Today, everybody around the world is living and working under the coverage of Geographic Information system (GIS) application and services such as the Google Earth, GPS and much more. Big Data visualization tools are increasingly creating a wonder in the world of GIS. GIS has diverse application, from geo-positioning services to 3D demonstrations and virtual reality. Big Data and its tools of visualization has boosted the field of GIS. This article seeks to explore how Big data visualization has expanded the field of Geo- spatial analysis with the intention to present practicable GIS-based tools required to stay ahead in this field.

Harvard CGA Geotweet Geography Archive is a subset of Harvard CGA Geotweet Archive v2.0 enriched with boundaries at the ADMIN-2 level. It contains the tweet identification records and ADMIN 2 variables for more than 4.3 billion geo-tagged tweets since 2019. This dataset has been used to calculate the geographic variables of our Twitter Sentiment Geographical Index dataset and is available to the academic community at large, unlike the Harvard CGA Geotweet Archive v2.0 which is under Twitter's redistribution policy restriction for public sharing. It could serve as cross-validation data for publications that used data from Harvard CGA Geotweet Archive v2.0 . If you are interested in accessing this archive, please fill out our Geotweet Request Form. Before requesting or receiving Tweet IDs, requestors must agree to Twitter's Terms of Service, Twitter's Privacy Policy, and Twitter's Developer Policy . Geotweets IDs data provided by CGA can only be used for not-for-profit research and academic purposes. Recipients may not share CGA provided Tweet IDs or content derived from them without written permission from the CGA. Citations: If you use the Geotweet Archive in your research please reference it: "Harvard CGA Geotweet IDs Archive". ======================================================== Schema of Geotweet Census Archive Field name_TYPE_Description message_id----TEXT----Tweet ID OBJECTID ----INT----Object ID ID_0 ----INT----Country/Region ID NAME_0 ----TEXT----Country/Region Name ISO ----TEXT----Country/Region Abbreviation ID_1 ----INT----State/Province ID NAME_1 ----TEXT----State/Province Name ID_2 ----INT----County/City ID NAME_2 ----TEXT----County/City Name

The research project 3D-MAPP develops a web-based methodology to obtain digital geodata via the combination of data analysis by human and machine. Through a quick and easy-to-use 3D Web visualization users are able – in a few seconds – to solve 3D micro mapping tasks, which can hardly or even not be solved by automatic algorithms.

This data set was developed as an information layer for the Washington State Department of Commerce. It is designed to be used as part of the Puget Sound Mapping Project to provide a generalized and standardized depiction of land uses and growth throughout the Puget Sound region.

This map represents land uses, zoning abbreviations and zoning descriptions. Zoning data was collected in raster format and digitized by State Department of Commerce staff. The generalized depiction of intended future land use is based primarily upon 2012 zoning and 2010 assessor's records.NOTE: Because this is a large dataset, some geoprocessing operations (i.e. dissolve) may not work on the entire dataset. You will receive a topoengine error. Clipping out an area of interest (i.e. a county) and performing the operation on it instead of on the full dataset is a way to get around this software limitation.

This dataset is provided for the paper "Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data".

Please refer to the readme in the zipped file for additional documentation.

The zipped file contains two CSV files for every country in Africa obtained by queries "[country name]" and "[country name + people]".

The archaeological record is one piece of the puzzle in understanding the evolution of humans, helping to trace the cultural connections between different species and their technologies, as well as their expansion in time and space. Here we demonstrate a method for mapping the boundaries, centers, and peripheries of ancient cultures, as well as the technological similarities between different cultures. The proposed workflow includes: a systematic collection of archaeological information in a database; a process to infer the similarities between assemblages and generate a network; and finally, a graphical method for big data visualization, a technique also used in social media analysis. We present the geography of multiple cultural complexes that span several stages of cultural evolution from the Lower to the Upper Paleolithic (Stone Age) and involve several species of the genus Homo. Finally, we discuss some alternative trajectories in which this workflow can be developed further.

MG-Geo DatasetTowards Interactive Global Geolocation Assistant 🤖

MG-Geo is a novel multimodal dataset comprising 5 million meticulously curated image-text pairs, specifically designed to address the existing limitations in geography-related data for Multimodal Large Language Models (MLLMs).

  Dataset Highlights 🌟

Large Scale: Contains 5,000,000 high-quality image-text pairs. Geography Focused: Specially designed to capture the complex interplay between geographic… See the full description on the dataset page: https://huggingface.co/datasets/kendouvg/MG-Geo.

Harvard CGA Geotweet Sentiment Archive is a subset of Harvard CGA Geotweet Archive v2.0 enriched with a sentiment score. It contains the tweet identification records along with a sentiment score based on tweet text for about 4.3 billion geo-tagged tweets since 2019. This sentiment score was calculated using Bidirectional Encoder Representations from Transformers. More information about this methodology can be found in our Nature Paper on Twitter Sentiment Geographical Index. This dataset is available to the academic community at large, unlike the Harvard CGA Geotweet Archive v2.0 which is under Twitter's redistribution policy restriction for public sharing. It could serve as cross-validation data for publications that used data from Harvard CGA Geotweet Archive v2.0 . If you are interested in accessing this archive, please fill out our Geotweet Request Form. Before requesting or receiving Tweet IDs, requestors must agree to Twitter's Terms of Service, Twitter's Privacy Policy, and Twitter's Developer Policy . Geotweets IDs data provided by CGA can only be used for not-for-profit research and academic purposes. Recipients may not share CGA provided Tweet IDs or content derived from them without written permission from the CGA. Citations: If you use the Geotweet Archive in your research please reference it: "Harvard CGA Geotweet IDs Archive". ======================================================== Schema of Geotweet Census Archive Field name_TYPE_Description message_id----TEXT----Tweet ID score ----FLOAT----BERT sentiment score

Ovaj skup podataka uključuje finansijske izvještaje, račune i blokade, te nekretnine. Podaci uključuju prihode, rashode, dobit, imovinu, obaveze i informacije o nekretninama u vlasništvu kompanije. Finansijski podaci, finansijski sažetak, sažetak kompanije, preduzetnik, zanatlija, udruženje, poslovni subjekti.

