All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Click here to download data from 2011 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2011/fpz8-jqf4

Click here to download data from 2012 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2012/as38-8eb5

Click here to download data from 2013 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2013/hybb-af8n

Click here to download data from 2014 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2014/vtzg-7562

Click here to download data from 2015 - https://data.cityofnewyork.us/dataset/311-Service-Requests-From-2015/57g5-etyj

The District of Columbia offers several interactive online visualizations highlighting data and information from various fields of interest such as crime statistics, public school profiles, detailed property information and more. The web visualizations in this group present data coming from agencies across the Government of the District of Columbia. Click each to read a brief introduction and to access the site. This app is embedded in https://opendata.dc.gov/pages/dashboards.

This dataset had adapted from 'Credit Card Churn Prediction: https://www.kaggle.com/datasets/anwarsan/credit-card-bank-churn ' for visualization in our university project. We have modified customer information, spending behavior, and also added revenue targets.

Scenario 🕶️ In 2019, the marketing team launched a campaign to attract millennial customers (born 1980-1996) with the goal of increasing revenue and enhancing the brand's appeal to a younger audience.
As the BI team, your task is to create a dashboard for users. 1. The Vice President of Sales wants to view the performance of the credit business. 2. The marketing team is interested in understanding customer segments and customer spending to measure Customer Lifetime Value (CLV) and Marketing Cost per Acquired Customer (MCAC).

⚠️Note: This is just a suggestion to guide the creation of the dashboard

Example in Tableau

Executive summary https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F508a2d2d89dabdfd368743f86c2a71e1%2Fexecutive%20overview.JPG?generation=1696110593484137&alt=media" alt=""> Customer behavior https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10099382%2F1e4a1f62a25eab3c6707d002243894c7%2Fcustomer_behaviour.JPG?generation=1696110689732332&alt=media" alt="">

Click on any of the images below to explore an interactive data visualization:

The map shows the georeferencing of the libraries and archives present in the Liguria Region. The data derive from the Liguria Region's official database, managed by the Culture and Entertainment sector. Coverage: Entire Regional Territory - Origin: Georeferencing on Regional Technical Map - sc. 1:5000 - ETRF89 Projection System

This spreadsheet contains a collection of over 230 data visualisations about public finances from media organisations, journalists, civil society organisations, advocacy groups, civic hackers, companies and public institutions. In order to build the collection I started with a collection of projects derived from another study mapping “open budget data” on digital media (Gray, 2015). Over 65% of the 120 fiscal data projects identified through the study used visualisations to present information about public finances. Examples were also incorporated from other lists, including relevant items from a database of 466 projects from The Guardian and the New York Times from between 2000 and 2015 (Rooze, 2015), as well as from expert data visualisation blogs such as Infosthetics and Visual Complexity. Further examples were solicited from expert mailing lists, forums and targeted outreach via email and social media. Analyses of the data visualisations are forthcoming in several publications. The collection will continue to be updated periodically. If you have suggestions for projects to add, please get in touch: http://jonathangray.org/contact/

References Gray, J. (2015) "Open Budget Data: Mapping the Landscape". Available at: http://dx.doi.org/10.2139/ssrn.2654878Rooze, M. (2015) "News Graphics Collection". Available at: http://collection.marijerooze.nl/

Information level associated with the Registry of Sites to be remediated pursuant to art. 251 of Legislative Decree 152/2006 and by art. 8 of the Regional Law 10/2009. The data derive from the census of the sites subject to reclamation and/or safety measures or, subjected to risk analysis following the procedure pursuant to art. 242 of Legislative Decree 152/06. The alphanumeric information of each site can be queried from the centroid to which the relative administrative perimeter is associated, consisting of the envelope of the cadastral maps subject to reclamation and/or safety measures or subjected to risk analysis. The registry is periodically updated by the Ecology Sector of the Liguria Region - Coverage: Entire Regional Territory - Origin: Localization on Regional Technical Map

The Hubway trip history data includes every trip taken through Nov 2013 ? with date, time, origin and destination stations, plus the bike number and more. Data from 2011/07 through 2013/11 The Hubway trip history data Every time a Hubway user checks a bike out from a station, the system records basic information about the trip. Those anonymous data points have been exported into the spreadsheet. Please note, all private data including member names have been removed from these files. What can the data tell us? The CSV file contains data for every Hubway trip from the system launch on July 28th, 2011, through the end of September, 2012. The file contains the data points listed below for each trip. We ve also posed some of the questions you could answer with this dataset - we re sure you.ll have lots more of your own. Duration - Duration of trip. What s the average trip duration for annual members vs. casual users? Start date - Includes start date and time. What are the peak Hubway hours?

This dataset contains a list of all the open datasets we have collected through the Socrata API and is used in developing the PRIVEE interface. We have enriched the dataset with metadata information of these datasets, including their columns, tags, and the number of rows. We have also identified some of the quasi-identifiers present in these datasets.

