Context San Francisco, known for its iconic Golden Gate Bridge, steep hills, and vibrant neighborhoods, has become a hotspot for travelers seeking unique stays and authentic local experiences. This dataset provides a detailed snapshot of Airbnb activity in San Francisco, California, offering insights into short-term rental trends, pricing dynamics, and guest reviews.

Content The dataset consists of three key files that capture different aspects of Airbnb activity in San Francisco:

Listings Data (listings.csv.gz): Comprehensive details about Airbnb listings, including descriptions, host information, and average review scores. Calendar Data (calendar.csv.gz): Daily availability and pricing for each listing, ideal for analyzing seasonal trends and price fluctuations. Reviews Data (reviews.csv.gz): Detailed guest reviews, including unique reviewer IDs, comments, and timestamps, offering insights into guest experiences and sentiments. Inspiration This dataset offers numerous opportunities for analysis and exploration:

Neighborhood Vibes: Can you identify the unique character of San Francisco’s neighborhoods based on listing descriptions? Seasonality Trends: When is the best time to visit San Francisco, and how do rental prices change throughout the year? Market Dynamics: Is there evidence of growth in Airbnb activity, such as an increase in new listings or guest reviews? Uncover hidden patterns in San Francisco’s Airbnb market and dive into the data to learn more about this iconic city! 🌉

This data accompanies the study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" published in (Nature) Communications Engineering. This paper focuses on using large and inexpsensive datasets for obtaining information on the dynamics of bridges. In this study, data is collected by smartphones in moving vehicles as the cross over a bridge, in three distinct applications. Smartphone data was collected in controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and an analytical method was developed to accurately recover modal properties. The method was also successfully applied to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. The results suggest that larve and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure. The data provided includes the source data for the figures in the publication as well as the "controlled data" referenced in the study. Methods All data were recorded by an iPhone 5 and iPhone 6 using the Sensor Play App. Two-hundred and four datasets (102 per phone) were collected during vehicle trips over the Golden Gate Bridge in morning and afternoon rush-hour periods over five days (June 18 - 22, 2017). The positions and orientations of the phones were fixed during data collection. The operator manually hit "record" on each phones at the approach of the bridge and hit "stop" at the end of the bridge -- the data is not syncrhonized. Two sedan-style vehicles were used and five target speeds were defined: 32, 40, 48, 56, and 64 km/hr (note the speed limit on the bridge is 72 km/hr). Datasets 1-50 were collected by a Nissan sedan, and datasets 51-102 were collected by a Ford sedan. Each dataset is provided as a CSV file with twenty-four channels of raw data including timestamps, the default output for the Sensor Play App:

Accelerometer: X, Y, Z Gyroscope: X, Y, Z Attitude: Roll, Pitch, Yaw Location: Longitude, Latitude, Speed, TrueHeading, Altitude Motion Activity: Type & Confidence Barometer: Pressure, Relative Altitude Magnetometer: µT X, Y, Z, calibrated x,y,z,

The referenced study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" only analyzed vertical acceleration (acceleration in the Z-direction) and GPS lat-long channels, which were sampled at 100 Hz and 1 Hz, respectively. Further details of the data and analysis are available in the Methods section of "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips". Source data is provided for figures 2, 3, 4, 5, and 7 in the Main Text of "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" and figures S1, S2, and S3 of the Supplementary Material

These data provide an accurate high-resolution shoreline compiled from imagery of San Francisco Bay, Golden Gate to Point Chauncey, CA . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coast...

Structural information deficits about our aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of large-scale deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive yet has produced structural information, i.e., modal frequencies, in crowdsensing applications. However, current methods cannot extract spatial vibration characteristics, which are needed for damage identification. Here we present the most extensive real-world study on bridge monitoring with crowdsourced smartphone-vehicle trips and simulate damage detection capabilities. Our method analyzes over 500 trips across four bridges with main spans ranging from 30 to 1300 meters in length, representing about one-quarter of US bridges, and extracts absolute value mode shapes, a damage-sensitive feature. We d..., This data set was collected from various sources: the research team, ANAS employees, and Uber drivers. The method for data collection and data processing for each dataset can be found in the related works. , , # Commodifying infrastructure spatial dynamics with crowdsourced smartphone data

https://doi.org/10.5061/dryad.15dv41p49

The dataset consists of acceleration and position measurements taken using smartphones as vehicles drove across various bridges. Current projects using this data revolve around mobile sensing and system identification. The accelerations were measured using the smartphone's onboard triaxial accelerometer. However, the position measurements are a process version of the GPS measurements. The position measurements are 1D distance from a bridge pier, measured in meters, rather than the raw 2D GPS measurements from the phone. Within this repository, there are multiple case studies, and between cases, the method of data collection varied. The methods for data collection can be found in the related work. A Jupyter Notebook accompanies the datasets to reproduce the Golden Gate Bridge absolute mode shape results in the related work....

