The U.S. Vessel Traffic application is a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns, and download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts. To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript. Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

a packet sniffer software

Dear Scientist!This database contains data collected due to conducting study: "Analysis of the route safety of abnormal vehicle from the perspective of traffic parameters and infrastructure characteristics with the use of web technologies and machine learning" funded by National Science Centre Poland (Grant reference 2021/05/X/ST8/01669). The structure of files is arising from the aims of the study and numerous of sources needed to tailor suitable data possible to use as an input layer for neural network. You can find a following folders and files:1. Road_Parameters_Data (.csv) - which is data colleced by author before the study (2021). Here you can find information about technical quality and types of main roads located in Mazovia province (Poland). The source of data was Polish General Directorate for National Roads and Motorways. 2. Google_Maps_Data (.json) - here you can find the data, which was collected using the authors’ webservice created using the Python language, which downloaded the said data in the Distance Matrix API service on Google Maps at two-hour intervals from 25 May 2022 to 22 June 2022. The application retrieved the TRAFFIC FACTOR parameter, which was a ratio of actual time of travel divided by historical time of travel for particular roads.3. Geocoding_Roads_Data (.json) - in this folder you can find data gained from reverse geocoding approach based on geographical coordinates and the request parameter latlng were employed. As a result, Google Maps returned a response containing the postal code for the field types defined as postal_code and the name of the lowest possible level of the territorial unit for the field administrative_area_level. 4. Population_Density_Data (.csv) - here you can find date for territorial units, which were assigned to individual records were used to search the database of the Polish Postal Service using the authors' original web service written in the Python programming language. The records which contained a postal code were assigned the name of the municipality which corresponded to it. Finally, postal codes and names of territorial units were compared with the database of the Statistics Poland (GUS) containing information on population density for individual municipalities and assigned to existing records from the database.5. Roads_Incidents_Data (.json) - in this folder you can find a data collected by a webservice, which was programmed in the Python language and used for analysing the reported obstructions available on the website of the General Directorate for National Roads and Motorways. In the event of traffic obstruction emergence in the Mazovia Province, the application, on the basis of the number and kilometre of the road on which it occurred, could associate it later with appropriate records based on the links parameters. The data was colleced from 26 May to 22 June 2022.6. Weather_For_Roads_Data (.json) - here you can find the data concerning the weather conditions on the roads occurring at days of the study. To make this feasible, a webservice was programmed in the Python language, by means of which the selected items from the response returned by the www.timeanddate.com server for the corresponding input parameters were retrieved – geographical coordinates of the midpoint between the nodes of the particular roads. The data was colleced for day between 27 May and 22 June 2022.7. data_v_1 (.csv) - collected only data for road parameters8. data_v_2 (.csv) - collected data for road parameters + population density9. data_v_3 (.json) - collected data for road parameters + population density + traffic10. data_v_4 (.json) - collected data for road parameters + population density + traffic + weather + road incidents11. data_v_5 (.csv) - collected VALIDATED and cleaned data for road parameters + population density + traffic + weather + road incidents. At this stage, the road sections for which the parameter traffic factor was assessed to have been estimated incorrectly were eliminated. These were combinations for which the value of the traffic factor remained the same regardless the time of day or which took several of the same values during the course of the whole study. Moreover, it was also assumed that the final database should consist of road sections for traffic factor less than 1.2 constitute at least 10% of all results. Thus, the sections with no tendency to become congested and characterized by a small number of road traffic users were eliminated.Good luck with your research!Igor Betkier, PhD

This application provides an interactive experience to look up traffic count reports across the City of Cleveland. Traffic count reports are conducted using unmanned vehicle counter devices that detect the volume and speed of vehicular traffic.InstructionsEach point represents a single traffic count observation that was conducted since 2019.Zoom into a point, click on it to generate a pop-up that presents summary statistics and a PDF link for each report.Use Filter or Search to narrow down to your area or time of interest.Data GlossarySee: Cleveland Traffic Count Reports - Overview (arcgis.com)Update FrequencyMonthly, at the end of each monthThis application uses the following dataset(s):Cleveland Traffic Count ReportsContactsCity Planning Commission

Update Frequency: Daily

This data-set includes traffic crash information including case number, accident date and the location.

• Reportable crash reports can take up to 10 business days to appear after the date of the crash if there are no issues with the report.

• If you cannot find your crash report after 10 business days, please call the Milwaukee Police Department Open Records Section at (414) 935-7435 for further assistance.

• Non-reportable crash reports can only be obtained by contacting the Open Records Section and will not show up in a search on this site. A non-reportable crash is any accident that does not:

1) result in injury or death to any person

2) damage government-owned non-vehicle property to an apparent extent of $200 or more

3) result in total damage to property owned by any one person to an apparent extent of $1000 or more.

