This dataset lists the public and internals datasets published on the City of Austin Open Data Portal filtered to the Austin Transportation and Public Works department. Dataset types include stories, charts, datasets, filters, embedded links, and files. This dataset is maintained by the Data and Technology Services division in the department.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

This dataset is comprised of issues received by the City of Austin's Mobility Management Center, which is operated by the Austin Transportation & Public Works Department's Arterial Management Division.

These records are comprised of both resident-reported issues via 311 as well as issues reported directly to the Mobility Management Center by staff or regional agencies.

This dataset is related to the Mobility Management Center Activities dataset, which captures specific actions taken in response to these issues.

See:

Activities dataset: https://data.austintexas.gov/dataset/-UNDER-CONSTRUCTION-Mobility-Management-Center-Act/p7pt-re4k

Arterial management division homepage: https://www.austintexas.gov/department/arterial-management

This de-duped Geospatial Mobility dataset is derived from first-party, consented, mobile app data. This data is de-identified prior to Intuizi processing it, and is the highest level, least aggregated dataset that we are able to provide to our customers.

Intuizi customers use this data for many purposes, primarily to understand - at as granular a level as possible - the mobility patterns of de-identified mobile devices in specific countries.

This is incredibly useful for understanding visitation patterns to specific locations in particular territories or regions.

Some of our customers may, in addition, have their own first-party dataset that they want to compare/contrast to a high-level set of de-identified data, thus enriching their existing dataset. They may want to compare visitation to (their own, or other specific) locations to those owned/operated by competitors; or understand where else the devices that show up in their owned/operated locations also happen to go. Please note: re-identification of an individual is contractually prohibited.

When processed against PoI data, it is used to generate our Visualisation Details Dataset, which is then used to create visualisations within our visualisation platform. It can also be further refined, for use as our Postal Origin or Country of Origin Dataset.

The Intuizi De-identified Signals Dataset comprises fully-consented mobile device data, de-identified at source by the entity which has legal consent to own/process such data, and on who’s behalf we work to create a de-identified dataset of Encrypted ID visitation/mobility data.

A. SUMMARY The dataset contains data by SFMTA for use according to the Mobility Data Specification (MDS) Geography API. The Geography API allows regulatory agencies to define geographies that may be used by providers of mobility services and may be referenced in other portions of a MDS implementation. More information about MDS can be found here. The SFMTA uses MDS data to enforce regulations and conduct planning analyses related to its shared micromobility permit programs. The Geography API currently contains the key neighborhoods used by the SFMTA’s Powered Scooter Share Permit Program to help ensure that permitted operators offer an equitable and convenient distribution of services across San Francisco. Additional geographies may be added for analytical and/or other regulatory needs. B. HOW THE DATASET IS CREATED Staff create the geographies according to a regulatory and/or analytical need according to the MDS Geography API specification. C. UPDATE PROCESS Updates are made as needed. All previous geographies are included, even if they are no longer in effect. D. HOW TO USE THIS DATASET The data by itself may be of limited value, as the MDS Geography API is intended to be used in conjunction with other MDS APIs and regulatory functions. E. RELATED DATASETS SFMTA also makes the geographies data available via an API at services.sfmta.com/mobility/2_0/geographies. Dashboards containing aggregated MDS data as well as other datasets related to SFMTA's shared micromobility programs can be found here.

The dataset features SafeGraph data that measures foot-traffic mobility changes around Open Streets in New York City during Covid-19. In addition to the raw counts of visitors to each POI during the week. It contains weekly pattern data collected between May 2nd, 2020, to July 28th , 2021. The point-level POI data is aggregated to census block group neighborhood-level data to maintain a standard level of resolution for all data used for this study. The Open Streets have been manually geocoded in Google Earth and imported the KMZ data as a shapefile into ArcGIS. Once in ArcGIS, the locations of the Open Streets were matched to CBGs, which either bound or intersect with the Open Streets. Since the Open Streets vary in opening dates, we consider the week that a street first opens as an Open Street as Week 0 for each street. For each observation, we consider the time series data three weeks before the week of opening date (Week 0) and six weeks after as our observation period. To create a control sample, we draw a 1 mile buffer area around each Open Street in ArcGIS to minimize spillover effects, and randomly select a CBG that sits outside this buffer area and pair it with each observation. The buffer takes into account the spatial effects an Open Street is likely to have on surrounding neighborhoods, such that a neighborhood that is within a 15-20 minute walk of an Open Street may see increase in walking behaviors after the introduction of the Open Streets Program, even if the Open Street is not located directly within the CBG.

The YJMob100K human mobility datasets (YJMob100K_dataset1.csv.gz and YJMob100K_dataset1.csv.gz) contain the movement of a total of 100,000 individuals across a 75 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of 80,000 individuals across a 75-day business-as-usual period, while the second dataset contains the movement of 20,000 individuals across a 75-day period (including the last 15 days during an emergency) with unusual behavior.

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (cell_POIcat.csv.gz). The list of 85 POI categories can be found in POI_datacategories.csv.

For details of the dataset, see Data Descriptor:

Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. https://www.nature.com/articles/s41597-024-03237-9

--- Details about the Human Mobility Prediction Challenge 2023 (ended November 13, 2023) ---

The challenge takes place in a mid-sized and highly populated metropolitan area, somewhere in Japan. The area is divided into 500 meters x 500 meters grid cells, resulting in a 200 x 200 grid cell space.

The human mobility datasets (task1_dataset.csv.gz and task2_dataset.csv.gz) contain the movement of a total of 100,000 individuals across a 90 day period, discretized into 30-minute intervals and 500 meter grid cells. The first dataset contains the movement of a 75 day business-as-usual period, while the second dataset contains the movement of a 75 day period during an emergency with unusual behavior.

There are 2 tasks in the Human Mobility Prediction Challenge.

In task 1, participants are provided with the full time series data (75 days) for 80,000 individuals, and partial (only 60 days) time series movement data for the remaining 20,000 individuals (task1_dataset.csv.gz). Given the provided data, Task 1 of the challenge is to predict the movement patterns of the individuals in the 20,000 individuals during days 60-74. Task 2 is similar task but uses a smaller dataset of 25,000 individuals in total, 2,500 of which have the locations during days 60-74 masked and need to be predicted (task2_dataset.csv.gz).

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) as supplementary information (which is optional for use in the challenge) (cell_POIcat.csv.gz).

For more details, see https://connection.mit.edu/humob-challenge-2023

Based on citywide data sources for pedestrian generators, NYC DOT developed a holistic, data-driven framework to categorize streets based on pedestrian needs. The plan aims to improve pedestrian comfort and convenience as well as increase walking citywide. NYC DOT created five broad street categories to determine the pedestrian needs on the city’s sidewalks. For more information, please visit NYC DOT website: https://www1.nyc.gov/html/dot/html/pedestrians/pedestrian-mobility.shtml

WorldMove is an open-access worldwide human mobility dataset, we follow a generative AI-based approach to create a large-scale mobility dataset for cities worldwide. Our method leverages publicly available multi-source data, including population distribution, points of interest (POIs), and synthetic commuting origin-destination flow datasets, to generate realistic city-scale mobility trajectories.

The https://data.mobility.brussels portal contains a set of open data related to mobility in the Brussels-Capital Region. A cartographic interface is available to view the data directly. Information can also be found on the mobility portal.

Licensing on this dataset refers to GitHub content.

See Descartes Labs' Terms of Service on their Web site for additional information. https://www.descarteslabs.com/terms-of-service/

This dataset is used for the analysis in the publication entitled "Using data derived from cellular device locations to estimate visitation to natural areas: an application to the U.S. National Park system". It includes cell data purchased from Airsage Inc. at the monthly resolution for years 2018 and 2019 for 38 park units in the U.S. National Park system, corresponding monthly visitation obtained from the NPS Stats (https://irma.nps.gov/STATS/), and park attributes that are considered to affect the relationships between Cell and NPS data in the analysis.

Introduction

This dataset provides a comprehensive assessment of public transport connectivity across Germany by analyzing both walking distances to the nearest public transport stops as well as the quality of public transport connections for daily usage scenarios with housing-level-granularity on a country-wide scale. The data was generated through a novel approach that integrates multiple open data sources, simulation models, and visual analytics techniques, enabling researchers, policymakers, and urban planners to identify gaps and opportunities for transit network improvements. ewline

Why does it matter?

