For conversion of GPS to KML we used the GPS datatypes such asGPGGAwhich is described as Global Positioning System Fix Data andGPRMCwhichis described as Recommended minimum specific GPS/Transit data. To begin with, we first created two data frames with the given GPS data, by filtering out GPGGA and GPRMC types. As per our observation, we could see that GPGGAtype gave us information like Longitude, Latitude, Altitude, and Fix Quality (ifits 0 0 then it is invalid). On the other hand, GPRMC type fetched information like T Time, Speed in knots and Navigation receiver warning. Out of the other information from GPRMC datatype, we were only interested in speed and re-ceive warning. So we copied those columns and combined these columns into pandas data frame created for type GPGGA. Moving to the next part which is conversion of data. We converted the Latitude and Longitude of$GPGGA us-ing formula DDMMSSSS eg, 4533.35 is 45 degrees and 33.35 minutes. ”.35” of a minute is exactly 21 seconds. We checked for the direction and assigned proper sign as per the directon (W is -ve and E is +ve). We then converted all the numeric columns in dataframe for further calculation. Finally created a tuple with latitude, longitude and speed. This list of tuples was added as coordinates. One of the major challenge was to understand the GPS data.

Maps are currently experiencing a paradigm shift from static representations to dynamic platforms that capture, visualize and analyse new data, bringing different possibilities for exploration and research. The first objective of this paper is to present a map that illustrates, for the first time, the real flow of casual cyclists and bike messengers in the city of Madrid. The second objective is to describe the development and results of the Madrid Cycle Track initiative, an online platform launched with the aim of collecting cycling routes and other information from volunteers. In the framework of this initiative, different online maps are presented and their functionalities described. Finally, a supplemental video visualizes the cyclist flow over the course of a day.

Context

Selected activities from Strava for use in Kaggle Kernels only. GPX files from a GPS device.

Content

Laramie_Enduro_2014.gpx

Laramie_Enduro_2015.gpx

Laramie_Enduro_2016.gpx

West_Laramie_Bike_Ride.gpx

Colorado_Belford_Oxford_and_Missouri_Mountains_Hike.gpx

Colorado_Longs_Peak_and_Chasm_Lake_Hike.gpx

Berenthanti_Ghorepani_Ghandruk_Loop_Hike_Day_1_of_3_.gpx

Berenthanti_Ghorepani_Ghandruk_Loop_Hike_Day_2_of_3_.gpx

Berenthanti_Ghorepani_Ghandruk_Loop_Hike_Day_3_of_3_.gpx

Acknowledgements

Strava

Inspiration

Strava Labs

This dataset is composed by a GPX file. It can be used to practice data visualization and more specifically GPS coordinates manipulation. Librairies like gpxpy or geopy (Nominatim API) can be practiced through this dataset.

Since the early 1990s, the GFZ has operated a global GNSS station network with currently about 70 stations for precise satellite clock & orbit determination, realization of the terrestrial reference frame, radio occultation measurements or studies on crust dynamics. A subset of these stations contributes also to the tracking networks of the International GNSS Service (IGS) and the EUREF Permanent GNSS Network (EPN). Other stations contribute to GFZ observatories (IPOC, DESERVE, TERENO), to the GPS Atmosphere Sounding Project (GASP), to WMO Global Climate Observing System Reference Upper-Air Network (GRUAN) or to other external cooperations. We offer data of 51 GFZ GNSS stations under this DOI. Nearly all stations are equipped with Javad or Septentrio hardware. Depending on the location and hardware they provide data of GPS (L1 / L2 / L5), GLONASS (L1 / L2 / L3), Galileo (E1 / E5a / E5b / E6), BeiDou (B1 / B2 / B3), QZSS (L1 / L2 / L5 / L6), NAVIC (L5), and SBAS (L1 / L5). The GNSS Station Nework Site (https://isdc.gfz-potsdam.de/gnss-station-network/) provides direct access to the 1s and 30s sampled RINEX data (near real-time, file based) and to real-time streams. Real-time streams are available for stations contributing to the IGS. Raw data GNSS binary raw observations are available upon request. All GFZ Stations follow the site guidelines of the International GNSS Service (https://kb.igs.org/hc/en-us/articles/202011433-Current-IGS-Site-Guidelines) Station specific metadata can be found at our metadata portal SEMISYS. An overview of the list of stations with direct links to the station specific metadata in semisys is available via ftp://datapub.gfz-potsdam.de/download/10.5880.GFZ.1.1.2020.001/2020-001_Ramatschi-et-al_List-of-GFZ-GNSS-Stations-with-links-to-SEMISYS.pdf.