• All MV4000 crash reports, completed by MPD officers, will be available from the Wisconsin Department of Transportation (WisDOT) Division of Motor Vehicles (DMV) Accident Records Unit, generally 10 days after the incident.

Online Request: Request your Crash Report online at WisDOT-DMV website, https://app.wi.gov/crashreports.

Mail: Wisconsin Department of Transportation Crash Records Unit P.O. Box 7919 Madison, WI 53707-7919

Phone: (608) 266-8753

To download XML and JSON files, click the CSV option below and click the down arrow next to the Download button in the upper right on its page.

Here are a few use cases for this project:

1. Advanced Driver Assistance Systems (ADAS): The "mydata" model can be implemented in ADAS for real-time identification of various road elements. It can recognize other vehicles, people, bicycles, traffic lights and signs, helping to improve safety, navigation and overall driving experience.

2. Traffic Monitoring and Control Systems: This model could find application in smart city initiatives where it could monitor traffic movement, identify types of vehicles, and interpret various traffic lights and signals.

3. Autonomous Vehicle Technology: The capability to identify vehicles, signs, and signals is essential for self-driving cars. The "mydata" model can be used to provide crucial inputs for the AI of autonomous vehicles.

4. Augmented Reality Navigation Apps: Navigation applications could use the "mydata" model to accurately recognize and interpret road elements in real-time, providing users with enriched and interactive directions.

5. Security and Surveillance Systems: The model can be used in security systems for identifying unauthorized or suspicious activities such as people or vehicles in restricted areas, speeding, or non-compliance with traffic lights and signs.

Context

The data presented here was collected in a network section from Universidad Del Cauca, Popayán, Colombia by performing packet captures at different hours, during morning and afternoon, over six days (April 26, 27, 28 and May 9, 11 and 15) of 2017. A total of 3.577.296 instances were collected and are currently stored in a CSV (Comma Separated Values) file.

Content

This dataset contains 87 features. Each instance holds the information of an IP flow generated by a network device i.e., source and destination IP addresses, ports, interarrival times, layer 7 protocol (application) used on that flow as the class, among others. Most of the attributes are numeric type but there are also nominal types and a date type due to the Timestamp.

The flow statistics (IP addresses, ports, inter-arrival times, etc) were obtained using CICFlowmeter (http://www.unb.ca/cic/research/applications.html - https://github.com/ISCX/CICFlowMeter). The application layer protocol was obtained by performing a DPI (Deep Packet Inspection) processing on the flows with ntopng (https://www.ntop.org/products/traffic-analysis/ntop/ - https://github.com/ntop/ntopng).

For further information and if you find this dataset useful, please read and cite the following papers:

Research Gate: https://www.researchgate.net/publication/326150046_Personalized_Service_Degradation_Policies_on_OTT_Applications_Based_on_the_Consumption_Behavior_of_Users

Research Gate: https://www.researchgate.net/publication/335954240_Consumption_Behavior_Analysis_of_Over_The_Top_Services_Incremental_Learning_or_Traditional_Methods

Springer: https://link.springer.com/chapter/10.1007/978-3-319-95168-3_37

IEEExplore https://ieeexplore.ieee.org/document/8845576

Research Gate: https://www.researchgate.net/publication/345990587_Smart_User_Consumption_Profiling_Incremental_Learning-based_OTT_Service_Degradation

IEEExpore https://ieeexplore.ieee.org/document/9258898

Acknowledgements

I would like to thank Universidad Del Cauca for supporting the research that generated this dataset and Colciencias for my PhD scholarship.

Inspiration

Considering that most of the network traffic classification datasets are aimed only at identifying the type of application an IP flow holds (WWW, DNS, FTP, P2P, Telnet,etc), this dataset goes a step further by generating machine learning models capable of detecting specific applications such as Facebook, YouTube, Instagram, etc, from IP flow statistics (currently 75 applications).

Cristiano Ronaldo has one of the most popular Instagram accounts as of April 2024.

              The Portuguese footballer is the most-followed person on the photo sharing app platform with 628 million followers. Instagram's own account was ranked first with roughly 672 million followers.

              How popular is Instagram?

              Instagram is a photo-sharing social networking service that enables users to take pictures and edit them with filters. The platform allows users to post and share their images online and directly with their friends and followers on the social network. The cross-platform app reached one billion monthly active users in mid-2018. In 2020, there were over 114 million Instagram users in the United States and experts project this figure to surpass 127 million users in 2023.

              Who uses Instagram?

