This dataset is used for the classification of traffic intersection regulations using (car) GPS trajectory data. The coverage is mostly a large part of the city of Hannover, Germany.

The overview of the GPS trajectory dataset is given in the below figure: https://data.uni-hannover.de/dataset/379ed322-a9ea-48f3-bc13-2f5ea3174470/resource/ecfbc059-9ca3-4cbc-8774-de091f0fbcd6/download/hannover_traj.png" alt="Overview">

When the trajectory dataset is combined with the related intersection ground-truth information (available at: https://doi.org/10.25835/cqg0x1el): https://data.uni-hannover.de/dataset/379ed322-a9ea-48f3-bc13-2f5ea3174470/resource/bc832922-aedd-4bac-86b3-5d4a753bfecc/download/hannover_rules_traj.png" alt="Combined">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Hannover, Germany. The acquisition period was from December 2017 to March 2019 by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

OSM point features in Europe with tags categorized as POI in the Geofabrik taxonomy. Extracted from original OpenStreetMap database (osm.pbf) in 09.2019 (https://www.openstreetmap.org/#map=7/46.825/8.224)

This dataset contains the ground-truth intersection regulators for all intersections crossed by the open GPS trajectory data of Chicago, published on Mapconstruction.org.

Data Acquisition

The ground-truth data is generated by manually inspecting images at the intersections crossed by the published trajectory dataset on Mapconstruction.org. These images are derived from mapillary.com at the respective intersections. Satellite images are investigated as an additional data source, if needed. This procedure result in labels for each intersection arm.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

This dataset is a subset of a much larger data collection and used for the analysis of speed- and time-profiles of trajectories crossing different selected intersections. Resulting findings can be used for the intersection categorization according to traffic regulation types.

The six selected intersections (A - F) and the crossing trajectory samples (green) can be seen in the subsequent figure: https://data.uni-hannover.de/dataset/1a97c0df-4659-43bd-99e9-d2573d31f5cd/resource/0571e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png" alt="Intersections">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Hannover, Germany. The acquisition period was from December 2017 to March 2019 by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

The data allows to reproduce Figure1, Figure2 and Figure3 of the article "The Scales of Human Mobility". The article is currently under peer review. The aggregated data here presented is derived from anonymized GPS trajectories of ~700,000 individuals worldwide collected by a major telecommunication company. The data processing and analyses are fully described in the article. Raw data are not publicly available to preserve individuals' privacy under the European General Data Protection Regulation.Figure_1_data_size.csv describes the size of individual containers at different hierarchical levels.Figure1_data_time.csv describes the time spent within a container at different hierarchical levels. Figure2_panels_a/b/c/d_source.csv include the Source data necessary to reproduce Figure 2. Figure3.pkl includes the size of the hierarchical levels for selected individuals, their corresponding country of origin, walkability around the home location, urban/rural level, and gender. Data in .pkl format and can be opened in Python (see here: https://docs.python.org/2/library/pickle.html)

This dataset is used for the classification of traffic intersections regulation rules using car GPS trajectories. In order to apply supervised classification methods, the ground-truth information is also contained in this dataset. The regulators are annotated based on the intersection arms at each intersection.

An overview of the dataset's trajectories can be seen in the subsequent figure: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c5fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png" alt="Overview of the dataset containing the trajectories collected in Edessa.">

An overview of the related intersection regulator ground-truth rules can be seen in the following figure: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c8d75ea3-83ed-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png" alt="">

Data Acquisition

The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Edessa, Greece. The acquisition period was from March 2018 to September 2018 (6 month) by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second. Additionally to the GPS trajectories, the ground-truth regulator rules of the traffic intersections were annotated via street view images.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

Title: Attributing pedestrian networks with semantic information based on multi-source spatial dataAbstract: The lack of associating pedestrian networks, i.e., the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( https://doi.org/10.5281/zenodo.6436380) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).

The format of the dataset is similar to that of Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km2, and “Date” in days - representing the days since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380

Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans, 127(8), e2022JC018737. https://doi.org/10.1029/2022JC018737

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

Description

This dataset contains fingerprint information of WLAN access points and BLE beacons with a known position and IMU sensor data. Data was collected on the floor of the Web and Information Systems Engineering (WISE) Lab at the VUB (Pleinlaan 9, 3rd floor) with 110 training reference points and 30 test data points. Each reference point was recorded for 20 seconds in four different orientations.

