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 All previous geographies are included, even if they are no longer in effect.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 General Transit Feed Specification (GTFS) for the city of Rio de Janeiro, Brazil, also known as GTFS static or static transit to differentiate it from the GTFS realtime extension, defines a common format for public transportation schedules and associated geographic information.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.

GTFS Realtime feeds have been provided by the Victoria Department of Transport and Planning

to provide realtime updates about Public Transport services. It contains feeds about:

• Trip Updates \- delays, cancellations, changed routes
• Service Alerts \- stop moved, unforeseen events affecting a station, route or the entire network
• Vehicle Positions \- information about the vehicles including location and congestion level

Please note these feeds are provided in the Protocol Buffer format and are not human readable.

For more information refer to this GTFS Realtime page (https://gtfs.org/realtime/) which is maintained by MobilityData (https://mobilitydata.org/) and facilitates the GTFS and GTFS\-R specification.

API Key \- To obtain an API Key please continue to signup using our old Data Exchange Platform (TEMPORARY)

Mobility data is collected through location-aware mobile apps using an SDK-based implementation. Users explicitly consent to share their location data via a clear opt-in process and are provided with clear opt-out options. Factori ingests, cleans, validates, and exports all location data signals to ensure the highest quality data is available for analysis.

• Record Count: 90 Billion
• Capturing Frequency: Once per Event
• Delivering Frequency: Once per Day
• Updated: Daily

Mobility Data Reach

Our data reach encompasses the total counts available across various categories, including attributes such as country location, MAU (Monthly Active Users), DAU (Daily Active Users), and Monthly Location Pings.

Data Export Methodology

We collect data dynamically, offering the most updated data and insights at the best-suited intervals (daily, weekly, monthly, or quarterly).

Business Needs

Our data supports various business needs, including consumer insight, market intelligence, advertising, and retail analytics.

we extract human trips from Call Records Detail data. Combining traffic analysis zone dataset

This dataset contains General Transit Feed Specification (GTFS) data for the City of Duitama, Colombia. Generated by a group of students from the Salesian School of Duitama within the Digital Transport for Africa initiative (Digital Transport for Africa), this data is meant to support the transit sector and digital application development. It encompasses information on transit routes, schedules, and stop locations.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.

This dataset contains General Transit Feed Specification (GTFS) data for the City of Santiago, Chile. This data is meant to support the transit sector and digital application development. It encompasses information on transit routes, schedules, and stop locations.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.

Motivation

This repository includes:
1) Data scraper of Google, Apple and Waze Mobility data
2) Preprocessed mobility reports in different formats
3) Merged mobility reports in summary files

License

See LICENSE.txt

About data

About Google COVID-19 Community Mobility Reports

About Apple COVID-19 Mobility Trends Reports

About Waze COVID-19 local driving trends

Credit

If you use this dataset, please cite original data sources:

1. Google LLC "Google COVID-19 Community Mobility Reports". https://www.google.com/covid19/mobility/ Accessed:

2. Apple Inc. "Apple COVID-19 Mobility Trends Reports". https://www.apple.com/covid19/mobility Accessed:

3. Waze Ltd "Waze COVID-19 Impact Dashboard". https://www.waze.com/covid19 Accessed:

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.

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.

This data set is supplement to this Scientific Reports article.

The data set provides estimates of country-level daily mobility metrics (uncertainty included) for 17 countries from March 11, 2020 to present. Estimates are based on more than 3.8 million smartphone trajectories.

Metrics:

Estimated daily average travelled distance by people.

Estimated percentage of people who did not move during the 24 hours of the day.

Countries: Argentina (ARG), Chile (CHL), Colombia (COL), Costa Rica (CRI), Ecuador (ECU), Greece (GRC), Guatemala (GTM), Italy (ITA), Mexico (MEX), Nicaragua (NIC), Panama (PAN), Peru (PER), Philippines (PHL), Slovenia (SVN), Turkey (TUR), United States (USA) and Venezuela (VEN).

Covered period: from March 11, 2020 to present.

Temporal resolution: daily.

Temporal smoothing:

No smoothing.

7-day moving average.

14-day moving average.

21-day moving average.

28-day moving average.

Uncertainty: 95% bootstrap confidence interval.

Data ownership

Anonymized data on smartphone trajectories are collected, owned and managed by Futura Innovation SRL. Smartphone trajectories are stored and analyzed on servers owned by Futura Innovation SRL and not shared with third parties, including the author of this repository and his organization (University of Bergamo).

Contribution

Ilaria Cremonesi of Futura Innovation SRL is the data owner and data manager.

Francesco Finazzi of University of Bergamo developed the statistical methodology for the data analysis and the algorithms implemented on Futura Innovation SRL servers.

Repository update

CSV files of this repository are regularly produced by Futura Innovation SRL and published by the repository's author after validation.

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.

Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This sugges...

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 contains General Transit Feed Specification (GTFS) data for the City of Cordoba, Argentina. This data is meant to support the transit sector and digital application development. It encompasses information on transit routes, schedules, and stop locations.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.

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 contains on-demand mobility data on the city of Lomé (Togo). The city space is divided into meshes of 300m x 300m, each mesh can serve as origin or destination of a passenger mobility request. Geographical data are obtained from openstreetmap.org using osmnx python package and epsg:25231 projection.The passenger on-demand requests are plausibly generated from aggregated data obtained from the Lomé city urban bus company SOTRAL (https//www.sotraltogo.com), using a model published: here https://doi.org/10.1109/ITSC57777.2023.10422650.The geographical data are obtained from openstreetmap.org using osmnx python package (https://github.com/gboeing/osmnx) and epsg:25231 projection.

This dataset contains General Transit Feed Specification (GTFS) data for the City of Alexandria, Egypt. Generated by volunteers from the Digital Transport for Africa initiative (Digital Transport for Africa), this data is meant to support the transit sector and digital application development. It encompasses information on transit routes, schedules, and stop locations.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.

This file contains statistical measures of human individual movement trajectories calculated and analysed during the "The impact of human mobility data scales and processing on movement predictability" study submitted to Scientific Reports.

This dataset contains General Transit Feed Specification (GTFS) data for the City of Bamako, Mali. Generated by volunteers from the Digital Transport for Africa initiative (Digital Transport for Africa), this data is meant to support the transit sector and digital application development. It encompasses information on transit routes, schedules, and stop locations.

The dataset has been validated through the Canonical GTFS Schedule Validator (GTFS Schedule Validator). However, users should note that the file may still contain errors or become outdated over time.

To analyze and view the dataset, it is recommended to use the TUMI GTFS Analyzer. This tool facilitates a deeper understanding of the data through several analysis features.

This dataset was scouted as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing.