to natural disasters. This granular level of insight not only helps in pricing policies more effectively but also reduces the risk of underwriting losses. Moreover, the increasing frequency and severity of climate-related events have made traditional risk models obsolete, pushing insurers to adopt geospatial analytics as a critical tool for business continuity and resilience.

Another significant factor propelling market growth is the evolving regulatory landscape and the growing emphasis on transparency and compliance within the insurance industry. Regulatory bodies across various regions are mandating the use of data-driven approaches for assessing risk and ensuring fair premium calculations. Geospatial analytics plays a pivotal role in meeting these regulatory requirements by providing verifiable, location-based data that enhances the accuracy of risk evaluation and claim validation. Furthermore, the integration of real-time geospatial data facilitates immediate response to catastrophic events, enabling insurers to streamline claims processing and improve customer satisfaction. As regulations become more stringent, the adoption of geospatial analytics is expected to accelerate, further boosting market growth.

Technological advancements and the proliferation of cloud-based solutions are also contributing to the expansion of the geospatial analytics in insurance market. The advent of artificial intelligence (AI), machine learning, and big data analytics has revolutionized the way geospatial data is collected, processed, and analyzed. Cloud-based geospatial analytics platforms offer scalable and cost-effective solutions, making them accessible to both large enterprises and small and medium-sized insurers. These platforms enable seamless integration with existing insurance management systems, facilitating real-time data sharing and collaboration across departments. The continuous innovation in remote sensing technologies, drones, and IoT devices is further enhancing the quality and granularity of geospatial data, opening new avenues for insurers to optimize their operations and deliver personalized services to their customers.

From a regional perspective, North America continues to dominate the geospatial analytics in insurance market, accounting for the largest revenue share in 2024. The region’s advanced digital infrastructure, high insurance penetration rates, and early adoption of geospatial technologies are key contributors to its market leadership. Europe follows closely, driven by stringent regulatory frameworks and increasing investments in digital transformation initiatives by insurers. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, increasing natural disaster occurrences, and rising awareness among insurers about the benefits of geospatial analytics. Latin America and the Middle East & Africa are also emerging as promising markets, albeit at a slower pace, due to gradual technological adoption and evolving insurance landscapes.

Component Analysis

The geospatial analytics in insurance market is segmented by component into software, services, and hardware, with each playing a distinct role in shaping

Geospark Analytics tuned the Hyperion Health Event machine learning model to identify, track and analyze events associated with the spread of COVID-19. The data includes information from news and social media. We released the results of our Health Event model for free. The data tracks global reporting about the virus and have been accessed more that 15,000 times in the first month and has been integrated into other applications providing a unique view into the world’s COVID-19 information._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Machine learning (ML) interpretability has become increasingly crucial for identifying accurate and relevant structural relationships between spatial events and factors that explain them. Methodologically aspatial ML algorithms with an apparent high predictive power ignore non-stationary domain relationships in spatio-temporal data (e.g. dependence, heterogeneity), leading to incorrect interpretations and poor management decisions. This study addresses this critical methodological issue of ‘interpretability’ in ML-based modeling of structural relationships using the example of heterogeneous drivers of wildfires across the United States. Specifically, we present and evaluate a spatio-temporally interpretable random forest (iST-RF) that uses spatio-temporal sampling-based training and weighted prediction. Although the ultimate scientific objective is to derive interpretation in space-time, experiments show that iST-RF can improve predictive accuracy (76%) compared to the aspatial RF approach (70%) while enhancing interpretations of the trained model’s spatio-temporal relevance for its ensemble prediction. This novel approach can help balance prediction and interpretation with fidelity in a spatial data science life cycle. However, challenges exist for predictive modeling when the dataset is very small because in such cases locally optimized sub-model’s prediction performance can be suboptimal. With that caveat, our proposed approach is an ideal choice for identifying drivers of spatio-temporal events at country- or regional-scale studies.