More information is given in the README.pdf file included in the dataset.

File Contents

/wlan_aps.csv: Wireless access point information

/ble_beacons.csv: BLE beacon positions

/misc: Miscelanieous resources

/misc/floorplan.png: PNG version of the floorplan

/misc/floorplan_medium.png: PNG version of the floorplan (medium quality)

/misc/datapoints.svg: Training and test data points visualisation

/misc/datapoints.csv: Training data points CSV

/misc/testdatapoints.csv: Test data points CSV

/misc/spaces.geo.json: GeoJSON feature collection of symbolic spaces

/misc/documentation.css: README documentation CSS (unrelated to the dataset)

/train: Training data points (110 in 4 orientations)

/train/raw: Raw unprocessed data points (not aggregated)

/train/raw/wlan_fingerprints.csv: Raw WLAN fingerprints

/train/raw/imu_fingerprints.csv: Raw IMU data collection

/train/raw/ble_fingerprints.csv: Raw BLE fingerprints

/train/aggregated: Processed aggregated data points

/train/aggregated/wlan_fingerprints.csv: WLAN fingerprints

/train/aggregated/ble_fingerprints.csv: BLE fingerprints

/train/aggregated/imu_fingerprints.csv: IMU data collection

/train/aggregated/wlan-ble_fingerprints.csv: WLAN and BLE fingerprints merged

/test: Test data points (30 in 4 orientations)

/test/raw: Raw unprocessed test data points (not aggregated)

/test/raw/wlan_fingerprints.csv: Raw WLAN fingerprints

/test/raw/imu_fingerprints.csv: Raw IMU data collection

/test/raw/ble_fingerprints.csv: Raw BLE fingerprints

/test/aggregated: Processed aggregated data points

/test/aggregated/wlan_fingerprints.csv: WLAN fingerprints

/test/aggregated/ble_fingerprints.csv: BLE fingerprints

/test/aggregated/imu_fingerprints.csv: IMU data collection

/test/aggregated/wlan-ble_fingerprints.csv: WLAN and BLE fingerprints merged

/trajectories: Test trajectories (10)

/trajectories/???: Trajectory directory, ??? is the name of the trajectory

/trajectories/???/???_ble.csv: BLE advertisements received during the trajectory

/trajectories/???/???_imu.csv: IMU data from the trajectory

/trajectories/???/???_wlan.csv: WLAN signals received during the trajectory

This dataset contains weekly trajectory information of Gulf Stream Warm Core Rings from 2011-2020. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 282 WCRs present between January 1, 2011 and December 31, 2020. Each Warm Core Ring and is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2017 having a trailing alphabet of 'E' indicates that four rings were carried over from 2016 which were still observed on January 1, 2017. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km2, and “Date” in days - representing the week since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2011-2020. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

An accurate dataset describing trajectories performed by all the 442 taxis running in the city of Porto, in Portugal.

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

A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks

The dataset includes pedestrian trajectory data within a two-story indoor environment, as well as point cloud data of the indoor structure.

Sampling time as known information can certainly enhance the reliability of density estimation for space-time trajectories. Established kernel density estimation (KDE) methods utilize the sampling time to reshape the kernel or weights, but it is difficult to control two types of errors at the same time: overestimation or underestimation of the density due to temporal autocorrelation, and assignment of non-zero densities to non-reachable points. For this reason, this paper introduces a divide-and-conquer strategy for movement data with irregular sampling intervals, and proposes a time-KDE method under the dual drive of time geography and temporal autocorrelation. It establishes two mappings between time to kernels and to weights respectively in order to reconcile the above two types of errors by recalibrating the KDE, with the aim of maximizing the temporal information to abate the uncertainty of the density estimation, and thus providing a theoretical basis for unbiased density estimation.