About San Francisco San Francisco is a vibrant and dynamic city located on the west coast of the United States, in the state of California. Known for its hilly terrain, diverse neighborhoods, and iconic landmarks such as the Golden Gate Bridge and Alcatraz Island, San Francisco is a hub of culture, creativity, and innovation. The city is renowned for its world-class restaurants, thriving arts scene, and historic architecture, and is home to many tech companies and startups. With its mild climate, stunning views, and rich history, San Francisco is a must-visit destination for travelers from around the world.

About Dataset This dataset contains daily weather observations for San Francisco, USA from January 1, 1993 to January 1, 2023. The data is collected from Meteostat. The dataset contains 10 columns with 10958 rows.

These data were automated to provide an accurate high-resolution historical shoreline of Bay Area of CA, Golden Gate Entrance, CA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field s...

A. SUMMARY A list of street centerlines, including both active and retired streets. These centerlines are identified by their Centerline Network Number ("CNN").B. HOW THE DATASET IS CREATED This data is extracted from the Department of Public Works Basemap. Supervisor District and Analysis Neighborhood are added during the loading process. These boundaries utilize the centroid (middle) of the line to determine the district or neighborhood.C. UPDATE PROCESS This dataset refreshes daily, though the data may not change every day.D. HOW TO USE THIS DATASET Note 1: The Class Code field is used for symbolization:1 = Freeway2 = Major street/Highway3 = Arterial street4 = Collector Street5 = Residential Street6 = Freeway Ramp0 = Other (private streets, paper street, etc.)E. RELATED DATASETS Understanding street-level dataData pushed to ArcGIS Online on November 10, 2025 at 3:25 AM by SFGIS.Data from: https://data.sfgov.org/d/3psu-pn9hDescription of dataset columns:

 cnn
 Centerline Network Number - unique identifier for dataset


 lf_fadd
 From address number on left side of street, the lowest number in the address range


 lf_toadd
 To address number on left side of street, the highest number in the address range


 rt_fadd
 From address number on right side of street, the lowest number in the address range


 rt_toadd
 To address number on right side of street, the highest number in the address range


 street
 Street name without street type


 st_type
 Street Type (AVE, ST, BLVD, et al.)


 f_st
 The street name of the segment intersects at its beginning.


 t_st
 The street name of the segment intersects at its end.


 f_node_cnn
 Centerline Network Number for the node/intersection that the street segment begins from.


 t_node_cnn
 Centerline Network Number for the node/intersection that the street segment ends on.


 accepted
 Accepted by City and County of San Francisco for maintenance.


 active
 Active street segment, i.e., not retired.


 classcode
 Classification code for street segment. Used for symbolization: 1 = Freeway 2 = Major street/Highway 3 = Arterial street 4 = Collector Street 5 = Residential Street 6 = Freeway Ramp 0 = Other (private streets, paper street, etc.)


 date_added
 Date added to dataset by Public Works.


 date_altered
 Date altered to dataset by Public Works.


 date_dropped
 Date dropped to dataset by Public Works.


 gds_chg_id_add
 The internal change transaction id when the segment was added.


 gds_chg_id_altered
 The internal change transaction id when the segment was altered.


 gds_chg_id_dropped
 The internal change transaction id when the segment was dropped/retired.


 jurisdiction
 Agency with jurisdiction over the segment, if any.


 layer
 Derived from the source AutoCAD drawing, this field indicates the category of segment. Definitions for each of the values: Freeways such as 80, 280 and 101. Paper, the centerline segment is present on Assessor and/or Public Works map, but is not an actual street in reality. Paper_fwys, the centerline segment is present on Assessor and/or Public Works map, but is not an actual street in reality, and is under or near a freeway. Paper_water, the centerline segment is present on Assessor and/or Public Works map, but is not an actual street in reality, and is under water in the Bay. PARKS, street segement maintained by Recreation and Park Department, e.g., in Golden Gate Park. Parks_NPS_FtMaso, street segement maintained by the National Park Service within Fort Mason. Parks_NPS_Presid, street segement maintained by the National Park Service within the Presidio. Private, street segment is not maintained by the City and is not on an Assessor or Public Works map. Private_parking, street segment is not maintained by the City and is not on an Assessor or Public Works map, and is a parking lot. PSEUDO, street segment created for use in addressing. Streets, standard street centerline segement. Streets_HuntersP, standard street centerline segement within the Hunters Point Shipyard area. Streets_Pedestri, standard street centerline segement, but pedestrian access only. Streets_TI, standard street centerline segement within Treasure Island. Streets_YBI, standard street centerline segement within Yerba Buena Island. UPROW, Unpaved Right of Way street centerline segment.


 nhood
 SFRealtor-defined neighborhood that the segment is primarily intersects


 oneway
 Indicates if street segment is a one way street: possible values are F (the segment is one way beginning at the "from" street) , T (the segment is one way beginning at the "to" street), or B (traffic is legal in "both" directions)


 street_gc
 Street name without street type, with the numbered streets with leading zeroes dropped to facilitate geocoding


 streetname
 Full street name and street type


 streetname_gc
 Full street name and street type, with the numbered streets with leading zeroes dropped to facilitate geocoding


 zip_code
 ZIP Code that street segment falls in.


 analysis_neighborhood
 current analysis neighborhood


 supervisor_district
 current supervisor district


 line
 Geometry


 data_as_of
 Timestamp the data was updated in the source system


 data_loaded_at
 Timestamp the data was loaded to the open data portal

Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.