Partly due to global climate change, extreme weather and natural hazards have increased dramatically during the recent decades. Those sudden environmental changes often cause significant impacts on the living species on the planet via directly affecting the population structures or indirectly causing habitat loss or fragmentations. In August - October 2020, tremendous mortalities of avian species were reported in the western and central US, likely resulting from winter storms and wildfires based on previous evidence. However, the differences of how different species might respond to the environmental changes were still poorly understood. In this study, we focused on three species that have been recorded with the highest death observations collected by citizen scientists (i.e., Wilson’s warbler, barn owl, and common murre) and employed the random forest model to disentangle their responses to the two environmental changes. We found the mortalities of Wilson’s warbler were primarily impacted by early winter storms, with more deaths identified in areas with a higher average of maximum daily snowfalls. Barn owl responded to both wildfire effects and winter storms but with more deaths identified in places with high wildfire-induced air pollution. Both events had mild effects on common murre. Mortalities of common murre may be related to high water temperature. Our findings highlight the species-specific responses to environmental changes, which can provide significant insights into the resilience of ecosystems to environmental change and avian conservations.

1. PUBLICATION CORRESPONDING TO THESE DATA Sloan, Sean*; Locatelli, Bruno; Andela, Niels; Cattau, Megan E.; Gaveau, David; Tacconi, Luca. 2022 ‘Declining Severe Fire Activity on Managed Lands in Equatorial Asia’. Communications Earth & Environment. DOI: 10.1038/s43247-022-00522-6. *Corresponding author email: sean.sloan@viu.ca 2. ABSTRACT OF THE DATA The GIS data and corresponding attribute data described here pertain to discrete fire events, their severity, and their ignitions, as derived on the basis of daily MODIS Collection 6 MCD14ML active-fire detections (AFDs). Data on fire events and their ignitions are provided separately, as two data files. These data files on fire events and ignitions may however be linked to each other by the data user. Fire-event severity is quantified per fire event and reported in the data file for fire events. A fire event is a cluster of MODIS Collection 6 MCD14ML active-fire detections (AFDs) wherein each AFD has a spatial (<=1-km) and temporal (<=4-day) proximity to another AFD in the same fire event, inferring thus a relational co-occurrence amongst AFDs in time and space. In other words, a fire event is considered a likely occasion of burning wherein all constituent AFDs are related to each other in time and space, either directly (as for proximate AFDs) or indirectly (as in the case of a large area of fire activity that spread progressively over time and space from an initial source). Each fire event has a designated ignition AFD, being the AFD of the fire event with the earliest detection date. A given fire event can have more than one ignition AFD if the ignitions all share same earliest detection date. The ignition AFD(s) is the nominal initial source of the burning described by the corresponding fire event. All other, non-ignition AFDs of a fire event are deemed its ‘propagation’ AFDs, since these AFDs follow from the ignitions, temporally and spatially. See Figure 4 in the publication by Sloan et al. for an illustration of the geography of fire events and their ignition AFDs. Fire events and their ignitions were derived from standard science-quality MODIS Collection 6 MCD 14ML AFD data, commonly referred to as fire ‘hotspot’ data. Data were detected by both the Terra and Aqua satellite sensors daily for Indonesia between July 2002 and December 2019. Information on these input data are provide by the two citations below. The publication of Sloan et al. provides methodological details on how the MODIS Collection 6 MCD 14ML AFD data were processed into discrete fire events and ignitions. EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019). Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sensing of Environment 178, 31-41, (2016). 3. DATA FILES Two data files are distributed here – one for discrete fire events, and another for the ignition AFDs of each fire event. The data files are provided in a GIS-compatible format, and also as a generic text format, as described below. 3.1 GIS VERSION Data files in GIS-compatible format are provided as ‘feature classes’ within an ArcGIS file geodatabase ‘Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb’. These data files can be viewed and manipulated using either ArcGIS Desktop or ArcGIS Pro software. There is one feature class for fire events, and another file for ignitions. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb fire4_all_spatial_fire_2002_2019_joins_sp_LC This file pertains to fire events. All AFDs of a given fire event are included, without differentiation as to whether the AFDs are ignition AFDs or other (propagation) AFDs. Fire events are assigned unique ID values and basic attribute data. Sloan_MODIS_FireEvents_Ignitions_2002_2019.gdb fire4_all_spatial_fire_2002_2019_igs_sp_LC This file pertains to ignitions. Only ignition AFDs are included for a given fire event. Fire events corresponding to the ignitions are assigned unique ID values and basic attribute data. 3.2 CSV TEXT VERSION Both data files are also supplied as comma-separated value (CSV) text files for viewing and manipulation in non-GIS software, such as Excel, text editors, or any statistical software. The text files can also be read into various GIS software. CSV-formatted files have the same file name and attribute fields as the corresponding GIS-formatted data files. These CSV-formatted data files (as well as the GIS-formatted data files) include attribute data on the latitude and the longitude of each AFD. Attribute field names are included as the first row of values in a CSV file. No ‘text qualifiers’ like quotations (“ ”) or inverted commas (‘’) are used to designate text/string values within the CSV file. Text values appear directly between commas in the CSV data file, e.g., …,Kalimantan_Southern,… . Note two points of caution for working with these CSV data: i) Microsoft Excel may be used for a partial view of the data file nfire4_all_spatial_fire_2002_2019_joins_sp_LC.csv, but it is not recommended for working with this data file. This is because the number of records/rows in this csv file slightly exceeds that maximum that may be read by Excel, which is just over 1 million. This limitation does not apply to the other csv file, however. ii) The GIS-formatted data files employ ‘null values’ in their attribute tables, and so the corresponding ‘values’ in the CSV-formatted data files are similarly null. For null values, no value whatsoever is ascribed, not even 0. In the syntax of a CSV file (apparent upon opening the file in any text editor like Microsoft Notepad), a null value is denoted by two consecutive commas without any value, text, or space between them. If a CSV file were opened in Excel, a cell assigned a null value would be blank, not 0 or otherwise. This denotes the correct transcription of the GIS-formatted data. This feature will not impede the correct reading of these CSV data by whatever software. Users are made aware of this feature merely to ensure the proper input of these data into whatever software. 4. DATA STRUCTURE / GEOGRAPHY The GIS-formatted data files are ‘point data’, i.e., they map the geography of AFDs as individual ‘points’, in keeping with how these MODIS MCD14ML AFD data were originally structured. For the GIS-formatted data files, each record/row in its corresponding attribute tables corresponds geographically to single AFD ‘point’, regardless of whether that AFD belongs to a fire event comprised of many AFDs. In the parlance of GIS files, the files depict ‘single-part’ point features. The unique ID field [nfireID2] serves to denote the fire event to which a given AFD belongs. Similarly, for the CSV-formatted data files, each record/row of values corresponds to a single AFD. There are 1,232,377 records for the data file ‘nfire4_all_spatial_fire_2002_2019_joins_sp_LC’. There are 720795 records for the data file ‘nfire4_all_spatial_fire_2002_2019_igs_sp_LC’. 5. ATTRIBUTE FIELDS In the data files, while some attribute fields pertain to the individual AFD as the unit of observation (e.g., the land-cover class coincident with the AFD), other attribute fields correspond to the larger ‘fire event’ to which the individual AFD belongs (e.g., the total duration of fire activity for the fire event). Accordingly, for certain attribute fields pertaining to the fire event as a whole, their values will appear ‘duplicated’ in the data file amongst those individual AFDs (records) that constitute the fire event in question. Whether a given attribute field pertains to the individual AFD or to its constituent fire event is denoted below for each field. Each AFD is assigned a unique ID field denoting its constituent fire event, [nfireID2]. This field is consistent between both data files, so that attribute data for a given fire event may be ‘matched’ to attribute data for its corresponding ignition AFD(s), and vice versa, on the basis of the common value of the field [nfireID2]. Note that many attributes below are as originally defined/measured by the input MCD 14ML data, or are derived directly thereof. 5.1 DATASET nfire4_all_spatial_fire_2002_2019_joins_sp_LC Field Name Geography of Attribute Value Definition OID AFD Object ID value. Unique values for each AFD (record) in the GIS-formatted data file when viewed in ArcGIS. Field values are -1 in the CSV-formatted data file. Peat AFD Denotes whether the AFD occurs on peatlands (value=1) as defined in Sloan et al. MIN_CONFIDE Fire Event The minimum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. MAX_CONFIDE Fire Event The maximum detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. MEAN_CONFIDE Fire Event The mean detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. STD_CONFIDE Fire Event The standard deviation of detection confidence of all AFDs in the fire event, where confidence ranges from 1-100%. SUM_FRP Fire Event The sum total of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. MEAN_FRP Fire Event The mean of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. MIN_FRP Fire Event The minimum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. MAX_FRP Fire Event The maximum of all fire-radiative power (FRP) measures for all AFDs in the fire event. Units are megawatts. MEAN_LATITUD Fire Event The mean latitude of all AFDs in the fire event. MEAN_LONGITU Fire Event The mean longitude of all AFDs in the fire event. MIN_ACQ_DAT Fire Event The minimum acquisition date of all AFDs in the fire event (i.e., the ignition AFD detection date). MAX_ACQ_DAT Fire Event The maximum acquisition date of all AFDs in the