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.The Service Locations feature class was created by Southern Geospatial Services (SGS) from a shapefile of customer service locations generated by dataVoice International (DV) as part of their agreement with the Town of Apex (TOA) regarding the development and implemention of an Outage Management System (OMS).Point features in this feature class represent service locations (consumers of TOA electric services) by uniquely identifying the features with the same unique identifier as generated for a given service location in the TOA Customer Information System (CIS). This is also the mechanism by which the features are tied to the OMS. Features are physically located in the GIS based on CIS address in comparison to address information found in Wake County GIS property data (parcel data). Features are tied to the GIS electric connectivity model by identifying the parent feature (Upline Element) as the transformer that feeds a given service location.SGS was provided a shapefile of 17992 features from DV. Error potentially exists in this DV generated data for the service location features in terms of their assigned physical location, phase, and parent element.Regarding the physical location of the features, SGS had no part in physically locating the 17992 features as provided by DV and cannot ascertain the accuracy of the locations of the features without undertaking an analysis designed to verify or correct for error if it exists. SGS constructed the feature class and loaded the shapefile objects into the feature class and thus the features exist in the DV derived location. SGS understands that DV situated the features based on the address as found in the CIS. No features were verified as to the accuracy of their physical location when the data were originally loaded. It is the assumption of SGS that the locations of the vast majority of the service location features as provided by DV are in fact correct.SGS understands that as a general rule that DV situated residential features (individually or grouped) in the center of a parcel. SGS understands that for areas where multiple features may exist in a given parcel (such as commercial properties and mobile home parks) that DV situated features as either grouped in the center of the parcel or situated over buildings, structures, or other features identifiable in air photos. It appears that some features are also grouped in roads or other non addressed locations, likely near areas where they should physically be located, but that these features were not located in a final manner and are either grouped or strung out in a row in the general area of where DV may have expected they should exist.Regarding the parent and phase of the features, the potential for error is due to the "first order approximation" protocol employed by DV for assigning the attributes. With the features located as detailed above, SGS understands that DV identified the transformer closest to the service location (straight line distance) as its parent. Phase was assigned to the service location feature based on the phase of the parent transformer. SGS expects that this protocol correctly assigned parent (and phase) to a significant portion of the features, however this protocol will also obviously incorretly assign parent in many instances.To accurately identify parent for all 17992 service locations would require a significant GIS and field based project. SGS is willing to undertake a project of this magnitude at the discretion of TOA. In the meantime, SGS is maintaining (editing and adding to) this feature class as part of the ongoing GIS maintenance agreement that is in place between TOA and SGS. In lieu of a project designed to quality assess and correct for the data provided by DV, SGS will verify the locations of the features at the request of TOA via comparison of the unique identifier for a service location to the CIS address and Wake County parcel data address as issues arise with the OMS if SGS is directed to focus on select areas for verification by TOA. Additionally, as SGS adds features to this feature class, if error related to the phase and parent of an adjacent feature is uncovered during a maintenance, it will be corrected for as part of that maintenance.With respect to the additon of features moving forward, TOA will provide SGS with an export of CIS records for each SGS maintenance, SGS will tie new accounts to a physical location based on address, SGS will create a feature for the CIS account record in this feature class at the center of a parcel for a residential address or at the center of a parcel or over the correct (or approximately correct) location as determined via air photos or via TOA plans for commercial or other relevant areas, SGS will identify the parent of the service location as the actual transformer that feeds the service location, and SGS will identify the phase of the service address as the phase of it's parent.Service locations with an ObjectID of 1 through 17992 were originally physically located and attributed by DV.Service locations with an ObjectID of 17993 or higher were originally physically located and attributed by SGS.DV originated data are provided the Creation User attribute of DV, however if SGS has edited or verified any aspect of the feature, this attribute will be changed to SGS and a comment related to the edits will be provided in the SGS Edits Comments data field. SGS originated features will be provided the Creation User attribute of SGS. Reference the SGS Edits Comments attribute field Metadata for further information.