Efficient and accessible public transportation is a critical component of sustainable urban development. However, many transit networks struggle to adequately serve diverse populations due to infrastructural, financial, and urban planning limitations. Traditional transit planning often relies on aggregated statistics, expert opinions, or limited surveys, making it difficult to assess transport accessibility at an individual household level. This dataset provides a data-driven and reproducible methodology for unbiased country-wide comparisons.

Find more information at https://mobility.dbvis.de.

Key Facts, Download, Citation

Title OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles

Acronym OPTIMAP

Download https://mobility.dbvis.de/data-results/OPTIMAP_v2025-02-01.parquet (478MB, parquet)

License Datenlizenz Deutschland - Namensnennung - Version 2.0 (dl-de-by/2.0)

Please cite the dataset as:Maximilian T. Fischer, Daniel Fürst, Yannick Metz, Manuel Schmidt, Julius Rauscher, and Daniel A. Keim. OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles. Zenodo, 2025. doi: 10.5281/zenodo.14772646.

or, when using Bibtex

@dataset{MobilityProfiles.DatasetGermany.2025, author = {Fischer, Maximilian T. and Fürst, Daniel and Metz, Yannick and Schmidt, Manuel and Rauscher, Julius and Keim, Daniel A.}, title = {OPTIMAP: A Dataset for Open Public Transport Infrastructure and Mobility Accessibility Profiles}, year = 2025, publisher = {Zenodo}, doi = {10.5281/zenodo.14772646}}

Dataset Description

The dataset in the PARQUET format includes detailed accessibility measures for public transport at a fine-grained, housing-level resolution. It consists of four columns:

lat, lng (float32): GPS coordinates (EPSG:4326) of each house in Germany, expensively compiled from the house coordinates (HK-DE) data provided by the 16 federal states under the EU INSPIRE regulations.

MinDistanceWalking (int32): An approximate walking distance (in meters) to the nearest public transport stop from each registered building in Germany.

scores_OVERALL (float32): A simulated, demographic- and scenario-weighted measure of public transport quality for daily usage, considering travel times, frequency, and coverage across various daily scenarios (e.g., commuting, shopping, medical visits). The results are represented in an artificial time unit to allow comparative analysis across locations.

Methodology

The dataset was generated using a combination of open geospatial data and advanced transport simulation techniques.

Data Sources: Public transit information from the German national access point (DELFI NeTEx), housing geolocation data from various state authorities, and routing information from OpenStreetMap.

Walking Distance Calculation: The shortest path to the nearest transit stop was computed using the Dijkstra algorithm on a graph network of publicly available pathways sourced from OSM, considering the ten aerial-nearest public transport stops.

Public Transport Quality Estimation: The dataset incorporates a scenario-based simulation model, analyzing weight-averaged travel times and connection frequency to typical daily POIs such as the individually nearest train stations, kindergartens, schools, institutions of higher education, fitness centers, cinemas, places of worship, supermarkets, shopping malls, restaurants, doctors, parks, and cultural institutions. It includes walking distances to the start and from the destination public transport stops as well as the averaged travel and waiting times on the shortest route calculated via a modified Dijkstra algorithm. The results are aggregated using a demographically- and scenario-weighted metric to ensure comparability. The value is in the unit of time, although it should not be interpreted directly as real minutes.

Visualization and Validation: A WebGL-based interactive tool and static precomputed maps were developed to allow users to interactively explore transport accessibility metrics dynamically, available at https://mobility.dbvis.de.

Potential Applications

The dataset enables multiple use cases across research, policy, and urban planning:

Public Accessibility Studies: Provides insights into transport equity by evaluating mobility gaps affecting different demographic groups, different regional areas, and comparing county and state efforts in improving public transport quality.

Urban Planning and Transport Policy: Supports data-driven decision-making for optimizing transit networks, adjusting service schedules, or identifying underserved areas.

Smart City Development: Assists in integrating mobility analytics into broader smart city initiatives for efficient resource allocation and sustainability planning.

Academic Research: Facilitates studies in transportation engineering, urban geography, and mobility behavior analysis.