This dataset contains searchable text files used in the research:Table of Contents of 25 geospatial science textbooks (.zip file)The 244 GIST Body of Knowledge topics (.zip file)The 2024 Esri User Conference agenda (.txt file)The video transcripts for the Esri Spatial Data Science MOOC (.zip file)The text files for the syllabus for 33 Penn State OGE program courses (.zip file)The text files for course texts for 32 Penn State OGE program courses (.zip file)

Geospatial Risk Platforms for Insurance Market Outlook

According to our latest research, the global Geospatial Risk Platforms for Insurance market size reached USD 2.41 billion in 2024, and is projected to grow at a robust CAGR of 14.2% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 7.31 billion. This growth is primarily driven by the increasing demand for advanced risk analytics and the integration of location-based intelligence in insurance operations, as insurers seek to enhance decision-making and operational efficiency.

One of the most significant growth factors for the Geospatial Risk Platforms for Insurance market is the rising frequency and severity of natural disasters and climate-related events worldwide. Insurers are under mounting pressure to accurately assess and price risk, particularly in property and casualty lines, due to escalating losses from floods, wildfires, hurricanes, and other catastrophic events. Geospatial risk platforms offer sophisticated tools for real-time mapping, predictive analytics, and scenario modeling, enabling insurers to quantify exposures with greater precision. The ability to overlay historical loss data with current environmental and socio-economic information empowers underwriters and actuaries to make more informed decisions, which is critical for maintaining profitability and regulatory compliance in a volatile risk landscape.

Additionally, the rapid digitization of the insurance sector is fueling adoption of geospatial solutions. Insurers are leveraging these platforms to streamline core processes such as underwriting, claims management, and fraud detection. By integrating geospatial data with policyholder and claims information, insurers can automate risk assessments, identify fraudulent patterns, and expedite claims settlement, thereby improving customer satisfaction and operational efficiency. The proliferation of Internet of Things (IoT) devices, drones, and satellite imagery has further enriched the data ecosystem, allowing insurers to capture granular, real-time information on insured assets and events. This digital transformation is particularly pronounced among large insurance carriers and reinsurers, who are investing heavily in advanced analytics to gain a competitive edge.

Furthermore, regulatory and compliance requirements are pushing insurers to adopt more transparent and auditable risk assessment methodologies. Regulatory bodies across North America, Europe, and Asia Pacific are increasingly mandating the use of robust risk modeling and reporting frameworks to ensure solvency and protect policyholders. Geospatial risk platforms provide insurers with the tools to meet these requirements by offering comprehensive audit trails, standardized risk models, and transparent reporting capabilities. This not only helps insurers avoid regulatory penalties but also enhances their reputation and trustworthiness in the eyes of customers and investors.

From a regional perspective, North America currently leads the Geospatial Risk Platforms for Insurance market, accounting for over 38% of global revenue in 2024, followed by Europe and Asia Pacific. The high adoption rate in North America is attributed to the presence of major insurance carriers, advanced IT infrastructure, and a strong focus on innovation. Europe is also witnessing significant growth, driven by stringent regulatory standards and increasing awareness of climate risks. Meanwhile, Asia Pacific is emerging as a high-growth region, supported by rapid urbanization, expanding insurance penetration, and government initiatives to modernize the financial sector. Latin America and the Middle East & Africa are gradually catching up, with insurers in these regions investing in geospatial technologies to address unique local challenges such as flood management and agricultural risk assessment.