In early 2000 and again in 2001, RAFOS floats were deployed in the western tropical Atlantic near latitude 16N. These floats drift at pre-determined depths for durations of typically one year or more, and they are geo-located by triangulation using sound signals from moored underwater sound sources. The float trajectories then show the currents of the water they drift in. The first batch of floats drifted in the layer of the Antarctic Intermediate Water near 800m depth, the second in the upper North Atlantic Deep Water near 1400m depth. Here, the trajectory data from the floats (position and lateral velocity) are presented, together with temperature and pressure records. Two versions of the data are included: one that leaves gaps in the trajectories untreated (see "Download data"), and another one that is smoothed with a low-pass filter and has gaps filled or removed (see "Other version"). The floats were part of the Meridional Overturning Variability Experiment (MOVE), a project to investigate the meridional overturning circulation of the Atlantic. MOVE was funded by the German agencies BMBF (awards 03F0246A and 03F0377B) as well as DFG (award SE815/21).

Contains preprocessed single-cell data and metadata for feature selection. Preprocessed adata objects can be accessed using the read_h5ad function in anndata. Also contains the source data files for reproducing the Figures and Supplementary Figures referenced in the manuscript.

• The RPE iterative indirect immunofluorescence imaging dataset (adata_RPE.h5ad) from Ref. (https://doi.org/10.1016/j.cels.2021.10.007) was originally downloaded from the Zenodo repository (https://doi.org/10.5281/zenodo.4525425).
• The PDAC iterative indirect immunofluorescence imaging datasets (listed below) were originally downloaded from the Zenodo repository (https://doi.org/10.5281/zenodo.7860332).
  • adata_PDAC_BxPC3_control.h5ad
  • adata_PDAC_CFPAC_control.h5ad
  • adata_PDAC_HPAC_control.h5ad
  • adata_PDAC_MiaPaCa_control.h5ad
  • adata_PDAC_Pa01C_control.h5ad
  • adata_PDAC_Pa02C_control.h5ad
  • adata_PDAC_Pa16C_control.h5ad
  • adata_PDAC_PANC1_control.h5ad
  • adata_PDAC_UM53_control.h5ad
• The CD8+ T cell differentiation dataset (adata_CD8.h5ad) from Ref. (https://doi.org/10.1126/sciimmunol.aaz6894) was originally downloaded from the Gene Expression Omnibus under the accession code GSE131847 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE131847).
• The definitive endoderm differentiation dataset (adata_DE.h5ad) contains multiplexed single-cell RNA sequencing data profiling the differentiation of human embryonic stem cells into the definitive endoderm.

This dataset contains the ground-truth intersection regulators for a majority of intersections of the city of Hannover, Germany. The ground-truth information is used in order to apply machine learning techniques on (car) GPS trajectory data in order to automatically detect the intersection regulation.

https://data.uni-hannover.de/dataset/1123552a-7946-4924-bbbc-aa7fbc6a800f/resource/0d5185cf-1a67-4374-97c7-397b65dad394/download/hannover_rules_1.png" alt="Rules">

The GPS trajectories related to specifically this dataset are (also) available under: https://doi.org/10.25835/9bidqxvl

Data Acquisition

The ground-truth information are acquired by visiting them on-site and apply manual labeling of each intersection arm individually. Furthermore, satellite images and street-level images were considered but only on a minor degree as on-site labeling is found to be more precise and up-to-date.

Related Publications:

• Zourlidou, S., Sester, M. and Hu, S. (2022): Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1

• Zourlidou, S., Golze, J. and Sester, M. (2022): Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022

• Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022): Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022

• Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020): Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374

• Cheng, H., Zourlidou, S. and Sester, M. (2020): Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652

• Golze, J., Zourlidou, S. and Sester, M. (2020): Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x

• Zourlidou, S., Fischer, C. and Sester, M. (2019): Classification of street junctions according to traffic regulators. In: Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus.

Related Datasets:

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi

• Zourlidou, S., Golze, J. and Sester, M. (2022). Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3

• Zourlidou, S., Golze, J. and Sester, M. (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variable..., The movement visualization files used in the study were generated using the DynamoVis desktop software, available on Github: https://github.com/move-ucsb/DynamoVis Static visualizations were generated as exported screenshots from the software. Dynamic visualizations were generated as exported videos from the software (animated screenshots). Both types of visualizations were created using the built-in export features of DynamoVis. After export, images and videos were edited to add further contextual information, including start and stop icons on the static images, as well as scale bars on all visualizations for contextual information. The survey study design and data collection and analysis methods are described in the associated manuscript. A copy of the survey instrument and an anonymized survey report are included in the data folder. Â Â , Please refer to the README.txt file.