Conclusion

By offering high-resolution public transport accessibility data at housing-level granularity, this dataset contributes to a more transparent and objective understanding of urban mobility challenges. The integration of simulation models, demographic considerations, and scalable analytics provides a novel approach to evaluating and improving public transit systems. Researchers, city officials, and policymakers are encouraged to leverage this dataset to enhance transport infrastructure planning and accessibility.

This dataset contains both the approximate walking distances in meters and a weighted overall quality score in an artificial time unit for each individual house in Germany. More advanced versions are currently not publicly available. This base dataset is publicly available and adheres to open data licensing principles, enabling its reuse for scientific and policy-oriented studies.

Source Data Licenses

While not part of this dataset, the scientific simulation used to create the results leverages public transit information via the National Access Point (NAP) DELFI as NeTEx, provided via GTFS feeds of Germany (CC BY 4.0).

Also, routing information used during the processing was based on Open Street Map contributors (CC BY 4.0).

Primarily, this dataset contains original and slightly processed housing locations (lat, lng) that were made available as part of the EU INSPIRE regulations, based on Directive (EU) 2019/1024 (of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast)).

In Germany, the respective data is provided individually by the 16 federal states, with the following required attributions and license indications:

BB: EU INSPIRE / © GeoBasis-DE/LGB, dl-de-by/2.0 (data modified)

BE: EU INSPIRE / © Geoportal Berlin / Hauskoordinaten, dl-de-by/2.0 (data modified)

BW: EU INSPIRE / © LGL, www.lgl-bw.de, dl-de-by/2.0 (data modified)

BY: EU INSPIRE / © Bayerische Vermessungsverwaltung, CC BY 4.0 (data modified)

HB: EU INSPIRE / © Landesamt GeoInformation Bremen, CC BY 4.0 (data modified)

HE: EU INSPIRE / © HVBG, dl-de-by-zero/2.0 (data modified)

HH: EU INSPIRE / © FHH (LGV), dl-de-by/2.0 (data modified)

MV: EU INSPIRE / © LAiV M-V, CC BY 4.0 (data modified)

NI: EU INSPIRE / © LGLN 2024, CC BY 4.0 (data modified)

NW: EU INSPIRE / © Geobasis NRW, dl-de-by-zero/2.0 (data modified)

RP: EU INSPIRE / © GeoBasis-DE / LVermGeoRP 2024, dl-de-by/2.0 (data modified)

SH: EU INSPIRE / © GeoBasis-DE/LVermGeo SH, CC BY 4.0 (data modified)

SL: EU INSPIRE / © GeoBasis DE/LVGL-SL (2024), dl-de-by/2.0 (data modified)

SN: EU INSPIRE / © GeoSN, dl-de-by/2.0 (data modified)

ST: EU INSPIRE / © GeoBasis-DE / LVermGeo LSA, dl-de-by/2.0 (data modified)

TH: EU INSPIRE / © GDI-Th, dl-de-by/2.0 (data modified)

Original Research

The methodology and techniques are described in an original research article published in 2024. When referring to our approach, please cite the following publication:Yannick Metz, Dennis Ackermann, Daniel A. Keim, and Maximilian T. Fischer. Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent. In: 2024 IEEE Visualization in Data Science (VDS). VDS. IEEE, 2024, p. 9. doi: 10.1109/VDS63897.2024.00006

or, when using bibtex:

@inproceedings{MobilityProfiles.VDS.2024, author = {Metz, Yannick and Ackermann, Dennis and Keim, Daniel A. and Fischer, Maximilian T.}, title = {Interactive Public Transport Infrastructure Analysis through Mobility Profiles: Making the Mobility Transition Transparent}, booktitle = {2024 IEEE Visualization in Data Science (VDS)}, doi = {10.1109/VDS63897.2024.00006}, pages = {9}, publisher = {IEEE}, series = {VDS}, year = {2024}}

This dataset measures the mobility trend in different dimensions (location categories) for Brazil, Federation Units and Municipalities.