Component Analysis

The Component segment of the Geospatial Risk Platforms for Insurance market is bifurcated into Software and Services. Software solutions form the backbone of geospatial platforms, enabling insurers to ingest, process, and visualize spatial data for risk assessment and decision-making. These platforms typically include modules for mapping, spatial analytics, catastrophe modeling, and integration with core insurance systems. The software segment is experiencing rapid innovation, with vendors introducin

Floorplan Heatmaps for Events Market Outlook

According to our latest research, the global Floorplan Heatmaps for Events market size reached USD 1.42 billion in 2024, and is projected to grow at a robust CAGR of 15.7% from 2025 to 2033, culminating in a forecasted market size of USD 5.17 billion by 2033. The primary growth factor driving this market is the increasing demand for real-time spatial analytics to optimize event layouts, enhance attendee experience, and improve operational efficiency. As organizations across industries seek data-driven insights to maximize event ROI, the adoption of floorplan heatmap solutions is witnessing significant momentum globally.

The growth of the Floorplan Heatmaps for Events market is being propelled by the rapid digital transformation in the events industry. Technological advancements, such as IoT sensors, advanced analytics, and AI-powered visualization tools, are enabling event organizers to collect and interpret large volumes of spatial data during live events. These tools empower organizers to visualize attendee movement, identify high-traffic zones, and optimize booth placements and crowd control in real-time. The post-pandemic resurgence of in-person events, coupled with a heightened emphasis on data-driven decision-making, has further accelerated the adoption of heatmap solutions. Additionally, the increasing integration of these systems with event management platforms and mobile applications is streamlining operations and delivering actionable insights, making them indispensable for modern event planning and execution.

Another significant growth driver is the growing emphasis on attendee safety and security. With large-scale events attracting thousands of participants, ensuring crowd safety and compliance with local regulations has become a top priority. Floorplan heatmaps play a crucial role in monitoring crowd density, detecting bottlenecks, and facilitating quick emergency response. By providing real-time visualization of attendee distribution, these solutions help event organizers proactively manage risks, prevent overcrowding, and ensure a seamless flow of people. Regulatory mandates in several regions now require event organizers to implement crowd monitoring and safety protocols, further boosting the demand for advanced heatmap analytics in the events sector.

Furthermore, the market is benefitting from the expanding use cases of floorplan heatmaps beyond traditional event planning. Enterprises are leveraging these solutions for enhanced venue management, optimizing space utilization, and conducting detailed post-event analytics. The ability to generate comprehensive reports on attendee engagement, dwell times, and movement patterns is providing valuable feedback to organizers, exhibitors, and sponsors alike. This data-driven approach is not only improving event outcomes but also enabling continuous improvement in future event strategies. The proliferation of large-scale corporate events, trade shows, concerts, and sports events worldwide is creating a fertile ground for the sustained growth of the Floorplan Heatmaps for Events market.

Regionally, North America continues to dominate the market, driven by the presence of leading technology providers, high digital adoption rates, and a mature events industry. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, rising disposable incomes, and the increasing popularity of mega-events and exhibitions. Europe follows closely, with strong demand from the corporate and entertainment sectors. The Middle East & Africa and Latin America are also witnessing steady growth, supported by government initiatives to promote tourism and international events. This diverse regional landscape is shaping the competitive dynamics and innovation trajectories within the global market.

Component Analysis

The Component segment of the Floorplan Heatmaps for Events market is bifurcated into Software and Services, each playing a pivotal role in enabling comprehensive spatial analytics for event organizers. Software solutions constitute the core of this segment, encompassing advanced visualization platforms, analytics engines, and integration tools that process and display real-time heatmaps. These software products are designed to seamlessly integrate with event management systems, IoT sensors, and attendee tracking devices, providing organizers wi

Archived website of the XVIth International Conference of the Association for History and Computing, Amsterdam, the Netherlands, 14-17th September 2005.

The XVIth Conference of the international AHC aims to bring together specialists from three broad streams:
- Scholars, using computers in historical and related studies (history of art, archaeology, literary studies, etc.)
- Information and computing scientists, working in the domain of cultural heritage and the humanities
- Professionals, working in cultural heritage institutes (archives, libraries, museums) who use ICT to preserve and give access to their collectionsThe subject matter of the conference is primarily oriented at methodological issues, and not restricted to one particular domain within historical sciences and the humanities. Preferably, sessions will consist of a mix of these three interest groups and fields. There will be numerous cross links between the streams.