Uncorrected GPS/GNSS observational data collected using an Altus APS3G GNSS receiver on a 2m survey pole by Anne Hellie on the 19th and 20th of January, 2018 (UTC time).

The excel file “Uncorrected_data.xlsx” contains the output of eight different data files collected (SILLS to SILLS 7). The data has been compiled into the worksheet “Uncorrected_data”. The other worksheets contain copies of the notes from Carlson Surv PC (the software used to collect the points), along with a lot more metadata.

Using ArcGIS 10.3 and the XtoolsPro extension, “Uncorrected_data” has been made into an ArcGIS shapefile (*.shp) and GoogleEarth *.kml file. Both are named “4318_2018”, with their appropriate extensions.

Note that since these points were collected in autonomous mode (with no RTK or SBAS solutions), these are uncorrected data points, and have poor accuracy, particularly in the vertical (see the VSDV field for the vertical standard deviation).

The accuracy of these positions can be substantially improved by differentially correcting/post-processing the data. This can be done with the open source software RTKLIB. Using the Septentrio software (SBF converter), the raw data files (*.sbf files collected by the Septentrio unit) have been converted into Rinex *.obs files to facilitate this process. The base station files for the appropriate time period can be downloaded from the Geoscience Australia Geodesy ftp server. The closest base station (CORS) is located at Casey and known as “cas1”. ftp://ftp.ga.gov.au/geodesy-outgoing/gnss/data/highrate/2018/

NGO Kotkaklubi (Eagle Club) and 5D Vision Ltd have developed an online portal birdmap.5dvision.ee, to visualize GPS data of migratory birds. Current dataset serves as a copy of source data for this visualization. The portal operates since 2006. Birds of 12 different species have been monitored, total number of individuals has been more than 160. Currently over 850 thousand occurrences (GPS fixes) are stored in the database. The data is managed via web-based backend that enables different methods of importing gps data (Movebank, manual upload for different receiver data formats). As the dataset covers long period of monitoring different species, various types of GPS transmitters have been used. Illustrated data about species and individuals can also be added. To enable publishing of the data to GBIF, synchronization to PlutoF biological data management platform was developed. The aim of the Birdmap portal is to increase awareness, international communication about nature and its protection by combining the possibilities and advancements of modern technology with this attractive natural phenomenon as well as providing researches and decision-makers with visualized spatial data for using in environmental analysis of possible influence of large-scale infrastructure projects. (Due to legal reasons exact location data of species with highest protection category in Estonia, belonging to Estonian territory, are not disclosed in this dataset and are available on request.)

The construction of this data model was adapted from the Telvent Miner & Miner ArcFM MultiSpeak data model to provide interface functionality with Milsoft Utility Solutions WindMil engineering analysis program. Database adaptations, GPS data collection, and all subsequent GIS processes were performed by Southern Geospatial Services for the Town of Apex Electric Utilities Division in accordance to the agreement set forth in the document "Town of Apex Electric Utilities GIS/GPS Project Proposal" dated March 10, 2008. Southern Geospatial Services disclaims all warranties with respect to data contained herein. Questions regarding data quality and accuracy should be directed to persons knowledgeable with the forementioned agreement.The data in this GIS with creation dates between March of 2008 and April of 2024 were generated by Southern Geospatial Services, PLLC (SGS). The original inventory was performed under the above detailed agreement with the Town of Apex (TOA). Following the original inventory, SGS performed maintenance projects to incorporate infrastructure expansion and modification into the GIS via annual service agreements with TOA. These maintenances continued through April of 2024.At the request of TOA, TOA initiated in house maintenance of the GIS following delivery of the final SGS maintenance project in April of 2024. GIS data created or modified after April of 2024 are not the product of SGS.With respect to SGS generated GIS data that are point features:GPS data collected after January 1, 2013 were surveyed using mapping grade or survey grade GPS equipment with real time differential correction undertaken via the NC Geodetic Surveys Real Time Network (VRS). GPS data collected prior to January 1, 2013 were surveyed using mapping grade GPS equipment without the use of VRS, with differential correction performed via post processing.With respect to SGS generated GIS data that are line features:Line data in the GIS for overhead conductors were digitized as straight lines between surveyed poles. Line data in the GIS for underground conductors were digitized between surveyed at grade electric utility equipment. The configurations and positions of the underground conductors are based on TOA provided plans. The underground conductors are diagrammatic and cannot be relied upon for the determination of the actual physical locations of underground conductors in the field.

The dataset contains plant species occurrence records in the upper to mid Tana River Basin, digitized from preserved specimens at the East African Herbarium (EA), Nairobi. The dataset is a subset of the EA BRAHMS database generated over several years through various projects and day-to-day digitization efforts. Specimen records from the Tana River Basin were extracted from the EA BRAHMS dataset then cleaned and formatted in Darwin Core Standard. Data cleaning was done using Open Refine Software as well as the GPS Visualizer tool to generate altitude values from coordinates. Elevation data from GPS Visualizer is controlled from a variety of reputable sources including SRTM, ASTER, USGS and Google Maps. Other cleaning tasks included correcting spellings, filling in missing data, checking the taxonomy, dates, and subnational administrative areas. Records without coordinates were georeferenced using GEOLocate, Google Maps, and Gazetteers. The field altitude (m) was automatically generated from GPS Visualizer (https://www.gpsvisualizer.com/elevation) to generate missing elevation values. The process involved uploading a CSV format file with latitude and longitude values to the website which then sampled these coordinates against inbuilt elevation profile files (Digital Elevation Models) to give altitude values at specified coordinates. Most coordinates were also retrieved from gazetteers as they are legacy data.

These aerogeophysical data were collected as part of the ICECAP (International Collaborative Exploration of the Cryosphere through Airborne Profiling) collaboration in 2015/16 (ICP7) and 2016/17 (ICP8). These data were in part funded by the US National Science Foundation (grant PLR-1543452 to UTIG), Antarctic Gateway, ACE-CRC the G. Unger Vetlesen Foundation, and supported by the Australian Antarctic Division through project AAS-4346.

This data collection represents geolocated, time registered geophysical observations (L2 data). These data are derived from L0 and L1B data published as separate datasets. The data format are space delimited ASCII files, following the formats used for UTIG/AAD/NASA's predecessor ICECAP/OIB project at NASA's NSIDC DAAC. Fields are described in the # delimited detailed header for each granule.

We have installed 20 new Global Positioning System (GPS) markers in the West Pamir and the Tajik Depression and measured 25 markers once a year between 2013 and 2016 in survey mode. The stations are positioned along two dense NW-SE oriented profiles with an average spacing of 5-10 km. The profiles cross the Darvaz and the Vakhsh/Ilyak fault and thus monitor the recent slip of these two profiles, which are expected to accommodate the gravity-driven westward extrusion of the West Pamir into the Tajik Depression. Some of the stations include millimeter to centimeter offsets potentially caused by the 2015 Mw7.2 Sarez, Pamir, earthquake.