This portal, from the Natal Urban Mobility Secretariat (STTU), aims to provide the public with access to the city's urban mobility data, whether through open data files or public software services. According to the Open Knowledge Foundation (OKF), "data is open when anyone can freely use, reuse, and redistribute it, subject only, at most, to the requirement to attribute authorship and share under the same license." In the context of the Brazilian government, Article 8 of Law 12.527/2011 (Access to Information Law – LAI) establishes that information of collective or general interest must be compulsorily disclosed by public bodies and entities on their official websites, which must meet, among other things, the following requirements: In line with the Access to Information Law and "Open Data" initiatives, the STTU launches its Natal Urban Mobility Transparency Portal, providing information and data channels through the areas of Static Data, Dynamic Data - Developer Area, and Public Software Services. Translated from Portuguese Original Text: Esse portal, da Secretaria da Mobilidade Urbana de Natal (STTU), tem por objetivo fornecer à população acesso aos dados da mobilidade urbana da cidade, seja através de arquivos de dados abertos, seja através de serviços públicos de software. Segundo a Fundação do Conhecimento Aberto (Open Knowledge Foundation – OKF), “dados são abertos quando qualquer pessoa pode livremente usá-los, reutilizá-los e redistribuí-los, estando sujeito a, no máximo, a exigência de creditar a sua autoria e compartilhar pela mesma licença”. No contexto do governo brasileiro, o art. 8º da Lei 12.527/2011 (Lei de Acesso à Informação – LAI) estabelece que as informações de interesse coletivo ou geral devem ser obrigatoriamente divulgadas pelos órgãos e entidades públicos em seus sítios oficiais, os quais devem atender, entre outros, aos seguintes requisitos: Em sintonia com a Lei de Acesso a Informação e com as iniciativas de "Open Data", a STTU lança seu Portal da Transparência da Mobilidade Urbana de Natal, disponibilizando canais de informação e de dados, através das áreas de Dados Estáticos, Dados Dinâmicos - Área do Desenvolvedor e Serviços Públicos de Software.

The most vulnerable group of traffic participants are pedestrians using mobility aids. While there has been significant progress in the robustness and reliability of camera based general pedestrian detection systems, pedestrians reliant on mobility aids are highly underrepresented in common datasets for object detection and classification.

To bridge this gap and enable research towards robust and reliable detection systems which may be employed in traffic monitoring, scheduling, and planning, we present this dataset of a pedestrian crossing scenario taken from an elevated traffic monitoring perspective together with ground truth annotations (Yolo format [1]). Classes present in the dataset are pedestrian (without mobility aids), as well as pedestrians using wheelchairs, rollators/wheeled walkers, crutches, and walking canes. The dataset comes with official training, validation, and test splits.

An in-depth description of the dataset can be found in [2]. If you make use of this dataset in your work, research or publication, please cite this work as:

@inproceedings{mohr2023mau,
author = {Mohr, Ludwig and Kirillova, Nadezda and Possegger, Horst and Bischof, Horst},
title = {{A Comprehensive Crossroad Camera Dataset of Mobility Aid Users}},
booktitle = {Proceedings of the 34th British Machine Vision Conference ({BMVC}2023)},
year = {2023}
}

Archive mobility.zip contains the full detection dataset in Yolo format with images, ground truth labels and meta data, archive mobility_class_hierarchy.zip contains labels and meta files (Yolo format) for training with class hierarchy using e.g. the modified version of Yolo v5/v8 available under [3].
To use this dataset with Yolo, you will need to download and extract the zip archive and change the path entry in dataset.yaml to the directory where you extracted the archive to.

[1] https://github.com/ultralytics/ultralytics
[2] coming soon
[3] coming soon

Changes in the volume of visits to six different location types compared with a pre-coronavirus (COVID-19) baseline, using Google Mobility data.

The datasets are split by census block, cities, counties, districts, provinces, and states. The typical dataset includes the below fields.