              Instagram audiences are predominantly young – recent data states that almost 60 percent of U.S. Instagram users are aged 34 years or younger. Fall 2020 data reveals that Instagram is also one of the most popular social media for teens and one of the social networks with the biggest reach among teens in the United States.

              Celebrity influencers on Instagram
              Many celebrities and athletes are brand spokespeople and generate additional income with social media advertising and sponsored content. Unsurprisingly, Ronaldo ranked first again, as the average media value of one of his Instagram posts was 985,441 U.S. dollars.

LAMMPS is a classical molecular dynamics code that models an ensemble of particles in a liquid, solid, or gaseous state. It can model atomic, polymeric, biological, metallic, granular, and coarse-grained systems using a variety of force fields and boundary conditions This dataset is comprised of traces obtained from application LAMMPS by processing the otf2 output produced by the SCALASCA utility once the LAMMPS code has been instrumented. Specifically, five variants of LAMMPS have been analysed where the node count varies from 24 to 192.

These layers are used in the The U.S. Vessel Traffic application; a web-based visualization and data-access utility created by Esri. Explore U.S. maritime activity, look for patterns of vessel activity such as around ports and fishing grounds, or download manageable subsets of this massive data set. Vessel traffic data are an invaluable resource made available to our community by the US Coast Guard, NOAA and BOEM through Marine Cadastre. This information can help marine spatial planners better understand users of ocean space and identify potential space-use conflicts.To download this data for your own analysis, explore the Download Options, navigate to a NOAA Electronic Navigation Chart area of interest, and make your selection. This data was sourced from the Automatic Identification System (AIS) provided by USCG, NOAA, and BOEM through Marine Cadastre and aggregated for visualization and sharing in ArcGIS Pro. This application was built with the ArcGIS API for JavaScript.Access this data as an ArcGIS Online collection here. Learn more about AIS tracking here. Find more ocean and maritime resources in Living Atlas. Inquiries can be sent to Keith VanGraafeiland.

Here are a few use cases for this project:

1. Smart Parking Assistance: Utilize the Tarik-Tyane-annotations model to develop an application that assists drivers in locating available parking spots, including valid and invalid spots like handicapped and fire hydrant zones, in real-time, making the parking experience more convenient and efficient.

2. Traffic Management: Integrate the model into city traffic management systems to monitor and manage parking spaces in high traffic areas. By identifying improper parking, such as in fire hydrant zones, bus stops, or handicapped spots, authorities can enforce parking regulations proactively.

3. Urban Planning and Analysis: Use the Tarik-Tyane-annotations model to analyze public parking data, identifying patterns and trends related to parking spots' utilization. This can help city planners make informed decisions on the allocation and distribution of parking spaces, optimizing infrastructure for future growth.

4. Navigation App Integration: Enhance navigation applications like Google Maps or Waze with the model's parking spot information, allowing users to find not only available parking spaces nearby but also information on rules and restrictions in real-time, avoiding fines or inconveniences.

5. Emergency Response: Equip emergency response vehicles, such as fire trucks and ambulances, with the Tarik-Tyane-annotations model to identify fire hydrant locations or restricted parking areas quickly. This can help emergency services to navigate congested areas and ensure unblocked access to critical infrastructure during emergencies.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - KNIME Lab Manual (1st ed.). CRC Press. https://doi.org/10.1201/9781003304357 Update Log the dataset and tool for ABM Crime Simulation were updated on August 3, 2023, the toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv R_ArcGIS_Tools.tbx (PCAandFA, SpatialRegressionModel) 9 Louisiana Louisiana.gdb MLR Tools Pro.tbx R_ArcGIS_Tools.tbx (RegionalizationR) 10 SimuCity SimuCity.gdb Garin-Lowry.tbx 11A Columbus Columbus.gdb R_ArcGIS_Tools.tbx (WasteCommuteR) 11B Xiantao XT.gdb R_ArcGIS_Tools.tbx (MiniMaxR, MAEP) 12A Chicago ZoneEffect.gdb 12B BRMSA BRMSAmc.gdb MCSimulation.tbx 13 ABMSIM Data ...