About this itemHubbard County has over 3,700 monumented PLSS corners across all 999 square miles of the county. The County Surveyors have been been making a deliberate effort over the past 25 years to re-certify every corner with modern GPS coordinates. These certificates are heavily utilized by area surveyors and Hubbard County Staff alike. To better support the efforts of the County Surveyors, Hubbard County GIS has create a publicly available interface to view Certificate of Government Corners for every PLSS corners in the county. Users of the map can also quickly determine the general date of when a certificate was last recorded. The map also includes an mosaic overlay for the original PLSS (from the State of MN), and PDFs of the original PLSS surveys for each township. Author/ContributorJack BoveeOrganizationHubbard County, MN Org Websitehttps://www.co.hubbard.mn.us/

UFO Sightings (Location & Time)

Geocoded Sightings with Full Content and Standardized Attributes

By Tim Renner [source]

About this dataset

Welcome to the world of UFO sightings! This vast dataset contains records of reported UFO sightings from North America, including detailed information about the time, location, duration, shape and more for each sighting. All reports come directly from the NUFORC site's public database. With this dataset you can uncover everything from strange glowing orbs in the night sky to mysterious cylindrical objects circling high above us.

What will you find? Dive deep into a world of mystery as you explore date and time stamps of sightings along with GPS coordinates and full descriptions of what was seen in communities near and far. Examine shapes that range beyond simple circles or triangles; zig zags, chevrons or crescents may all make an appearance here - who knows what strange anomalies lurk in these reports? For those seeking something concrete look no further: each record also contains a direct link to the original report posted on NUFORC’s website where further details are provided. So study up on your ESTs (Estimated Time Sightings) - your journey begins now!

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How to use the dataset

UFO sightings have long been a subject of curiosity and debate among people from all walks of life. With the ease of global travel, it's now possible to uncover mysterious UFO sightings from all over the world, including North America. This dataset contains detailed reports on hundreds of thousands of UFOs reported in North America over the past five years, giving researchers an unprecedented opportunity to investigate these phenomena.

In this guide we will explore how to get started with using this dataset and extracting valuable insights about unknown aircrafts in our skies. To begin with, let's take a look at some key pieces of information contained within the dataset.

There is information regarding the date_time and duration when it was seen – enabling us to investigate trends in visibility across various timeframes or locations if necessary - as well as details about the reported objects such as their country , city , state , and most notably – its physical description detailed under the field ‘shape’ (e.g triangle-shaped) . Other essential attributes contained within this dataset include text research--summarized under ‘summary’–and links directly taking users to further information about particular reports (covered by ‘report_link’). Moreover; stats contains fused raw records; consisting also date/time etc but expressed differently than date_time field - nonetheless useful for other analysis scenarios than provided here ;

By leveraging data science techniques such as clustering algorithms or trend analyses – academics or curious enthusiasts alike will not only be able to make note of patterns related specifically with individual sightings but also potentially cast light on some unsolved mysteries regarding aerial activity buzzed around since ancient times.

Research Ideas

• Building a geographic heatmap of UFO sightings around North America over time that can track spikes in particular regions to better understand where they may originate from.
• Creating a Natural Language Processing algorithm that classifies the features of each UFO sighting, such as shape and duration, in order to more effectively detect patterns between sightings or compare them against one another for research purposes.
• Using city location data to calculate the distance travelled by certain UFOs in order to measure the speed by which they traverse space and analyze any potential relationship between speed and sighting attributes like shape or duration with other information such as strange anomalies detected on the ground nearby or any other correlated variables from surrounding areas

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

Unknown License - Please check the dataset description for more information.