Topics for sessions and papers include:- Data access, retrieval and presentation: Data bases in historical/humanities research;
- Data mining, data harvesting and data syndication;
- Digital data archives & longevity of digital heritage;
- Personalization and presentation of heritage information;
- Virtual libraries and virtual collaboratories in the humanities;
- Enriching data: Digital source editions; Knowledge enrichment and encoding methods;
- Metadata standards and semantic interoperability for access to cultural heritage;
- Images & multimedia: Image analysis and visual culture;
- Content based and other image retrieval methods;
- Digital photo/image/video collections;
- Digital museums;
- Geographical Information Systems: GIS Applications in the humanities and historical studies;
- GIS methods and techniques; GIS for access to heritage information;- Qualitative & Quantitative data analysis: Advanced statistics in historical research;
- Models and simulations;
- Exploratory analysis and visualization techniques- Digitization of heritage information: Large digitization projects of historical sources;
- Optical character and document recognition for historical materials;
- Handwriting recognition and script analysis tools
- Text analysis and retrieval: Applications of text analysis in the humanities;
- Methodological issues of text mining and text analysis;
- Digital text archives
- Theoretical, methodological and educational issues: e-Science, e-Humanities and e-History;
- Historiography of humanities computing;
- Educational issues

Low Countries Organization Committee:
- Onno Boonstra (Humanities computing, University of Nijmegen)
- Leen Breure (Computer and Information Science, University of Utrecht)
- Peter Doorn (NIWI - Netherlands Institute for Scientific Information Services, Amsterdam)- Jaap van den Herik (Computer Science, Universities of Leiden and Limburg)- Bart de Nil (Amsab - Institute for Social History, Gent, Belgium)
- Paula Witkamp (European Commission on Preservation and Access, Amsterdam)

Organizing institutions:
- Netherlands Institute for Scientific Information Services (NIWI)
- Royal Netherlands Academy of Arts and Sciences (KNAW)
- Vereniging voor Geschiedenis en Informatica (VGI)
- The Association for History and Computing (AHC)
- Dutch Research School for Information and Knowledge Systems (SIKS) The content of the website has been saved in three PDF packages with information over the conference and the collections of abstracts and posters.

This dataset comprises event peak flows, representing extreme floods at 516 stations in Germany. The data generation process involves several key steps. Initially, observed rainfall events associated with 10 historical flood disasters from 1950 to 2021 are undergone spatial shifts. These shifts involve three distances (20, 50, and 100 km) and eight directions (North, Northeast, East, Southeast, South, Southwest, West, Northwest), resulting in 24 counterfactual precipitation events. Including the factual (no shift) event, a total of 25 distinct shifting events are considered. Subsequently, these shifted fields are used as atmospheric forcing for a mesoscale hydrological model (mHM) set up and calibrated for the entire Germany. The model produces daily stream flows across its domain, from which the event peak flows are derived. This dataset is expected to provide a valuable resource for analyzing and modeling the dynamics extreme flood events in Germany.

This data set provides spatial catalog of the Extreme Heat and Cold Events (EHE/ECE) in the U.S. This also includes curated data set of the historical weather variables from the NOAA's Integrated Surface Data Set (ISD).

Hail Swath Analytics for Insurers Market Outlook

According to our latest research, the Global Hail Swath Analytics for Insurers market size was valued at $542 million in 2024 and is projected to reach $1.47 billion by 2033, expanding at a CAGR of 11.6% during 2024–2033. The increasing frequency and severity of hailstorms, coupled with rising insured asset values and the growing sophistication of geospatial analytics, are major factors propelling the demand for advanced hail swath analytics solutions worldwide. Insurers are increasingly leveraging these tools to enhance claims accuracy, reduce fraud, and improve risk modeling, thereby driving market growth across all key segments. The growing adoption of cloud-based platforms and integration of artificial intelligence further accelerates the transformation of traditional insurance processes, positioning hail swath analytics as a critical component in modern insurance operations.