The data shared here are presented in: Knight, K.L.; Hou, G.; Bhaskar, A.S.; Chen, S. Assessing the Use of Dual-Drainage Modeling to Determine the Effects of Green Stormwater Infrastructure on Roadway Flooding and Traffic Performance. Water 2021, 13, 1563. https://doi.org/10.3390/w13111563

Summary:

I. INPUT FILES

Input data including: stormwater data, DEM, study area outline, service requests, recurring flood locations, precipitation data, and streamflow data. Project files including Pre GSI model, 4 GSI scenario models, and validation model. Pre- and post-processing scripts including: LID application spreadsheet, stormwater data correction, 1D and 2D output data processing. Includes description of labeling method for output data files. The coordinate system of all project files and output data: NAD83 Colorado Central State Plane (US feet)

Stormwater network data (storm manholes, storm inlets, storm sewer mains, streams, and storm water detention and water quality areas) was acquired from the City and County of Denver Open Data catalog (https://www.denvergov.org/opendata)

DEM data (1-meter and 3-meter resolution) was acquired from the National Elevation Dataset (NED) using the United States Geologic Survey (USGS) The National Map (TNM) Download Client (https://apps.nationalmap.gov/downloader/#/)

Study area outline and the bounding layer that delineates roadways from surrounding area are in NAD83 Colorado Central State Plane (US Feet).

Other landuse data (building outlines, impervious area, street centerlines) was acquired from the City and County of Denver Open Data catalog (https://www.denvergov.org/opendata).

Street polygons were produced from the street centerlines data and a buffer representing 1/2 the street width determined from the street centerline attributes of lane numbers and roadway type.

Citizen service requests and known areas of recurring flooding datasets are not publically available, for more information contact Dr. Aditi Bhaskar

Precipitation data was downloaded from USGS at 5 raingages. data files include date, time, and 5-minute precipitation data in inches.

Streamflow data was downloaded from USGS 06711575. Data files include date, time, and 5-minute streamflow data in cubic feet per second.

The LID inputs for each subcatchment utilized a single representative 'GSI unit' based on the design of a street planter bioswale from the City and County of Denver Ultra Urban Report. The LID input for each subcatchment for 1%, 2.5%, 3.5%, and 5% GSI scenarios are included in the table. There are no LIDs applied to the Pre GSI or Validation scenarios.

II. PCSWMM FILES

PCSWMM project files include the '.inp' file and the relevant project file folder that contains the input layers for each PCSWMM project. The name of the project file folder and the '.inp' file are the same and need to be located in the same folder to run simulations. Input layers in the project file folders can be edited and viewed in ArcMap as well, but it is not recommended to directly edit PCSWMM input layers in ArcMap. Rather, create a copy of the desired layer, edit in ArcMap, open the copy in PCSWMM, and update the PCSWMM input layer using the 'import GIS/CAD' tool.

III. MATLAB FILES

The raw stormwater network data from the City and County of Denver was filled and corrected using the methods summarized in Appendix A of the Thesis document. The purpose of this data pre-processing was to fill and correct the missing stormwater network data and convert all known data into the proper formatting for input into PCSWMM. All data is projected into NAD83 Colorado Central State Plane (US feet) coordinate system and clipped to the study boundary.

The hydrograph outputs from the above scenarios were processed using MATLAB. The output streamflow data for each scenario was compared to the observed hydrograph at USGS streamgage 06711575. Additionally, the calibration and validation model outputs were analyzed compared to the observed streamflow data including statistical analysis. All precipitation data is in inches; all streamflow data is in cubic feet per second.

IV. ROAD NETWORK

These are data used for the GIS road network in the traffic modeling by Guangyang Hou (guangyanghou1986@gmail.com).

DPSNN-STDP is a natively distributed mini-application benchmark representative of plastic spiking neural network simulators. Processes describe synapses in input to cluster of neurons with an irregular interconnection topology, with complex inter-process traffic patterns broadly varying in time and per process. This dataset is comprised of textual data set describing all the details of the inter-processor communication for five different neural network configurations. Each file contains a 3D numerical matrix describing, for each millisecond time-step of simulated neural activity (and for a total duration of 3 seconds of simulation) the size of the payload to be exchanged between all pairs of processes.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

As of January 2024, Instagram was slightly more popular with men than women, with men accounting for 50.6 percent of the platform’s global users. Additionally, the social media app was most popular amongst younger audiences, with almost 32 percent of users aged between 18 and 24 years.

              Instagram’s Global Audience

              As of January 2024, Instagram was the fourth most popular social media platform globally, reaching two billion monthly active users (MAU). This number is projected to keep growing with no signs of slowing down, which is not a surprise as the global online social penetration rate across all regions is constantly increasing.
              As of January 2024, the country with the largest Instagram audience was India with 362.9 million users, followed by the United States with 169.7 million users.

              Who is winning over the generations?

              Even though Instagram’s audience is almost twice the size of TikTok’s on a global scale, TikTok has shown itself to be a fierce competitor, particularly amongst younger audiences. TikTok was the most downloaded mobile app globally in 2022, generating 672 million downloads. As of 2022, Generation Z in the United States spent more time on TikTok than on Instagram monthly.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

Switzerland is leading the ranking by population share with mobile internet access , recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).