Columns

File: nuforc_reports.csv | Column name | Description | |:----------------|:------------------------------------------------------| | level_0 | Unique identifier for each sighting. (Integer) | | text | Full text of the report. (String) | | stats | Date/time location etc. (String) ...

paper link:

http://arxiv.org/pdf/2406.09838v1

  For downloading the image, please go to the following url link:

https://drive.google.com/file/d/18nRGlpZpYuUNVWsQ8v26Hvnqs-MHMcB8/view?usp=share_link

GPS-Lab/DeKeyNLU dataset hosted on Hugging Face and contributed by the HF Datasets community

Velocity-derived data in elite women soccer player during official games. Position are playing positions (MO: Offensive Midfielder, A: Attackers, DC: Central Defenders, DL: Lateral Defenders, MD: Medial Defenders). Columns are xxx_YYY with xxx is the outcome and YYY is the game segment. xxx: Mean is mean velocity. CV is coefficient of variation, Max is maximum velocity, ApEn is approximate entropy, SampEnn is Sample entropy, H2 is the Hurst exponent with q = 2, Frac_max and Frac_peak are maximum and peak values from the multifractal (MFDFA) analysis. YYY: Game is the entire game, MT1 and MT2 are the first and second half-times, Q1, Q2, Q3 and Q4 are the four quarters of the game. MFDFA is the multifractal detrended fluctuation analysis. Analyses are conducted using R.

Categories

Global Positioning System, Professional Sport, Velocity, Soccer, Complex Analysis

Acknowledgements & Source

Nicolas Babault

Data Source: Mendeley Dataset

Context

Technology has many effects on the transportation industry.

Content

We have provided an accurate dataset describing a complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto, in Portugal (i.e. one CSV file named "train.csv"). These taxis operate through a taxi dispatch central, using mobile data terminals installed in the vehicles. We categorize each ride into three categories: A) taxi central based, B) stand-based or C) non-taxi central based. For the first, we provide an anonymized id, when such information is available from the telephone call. The last two categories refer to services that were demanded directly to the taxi drivers on a B) taxi stand or on a C) random street.

Each data sample corresponds to one completed trip. It contains a total of 9 (nine) features, described as follows:

• TRIP_ID: (String) It contains an unique identifier for each trip;

• CALL_TYPE: (char) It identifies the way used to demand this service. It may contain one of three possible values: ‘A’ if this trip was dispatched from the central; ‘B’ if this trip was demanded directly to a taxi driver on a specific stand; ‘C’ otherwise (i.e. a trip demanded on a random street).

• ORIGIN_CALL: (integer) It contains an unique identifier for each phone number which was used to demand, at least, one service. It identifies the trip’s customer if CALL_TYPE=’A’. Otherwise, it assumes a NULL value;

• ORIGIN_STAND: (integer): It contains an unique identifier for the taxi stand. It identifies the starting point of the trip if CALL_TYPE=’B’. Otherwise, it assumes a NULL value;

• TAXI_ID: (integer): It contains an unique identifier for the taxi driver that performed each trip;

• TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip’s start;

• DAYTYPE: (char) It identifies the daytype of the trip’s start. It assumes one of three possible values: ‘B’ if this trip started on a holiday or any other special day (i.e. extending holidays, floating holidays, etc.); ‘C’ if the trip started on a day before a type-B day; ‘A’ otherwise (i.e. a normal day, workday or weekend).

• MISSING_DATA: (Boolean) It is FALSE when the GPS data stream is complete and TRUE whenever one (or more) locations are missing

• POLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip’s destination while the first one represents its start;

The total travel time of the trip (the prediction target of this competition) is defined as the (number of points-1) x 15 seconds. For example, a trip with 101 data points in POLYLINE has a length of (101-1) * 15 = 1500 seconds. Some trips have missing data points in POLYLINE, indicated by MISSING_DATA column, and it is part of the challenge how you utilize this knowledge.

Acknowledgements

Data from ECML/PKDD 15: Taxi Trip Time Prediction (II) Competition

Inspiration

Added this dataset because competition datasets do not appear in the dataset search and this dataset could help learn basic methods in the area of geo-spatial analysis and trajectory handling