Climatic data from Golden Gate from 2010. Visit https://dataone.org/datasets/peggym.1108.55 for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/peggym.1205.7 for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/%7BB2ED68F2-0DAD-4A6D-8B4C-22E014BC8AA1%7D for complete metadata about this dataset.

This part of DS 781 presents data for the geologic and geomorphic map of the Offshore of San Francisco map area, California. The polygon shapefile is included in "Geology_OffshoreSanFrancisco.zip," which is accessible from http://pubs.usgs.gov/ds/781/OffshoreSanFrancisco/data_catalog_OffshoreSanFrancisco.html.

The Offshore of San Francisco map area includes the Golden Gate inlet which connects the Pacific Ocean and San Francisco Bay. San Francisco Bay, the largest estuary on the U.S. west coast, is located at the mouth of the Sacramento and San Joaquin rivers and drains over 40 percent of the state of California. The large surface area of the bay and diurnal tidal range of 1.78 m creates an enormous tidal prism (about 2 billion cu m) and strong tidal currents, commonly exceeding 2.5 m/s (Barnard and others, 2006a, 2006b, 2007). Acceleration of these currents through the constricted inlet has led to scouring of a bedrock channel that has a maximum depth of 113 m. Large fields of sand waves (Barnard and others, 2007) (unit Qmsw) have formed both west and east of this channel as flow expands and tidal currents decelerate. Active tidally influenced map units inside San Francisco Bay also include sand-dominated deposits (unit Qbs) and more coarse-grained sand, gravel, and pebble deposits (unit Qbsc). Sand wave fields resulting from tidal flow are also present in the nearshore along the Pacific Coast, both north and south of the Golden Gate inlet. The sand wave fields appear to be variably mobilized by both ebb and flood tides, but the presence of a large (~150 sq km) ebb-tidal delta at the mouth of the bay west of the inlet indicates net sediment transport has been to the west. The ebb-tidal delta west of the Golden Gate inlet is mapped as two units. The inner part of the delta (unit Qmst) comprises a semi-circular, inward-sloping (i.e., toward the Golden Gate inlet), sandy seafloor at water depths of about 12 to 24 m. This inner delta has a notably smooth surface, indicating sediment transport and deposition under different flow regimes (defined by tidal current strength and depth) than those in which the sand waves formed and are maintained.

Further deceleration of tidal currents beyond the inner delta has led to development of a large, shoaling (about 8 to 12 m water depth), horse-shoe shaped, delta-mouth bar (unit Qmsb). This feature (the "San Francisco Bar") surrounds the inner delta, and its central crest is cut by a dredged shipping channel that separates the nothern and southern parts of the bar, the "North Bar" and "South Bar," respectively. The San Francisco Bar is shaped by both tidal currents and waves, which regularly exceed 6 m in height on the continental shelf during major winter storms (Barnard and others, 2007). This mix of tidal and wave influence results in a variably hummocky, mottled, and rilled seafloor, and this surface texture is used as a primary criteria for mapping the unit and defining its boundaries. Outside the San Francisco Bar to the limits of the map area, the notably flat shelf (less than 0.2 degrees) and the nearshore are wave-dominated and characterized by sandy marine sediment (unit Qms). Local zones of wave-winnowed (?) coarser sediment (unit Qmsc) indicated by high backscatter occur along the coast offshore Ocean Beach. Unit Qmsc is also mapped inside and at the mouth of the Golden Gate inlet where it presumably results from winnowing by strong tidal currents. Coarser sediment also occurs as winnowed lags in rippled scour depressions (unit Qmss), recognized on the basis of high-resolution bathymetry and backscatter. These depressions are typically a few tens of centimeters deep and are bounded by mobile sand sheets (for example, Cacchione and others, 1984). This unit occurs primarily in the nearshore south of the Golden Gate inlet offshore of Ocean Beach (water depth less than 13 m) and north of the inlet offshore Muir Beach (water depth less than 17 m).

Artificial seafloor (unit af) has several distinct map occurrences: (1) sites of active sand mining inside San Francisco Bay; (2) the dredged shipping channel at the central crest of the San Francisco Bar; (3) the sewage outfall pipe, associated rip rap, and surrounding scour channel offshore Ocean Beach; and (4) the location of a former waste disposal site about 2.5 km offshore Point Lobos.

Although the map shows the areas in which several active sedimentary units (Qmsw, Qmst, Qmsb, Qms, Qmsc, Qmss, Qbsm, Qbsc) presently occur, it is important to note that map units and contacts are dynamic and ephemeral, likely to change during large storms, and on seasonal to decadal scales based on changing external forces such as weather, climate, sea level, and sediment supply. Dallas and Barnard (2011) have noted, for example, that the ebb-tidal delta has dramatically shrunk since 1873 when the first bathymetric s... Visit https://dataone.org/datasets/b45a8e51-b0fc-4d6d-a945-b19caa72618d for complete metadata about this dataset.

No description is available. Visit https://dataone.org/datasets/peggym.113.9 for complete metadata about this dataset.