Government Open Data Management Platform Market Outlook

The global government open data management platform market size was valued at USD 2.5 billion in 2023 and is projected to reach USD 6.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. The rising emphasis on transparency, accountability, and citizen engagement by governments worldwide is a significant driving factor for this market's growth.

The proliferation of digital governance initiatives is one of the primary growth factors for the government open data management platform market. Governments across the globe are increasingly adopting digital platforms to improve public service delivery, enhance citizen engagement, and increase operational efficiency. By providing open access to data, these platforms enable better decision-making and foster innovation among various stakeholders, including businesses, researchers, and the general public. This trend is further accelerated by the growing demand for data-driven governance and public policies that are more responsive and accountable.

Moreover, advancements in data analytics and artificial intelligence (AI) are significantly contributing to the growth of the government open data management platform market. Modern open data platforms are increasingly incorporating sophisticated analytics tools and AI capabilities to offer more insightful and actionable data. These technological advancements enable governments to leverage large datasets for predictive analytics, enhancing their ability to anticipate and respond to public needs effectively. Additionally, the integration of AI in data management platforms helps in automating data processing tasks, thereby improving efficiency and reducing operational costs.

The increasing focus on smart city initiatives is another critical factor driving the demand for government open data management platforms. Smart cities rely heavily on data to optimize urban planning, improve traffic management, enhance public safety, and provide efficient public services. Open data platforms play a crucial role in these initiatives by providing a centralized repository for diverse data sets collected from various sensors and systems across the city. This data can be accessed and analyzed by different stakeholders to develop innovative solutions that address urban challenges and improve the quality of life for citizens.

Government Software plays a pivotal role in the development and implementation of open data management platforms. These software solutions are designed to meet the specific needs of government agencies, providing robust tools for data collection, analysis, and dissemination. By leveraging government software, agencies can ensure data accuracy, enhance transparency, and improve public service delivery. The integration of advanced features such as data visualization, predictive analytics, and machine learning within government software allows for more informed decision-making and policy formulation. As governments continue to prioritize digital transformation, the demand for specialized government software solutions is expected to rise, driving further growth in the open data management platform market.

From a regional perspective, North America holds a significant share of the government open data management platform market, driven by the early adoption of digital governance solutions and the presence of major technology providers in the region. Europe is also a prominent market, with several countries implementing open data policies to promote transparency and citizen participation. The Asia Pacific region is expected to witness substantial growth during the forecast period, supported by increasing government initiatives to digitize public services and the rising adoption of smart city projects. Latin America, the Middle East, and Africa are also anticipated to show promising growth, although at a comparatively slower pace due to varying levels of technological infrastructure and government investment in these regions.

Component Analysis

The government open data management platform market is segmented by component into software and services. Software components include the core data management platforms, which facilitate the collection, storage, and dissemination of open data. These software solutions are designed to handle large volumes of data and provide various functionalities such as data analytics, visualization, and integration. The increasi

The information level Risk shows the risk of high coast, divided into moderate, medium, high and very high, the risk of low coast, divided into moderate, medium, high and very high, and the risk located at maritime works. In addition, the detailed isometric lines, the current shoreline, the locations of the cornerstones and Lidar sections of the emerged beach profiles, the boundary limits are reported as useful elements.Scope 8 approved by Presidential Decree No 7 of 23 August 2016, scope 15 approved by Presidential Decree No 18 of 25 September 2012 and scopes 16.17.18 approved by Presidential Decree No 2 of 21 February 2023 are represented. - Coverage:PTAMC AMBITI 08 - 15 - 16 - 17 - 18 - Year: 2020- Projection System: Gauss Boaga - Cast West Projected

Storytelling with Data is organized into 2 main parts. Part I comprises four modules, and is collectively aimed at introducing students to the process of creating "data stories" using Python data science tools: Module 1: What makes a good story? Module 2: Visualizing data Module 3: Python and Jupyter notebooks as a medium for data storytelling Module 4: Data science tools Part II is project-based, and revolves around mini data science projects. For each project, one or more students choose a question and dataset to explore and turn into a data story. Each week students and groups will report on their progress with the latest iterations of their stories. Students should aim to participate in three or more projects during Part II of the course. At students' discretion, those three (or more) projects may comprise the same questions and/or datasets (e.g., whereby each story builds on the previous story), or multiple questions and/or datasets that may or may not be related. In addition, students are encouraged to build off of each others' code, projects, and questions. Projects and project groups should form organically and should remain flexible to facilitate changing goals and interests.

This site provides National level geospatial data within the open public domain that can be useful to support tribal community resiliency, research, and more. The data is available for download as CSV, KML, Shapefile, and accessible via web services to support application development and data visualization. This site contains data created and maintained by the Branch of Geospatial Support.