This metadata record pertains to processed dual-frequency geodetic quality Global Positioning System (GPS) data from six sites deployed on the surface of the Totten Glacier between approximately late 2016 and early 2019. The data captures the three-dimensional motion/trajectory of the glacier over a period of approximately 2 years, with a temporal sampling rate of 1 coordinate estimate every 5 minutes. The raw GPS data is covered in a separate metadata record. Site description: • At each site, aluminium towers were erected on the glacier surface – these towers housed a GPS antenna connected to GPS receiver, batteries and solar panel. The approximate height of the GPS antenna was ~3 m above the snow surface at the time of deployment. The GPS receivers to logged data to all GPS satellites in view at a data rate of 1 observation every 15 seconds. • The sites were revisited at the midpoint of the project during the 2017/18 austral summer field season. Given the expect large snow accumulation rate in the region of the deployment, the aluminium towers were extended to elevate the antenna ~3 m above the snow surface at that time. • The GPS equipment used were Trimble NETR9 receivers and Trimble TRM57971.00 antennas. Processing description: • Dual frequency code and carrier phase GPS data from each site was processed using a kinematic Precise Point Positioning (kPPP) approach with the NASA/JPL Gipsy software package (v 6.3). Processing was undertaken by Christopher Watson (University of Tasmania). • The processing approach followed the standard conventions in the geodetic community (IERS2010 conventions for tidal deformation of the solid Earth, VMF1 mapping function for the resolution of tropospheric zenith delay, JPL final orbits and clocks). Site coordinates were computed once every 5 minutes. • Processed site trajectories are provided at a temporal resolution of 1 sample per 5 minutes. Coordinates are relative to the ITRF2014 terrestrial reference frame. • The data archive with file name AAS_4287_GPS_txyz_llh_sigma is considered the first level of processing it contains the most basic output of antenna position for each epoch in time. These files provide Earth-Centred Earth-Fixed (ECEF) cartesian coordinates (XYZ) as well as latitude, longitude and ellipsoidal height coordinates (expressed on the GRS80 ellipsoid). The formal uncertainty from Gipsy v6.3 processing is provided for each epoch in the form of a 3D sigma (expressed in units mm). No outlier detection has been undertaken. The time standard used is UTC, and the temporal sampling is 1 coordinate estimate per 5 minutes. The change in antenna height at the mid-point of the data collection (to raise the tower given snow accumulation) has not been corrected for in any way. • The data archive with file name AAS_4287_GPS_tllh_al_ac_pos_vel is considered the next level of post-processed data as it contains quantities derived from the GPS trajectories and appropriate correction of the antenna height change at the mid-point of the data collection. These files contain the site latitude, longitude and ellipsoidal height (expressed on the GRS80 ellipsoid), the along flow position and velocity, the across flow position and velocity. Outlier detection using a threshold of 25 mm on the 3D sigma (see previous file description) has been undertaken. To resolve the along and across flow coordinate transformation, coordinates were first transformed into a topocentric (north, east) system. The origin of this transformation is provided in the header of each data file. From the local topocentric system, an along and across flow system was derived using piecewise linear fitting of n and e components and temporal knots every 3 months. The temporal knots used in the piecewise linear fitting are provided in the header of each data file. The antenna height correction was estimated using a regression process – offsets and their uncertainty in north, east and up directions are provided in the header of each data file. Again, the time standard used is UTC, and the temporal sampling is 1 coordinate estimate per 5 minutes.

Six GPS sites were deployed in the 2016/17 austral summer season. The sites were revisited in the 2017/18 austral summer season and then retrieved in the 2018/19 austral summer field season. Dates for specific sites are below (yyyy/mm/dd): Site TG01: 2016/11/22 – 2018/12/23 Site TG02: 2016/11/25 – 2019/01/09 Site TG03: 2016/12/03 – 2019/01/28 Site TG04: 2016/12/03 – 2018/12/24 Site TG05: 2016/11/25 – 2019/01/08 Site TG06: 2016/12/03 – 2018/12/24 Note that data has temporal gaps due to a) lack of solar power over winter and b) equipment failure at TG03.