Column numbers, Data attribute, Description 1, device_id, hashed anonymized unique id per moving device 2, origin_geoid, geohash id of the origin grid cell 3, destination_geoid, geohash id of the destination grid cell 4, origin_lat, origin latitude with 4-to-5 decimal precision 5, origin_long, origin longitude with 4-to-5 decimal precision 6, destination_lat, destination latitude with 5-to-6 decimal precision 7, destination_lon, destination longitude with 5-to-6 decimal precision 8, start_timestamp, start timestamp / local time 9, end_timestamp, end timestamp / local time 10, origin_shape_zone, customer provided origin shape id, zone or census block id 11, destination_shape_zone, customer provided destination shape id, zone or census block id 12, trip_distance, inferred distance traveled in meters, as the crow flies 13, trip_duration, inferred duration of the trip in seconds 14, trip_speed, inferred speed of the trip in meters per second 15, hour_of_day, hour of day of trip start (0-23) 16, time_period, time period of trip start (morning, afternoon, evening, night) 17, day_of_week, day of week of trip start(mon, tue, wed, thu, fri, sat, sun) 18, year, year of trip start 19, iso_week, iso week of the trip 20, iso_week_start_date, start date of the iso week 21, iso_week_end_date, end date of the iso week 22, travel_mode, mode of travel (walking, driving, bicycling, etc) 23, trip_event, trip or segment events (start, route, end, start-end) 24, trip_id, trip identifier (unique for each batch of results) 25, origin_city_block_id, census block id for the trip origin point 26, destination_city_block_id, census block id for the trip destination point 27, origin_city_block_name, census block name for the trip origin point 28, destination_city_block_name, census block name for the trip destination point 29, trip_scaled_ratio, ratio used to scale up each trip, for example, a trip_scaled_ratio value of 10 means that 1 original trip was scaled up to 10 trips 30, route_geojson, geojson line representing trip route trajectory or geometry

The datasets can be processed and enhanced to also include places, POI visitation patterns, hour-of-day patterns, weekday patterns, weekend patterns, dwell time inferences, and macro movement trends.

The dataset is delivered as gzipped CSV archive files that are uploaded to your AWS s3 bucket upon request.

Our location data powers the most advanced address validation solutions for enterprise backend and frontend systems.

A global, standardized, self-hosted location dataset containing all administrative divisions, cities, and zip codes for 247 countries.

All geospatial data for address data validation is updated weekly to maintain the highest data quality, including challenging countries such as China, Brazil, Russia, and the United Kingdom.

Use cases for the Address Validation at Zip Code Level Database (Geospatial data)

• Address capture and address validation

• Address autocomplete

• Address verification

• Reporting and Business Intelligence (BI)

• Master Data Mangement

• Logistics and Supply Chain Management

• Sales and Marketing

Product Features

• Dedicated features to deliver best-in-class user experience

• Multi-language support including address names in local and foreign languages

• Comprehensive city definitions across countries

Data export methodology

Our location data packages are offered in variable formats, including .csv. All geospatial data for address validation are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why do companies choose our location databases

• Enterprise-grade service

• Full control over security, speed, and latency

• Reduce integration time and cost by 30%

• Weekly updates for the highest quality

• Seamlessly integrated into your software

Note: Custom address validation packages are available. Please submit a request via the above contact button for more details.

This multi-city human mobility dataset contains data from 4 metropolitan areas (cities A, B, C, D), somewhere in Japan. Each city is divided into 500 meters x 500 meters cells, which span a 200 x 200 grid. The human mobility datasets contain the movement of individuals across a 75-day period, discretized into 30-minute intervals and 500-meter grid cells. Each city contains the movement data of 100,000, 25,000, 20,000, and 6,000 individuals, respectively.

While the name or location of the city is not disclosed, the participants are provided with points-of-interest (POIs; e.g., restaurants, parks) data for each grid cell (~85 dimensional vector) for the four cities as supplementary information (e.g., POIdata_cityA). The list of 85 POI categories can be found in POI_datacategories.csv.

This dataset was used for the HuMob Data Challenge 2024 competition. For more details, see https://wp.nyu.edu/humobchallenge2024/

Researchers may use this dataset for publications and reports, as long as: 1) Users shall not carry out activities that involve unethical usage of the data, including attempts at re-identifying data subjects, harming individuals, or damaging companies, and 2) The Data Descriptor paper of an earlier version of the dataset (citation below) needs to be cited when using the data for research and/or commercial purposes. Downloading this dataset implies agreement with the above two conditions.

Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. https://www.nature.com/articles/s41597-024-03237-9