Context

This dataset contains the San Francisco Police Department’s (SFPD) incident reports from 2018 to present. The dataset will be updated daily.

Content

+500.000 rows and 34 columns. Columns' description are listed below.

• Incident Datetime : The date and time when the incident occurred.
• Incident Date : The date when the incident occurred.
• Incident Time : The time when the incident occurred.
• Incident Year : The year when the incident occurred.
• Incident Day of Week : The day of week the incident occurred.
• Report Datetime : The date and time when the report was filed.
• Row ID : A unique identifier for each row of data in the dataset.
• Incident ID : This is the system generated identifier for incident reports. Incident IDs and Incident Numbers both uniquely identify reports, but Incident Numbers are used when referencing cases and report documents.
• Incident Number : The number issued on the report, sometimes interchangeably referred to as the Case Number. This number is used to reference cases and report documents.
• CAD Number : The Computer Aided Dispatch (CAD) is the system used by the Department of Emergency Management (DEM) to dispatch officers and other public safety personnel. CAD Numbers are assigned by the DEM system and linked to relevant incident reports (Incident Number). Not all Incidents will have a CAD Number. Those filed online via Coplogic (refer to “Filed Online” field) and others not filed through the DEM system will not have CAD Numbers.
• Report Type Code : A system code for report types, these have corresponding descriptions within the dataset.
• Report Type Description : Initial, Initial Supplement, Vehicle Initial, Vehicle Supplement, Coplogic Initial or Coplogic Supplement
• Filed Online : “TRUE” or left blank.
• Incident Code : Incident Codes are the system codes to describe a type of incident. A single incident report can have one or more incident types associated.
• Incident Category : A category mapped on to the Incident Code used in statistics and reporting.
• Incident Subcategory : A subcategory mapped to the Incident Code that is used for statistics and reporting.
• Incident Description : The description of the incident that corresponds with the Incident Code.
• Resolution : Cite or Arrest Adult, Cite or Arrest Juvenile, Exceptional Adult, Exceptional Juvenile, Open or Active or Unfounded
• Intersection : The 2 or more street names that intersect closest to the original incident separated by a backward slash.
• CNN : The unique identifier of the intersection for reference back to other related basemap datasets.
• Police District : The Police District where the incident occurred.
• Analysis Neighborhood : This field is used to identify the neighborhood where each incident occurs.
• Supervisor District : There are 11 members elected to the Board of Supervisors in San Francisco, each representing a geographic district. The districts are numbered 1 through 11.
• Latitude : The latitude coordinate in WGS84.
• Longitude : The longitude coordinate in WGS84.
• Point : Geolocation in OGC WKT format.
• Neighborhoods : undefined
• ESNCAG - Boundary File : undefined
• Central Market/Tenderloin Boundary Polygon - Updated : undefined
• Civic Center Harm Reduction Project Boundary : undefined
• HSOC Zones as of 2018-06-05 : undefined
• Invest In Neighborhoods (IIN) Areas : undefined
• Current Supervisor Districts : undefined
• Current Police Districts : undefined

Acknowledgements

Data from DataSF. Image from Thales.

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From: http://kavita-ganesan.com/entity-ranking-data

Downloads: - Dataset - Only reviews (~98MB) [ readme ]

• Dataset with Judgments - Reviews and Relevance Judgments (~100MB) [ readme ]

OpinRank Dataset - Reviews from TripAdvisor and Edmunds Dataset Type: Text Format: Full reviews from Tripadvisor (~259,000 reviews) and Edmunds (~42,230 reviews) Domain: hotels, cars How to cite dataset: [ bib ]

Citing Dataset [ bib ] If you use this dataset for your own research please cite the following to mark the dataset:
Ganesan, K. A., and C. X. Zhai, "Opinion-Based Entity Ranking", Information Retrieval.

@article{ganesan2012opinion, title={Opinion-based entity ranking}, author={Ganesan, Kavita and Zhai, ChengXiang}, journal={Information retrieval}, volume={15}, number={2}, pages={116--150}, year={2012}, publisher={Springer} }

Dataset Overview This data set contains full reviews for cars and and hotels collected from Tripadvisor (~259,000 reviews) and Edmunds (~42,230 reviews).

Car Reviews Dataset Description

Full reviews of cars for model-years 2007, 2008, and 2009 There are about 140-250 cars for each model year Extracted fields include dates, author names, favorites and the full textual review Total number of reviews: ~42,230 Year 2007 -18,903 reviews Year 2008 -15,438 reviews Year 2009 - 7,947 reviews Format There are three different folders (2007,2008,2009) representing the three model years. Each file (within these 3 folders) would contain all reviews for a particular car. The filename represents the name of the car. Within each car file, you would see a set of reviews in the following format:

Note that each review is enclosed within a element as shown above and all the extracted items are within this element.

Hotel Reviews Dataset Description

Full reviews of hotels in 10 different cities (Dubai, Beijing, London, New York City, New Delhi, San Francisco, Shanghai, Montreal, Las Vegas, Chicago) There are about 80-700 hotels in each city Extracted fields include date, review title and the full review Total number of reviews: ~259,000 Format There should be 10 different folders representing the 10 cities mentioned earlier. Each file (within these 10 folders) would contain all reviews related to a particular hotel. The filename represents the name of the hotel. Within each file, you would see a set of reviews in the following format:

Date1