The definitive dataset framework realized on the ArcGIS software, containing the Data Analysis on Berga, used to build the composite indicators of the HISMAGIS Protocol.

Alle the feature class, geodatabase and collector carpets have been translated in English.

Harvard CGA Geotweet IDs Archive is a subset of Harvard CGA Geotweet Archive v2.0 . It contains the user and message identification records of individual tweets for approximately 10 billion geo-tagged tweets from January 2010 to July 2023. This dataset is available to the academic community at large, unlike the Harvard CGA Geotweet Archive v2.0 which is under Twitter's redistribution policy restriction for public sharing. It could serve as cross-validation data for publications that used data from Harvard CGA Geotweet Archive v2.0 . If you are interested in accessing this archive, please fill out our Geotweet Request Form. Before requesting or receiving Tweet IDs, requestors must agree to Twitter's Terms of Service, Twitter's Privacy Policy, and Twitter's Developer Policy . Geotweets IDs data provided by CGA can only be used for not-for-profit research and academic purposes. Recipients may not share CGA provided Tweet IDs or content derived from them without written permission from the CGA. Citations: If you use the Geotweet Archive in your research please reference it: "Harvard CGA Geotweet IDs Archive". ======================================================== Schema of Geotweet IDs Archive Field name_TYPE_Description message_id----BIGINT----Tweet ID user_id ----BIGINT----User ID number

This data set was developed as an information layer for the Washington State Department of Commerce. It is designed to be used as part of the Puget Sound Mapping Project to provide a generalized and standardized depiction of land uses and growth throughout the Puget Sound region.NOTE: Because this is a large dataset, some geoprocessing operations (i.e. dissolve) may not work on the entire dataset. You will receive a topoengine error. Clipping out an area of interest (i.e. a county) and performing the operation on it instead of on the full dataset is a way to get around this software limitation.

The contour lines are the set of lines connecting all points at the same elevation in a model used to represent the relief on a large scale.

The global geostationary (GEO) satellite market is experiencing robust growth, driven by increasing demand for high-throughput satellite (HTS) broadband services, advancements in satellite technology, and the expanding adoption of GEO satellites across various sectors. The market's size in 2025 is estimated at $15 billion, reflecting a substantial increase from previous years. A Compound Annual Growth Rate (CAGR) of 8% is projected from 2025 to 2033, indicating a steady expansion fueled by factors like the growing need for reliable communication infrastructure in remote areas, advancements in satellite technology leading to increased bandwidth and reduced costs, and the rising popularity of satellite-based internet access, particularly in underserved regions. The commercial sector, encompassing applications such as broadcasting, telecommunications, and internet service provision, dominates the market share, although the military and other governmental applications represent a significant and growing segment. Larger GEO satellites (above 500kg) are expected to witness higher growth rates due to their enhanced payload capacity and ability to accommodate larger constellations, while technological advancements continuously push the boundaries of satellite capabilities, improving efficiency and performance. Geographic distribution shows strong concentration in North America and Europe, though Asia-Pacific is projected to experience significant growth in the coming years, driven by investments in infrastructure development and increasing internet penetration. Market restraints include the high initial investment costs associated with launching and operating GEO satellites and regulatory complexities. Competition within the GEO satellite market is intense, with key players including Airbus Defence and Space, OHB SE, Boeing, Lockheed Martin, Northrop Grumman, and Thales Alenia Space. These companies are engaged in a continuous race to innovate, focusing on improving satellite design, reducing launch costs, and expanding service offerings to maintain a competitive edge. The strategic partnerships and mergers & acquisitions are anticipated to further shape the market landscape, leading to consolidation among major players. The increasing demand for miniaturized and more cost-effective satellites presents opportunities for smaller, specialized companies to enter the market. Overall, the GEO satellite industry anticipates a period of sustained growth, driven by technological innovation and escalating demand for communication and data services across the globe. The ongoing evolution in satellite technology, along with the expanding role of GEO satellites in crucial applications like disaster relief and environmental monitoring, will contribute to the market’s continued expansion.

Information flow (both large and small)

Most of our nation’s people live, work, or visit places where big earthquakes could strike without warning. To get ready for an earthquake, the first step is understanding what it could be like. This USGS geo-narrative (story map) summarizes what to expect in an earthquake large enough to affect lives and livelihoods, notes the aspects of human nature that influence all our actions and decisions during disasters, and also provides links to information about what people can do to get ready, as individuals, businesses, communities, and governments.

SIAM 2013 Presentation

Models to calculate composite indicators (Berga and Sutri). HISMACITY Protocol. The folder and .gdb files with the names: Attractiveness and Convertibility contain the data for Tourist Efficiency and Transformability indexes.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan–Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

This dataset presents information on singleton LGA, that is the percentage of singleton live births over a year that are large for gestational-age at birth.