Geolocation based on the toponymic address associated with each school. The map shows the geo-referencing of the public higher education and professional training institutes of the entire regional territory. For further information, refer to the "Liguria Digital School Project" (http://www.scuoladigitaleliguria.it/) - acquisition scale 1:5000 - Year: 2020 - Projection system: Gauss Boaga - West Zone - Territorial coverage: entire regional territory

Green stones is a commonly used term used to identify ophiolites, igneous and metamorphic rocks, likely to contain asbestos minerals. The dataset derives from the extrapolation of the official data of the most recent geological surveys carried out in the context of national and regional projects such as the CARG and CGR projects. The mapping, at a scale of 1:25000, already published in the cartographic repertoire since 2008, has been updated with the new geological surveys of the Spigno Monferrato sheet. It constitutes a level of knowledge for the management and use of excavated earth and rocks and also contains an indication of the specific lithology of the rocky substrate. - Coverage: Provinces of Savona, Genoa, La Spezia - Origin: Digitization from CARG and CGR geological maps

The Data Visualization Workshop II: Data Wrangling was a web-based event held on October 18, 2017. This workshop report summarizes the individual perspectives of a group of visualization experts from the public, private, and academic sectors who met online to discuss how to improve the creation and use of high-quality visualizations. The specific focus of this workshop was on the complexities of "data wrangling". Data wrangling includes finding the appropriate data sources that are both accessible and usable and then shaping and combining that data to facilitate the most accurate and meaningful analysis possible. The workshop was organized as a 3-hour web event and moderated by the members of the Human Computer Interaction and Information Management Task Force of the Networking and Information Technology Research and Development Program's Big Data Interagency Working Group. Report prepared by the Human Computer Interaction And Information Management Task Force, Big Data Interagency Working Group, Networking & Information Technology Research & Development Subcommittee, Committee On Technology Of The National Science & Technology Council...

Bibliography selected to start an open science project in a University. Ppt presentation is added for use in Spanish.

We provide a dataset that includes visualizations of eye-tracking scanpaths with a particular focus Autism Spectrum Disorder (ASD). The key idea is to transform the dynamics of eye motion into visual patterns, and hence diagnosis-related tasks could be approached using image analysis techniques. The image dataset is publicly available to be used by other studies aiming to experiment the usability of eye-tracking within the ASD context. It is believed that the dataset can allow for the development of further interesting applications using Machine Learning or image processing techniques. For more info, please refer to the publication below and the project website.Original Publication:Carette, R., Elbattah, M., Dequen, G., Guérin, J, & Cilia, F. (2019, February). Learning to predict autism spectrum disorder based on the visual patterns of eye-tracking scanpaths. In Proceedings of the 12th International Conference on Health Informatics (HEALTHINF 2019).Project Website:https://www.researchgate.net/project/Predicting-Autism-Spectrum-Disorder-Using-Machine-Learning-and-Eye-Trackinghttps://mahmoud-elbattah.github.io/ML4Autism/

The dataset contains example gwfvisdb files required to run the examples displayed on the GWF-VIS visualization gallery (https://gwf-vis.usask.ca/#gallery). The code associated with each visualization example contains a 'data_source' variable. This variable can be examined to see where the data is currently hosted. Users may also upload the data file on other static file servers and update the 'data_source' to replicate the visualizations.

The Government Open Data Management Platform market is experiencing robust growth, projected to reach $163.29 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 9.73% from 2025 to 2033. This expansion is fueled by increasing government initiatives promoting transparency and citizen engagement through open data, coupled with the rising need for efficient data management and analysis to support evidence-based policymaking. Governments worldwide are recognizing the value of open data in improving public services, fostering economic development, and enhancing citizen trust. The market is segmented by end-users, encompassing large enterprises and SMEs, reflecting the diverse needs and technological capabilities across different government bodies. Leading companies like Microsoft, Oracle, and Esri are actively shaping the market landscape through innovative platform offerings and strategic partnerships, while smaller, specialized firms cater to niche requirements. The North American market currently holds a significant share, driven by early adoption and advanced technological infrastructure, but regions like Asia-Pacific are showing considerable potential for future growth, spurred by rapid digital transformation and increasing government investment in data infrastructure. Market restraints include challenges in data standardization, security concerns, and the need for skilled professionals to manage and analyze complex datasets. However, ongoing technological advancements in areas such as AI and machine learning, coupled with increasing government funding for digital transformation, are expected to mitigate these challenges and drive further market expansion. The competitive landscape is characterized by a blend of established technology giants and specialized open data platform providers. Strategies for success include offering scalable and secure platforms, providing robust data visualization and analytics capabilities, ensuring ease of data integration with existing government systems, and providing strong customer support and training. Industry risks include the evolving regulatory landscape surrounding data privacy and security, competition from open-source alternatives, and the potential for integration challenges with legacy systems. The historical period (2019-2024) likely showed a growth trajectory setting the stage for the robust forecast period (2025-2033). Future market evolution hinges on successful navigation of these challenges and the sustained commitment of governments to open data initiatives.