Regional Outlook

North America commands the largest share of the global hail swath analytics for insurers market, accounting for over 38% of total revenue in 2024. This dominance is attributed to the region’s mature insurance industry, high penetration of property and casualty insurance, and frequent occurrence of severe hail events, particularly in the United States and Canada. Regulatory mandates for rapid claims processing and the presence of several leading technology vendors have fostered a robust ecosystem for hail analytics solutions. Additionally, North American insurers have been early adopters of geospatial and remote sensing technologies, integrating hail swath analytics into their core risk management and claims workflows. The region’s advanced data infrastructure and strong focus on customer-centric digital transformation further support the widespread deployment of these analytics solutions.

Asia Pacific is emerging as the fastest-growing region, projected to expand at a CAGR exceeding 14.2% between 2024 and 2033. The surge in insurance penetration, rapid urbanization, and increased investments in digital transformation initiatives are key growth drivers. Countries such as China, Japan, and Australia are experiencing heightened exposure to extreme weather events, prompting insurers to adopt advanced analytics for more accurate risk assessment and claims management. Government initiatives encouraging disaster risk reduction and the proliferation of insurtech startups are accelerating technology adoption in the region. The growing availability of high-resolution meteorological data and the expansion of cloud infrastructure are making it easier for insurers in Asia Pacific to deploy scalable hail swath analytics solutions tailored to local needs.

Emerging economies in Latin America, the Middle East, and Africa are gradually adopting hail swath analytics, though market growth remains constrained by infrastructural and regulatory challenges. In these regions, insurance penetration is still relatively low, and many insurers rely on traditional claims assessment methods. However, growing awareness of climate-related risks, coupled with international reinsurers’ efforts to modernize operations, is fostering incremental adoption. Localized demand is often shaped by catastrophic hail events, which prompt short-term spikes in technology uptake. Policy reforms aimed at modernizing the insurance sector and international partnerships are expected to gradually improve the adoption rate, though challenges such as data standardization, limited access to high-quality meteorological data, and budget constraints persist.

Report Scope

HydroShare is a system operated by CUAHSI for sharing hydrologic data and models aimed at giving hydrologists the cyberinfrastructure needed to manage data, innovate, and collaborate in research to solve water problems. HydroShare addresses the challenges of sharing data and hydrologic models to support collaboration and reproducible hydrologic science through the publication of hydrologic data and models. With HydroShare users can: (1) share data and models with colleagues; (2) manage who has access to shared content; (3) share, access, visualize and manipulate a broad set of hydrologic data types and models; (4) use the web services API to program automated and client access; (5) publish data and models to meet the requirements of research project data management plans; (6) discover and access data and models published by others; and (7) use web apps to visualize, analyze, and run models on data in HydroShare. This workshop will introduce participants to HydroShare and show new features recently deployed. Participants will learn how to use HydroShare to: • Upload, share and publish science products in HydroShare and receive a citable digital object identifier (DOI). This helps fulfill NSF’s data management requirements. • Use HydroShare for collaboration, sharing data and models with individual users or a group • Organize resources into collections in HydroShare • Use the HydroShare GIS app to visualize and create web maps using content in HydroShare • Use the HydroShare Jupyter Notebook app to write scripts and short programs to analyze and model with data in HydroShare. • Use Apps to access and visualize data from the National Water Model.

This shapefile was used to generate a permafrost map of Alaska that was presented in conjunction with the Ninth International Conference on Permafrost held at the University of Alaska in 2008. The permafrost shapefile is coded with surficial geology, mean annual air temperature (MAAT), primary soil texture, permafrost extent, ground ice volume and primary thermokarst landforms. Original surficial geology compiled from hand delineations on 1:250,000 USGS topo maps. Significant horizontal errors (1-3kms) can be found when compared to ortho corrected imagery. See process description in FGDC metadata view for full description of data compilation. Use data at a scale appropriate for general state-wide mapping or assessment. Authors: Torre Jorgenson, Kenji Yoshikawa, Mikhail Kanevskiy, and Yuri Shur University of Alaska Fairbanks, Institute of Northern Engineering, Fairbanks, Alaska, USA Vladimir Romanovsky, Sergei Marchenko, and Guido Grosse University of Alaska Fairbanks, Geophysical Institute, Fairbanks, Alaska, USA Jerry Brown International Permafrost Association, Woods Hole, Massachusetts, USA Ben Jones U.S. Geological Survey, Anchorage, Alaska, USA

The EPSCoR eligibility of each US state and the number of Ph.D. granting universities in that state are catalogued by academic analytics.