Commercial valuation data collected and maintained by the Cook County Assessor's Office, from 2021 to present. The office uses this data primarily for valuation and reporting. This dataset consolidates the individual Excel workbooks available on the Assessor's website into a single shared format. Properties are valued using similar valuation methods within each model group, per township, per year (in the year the township is reassessed). This dataset has been cleaned minimally, only enough to fit the source Excel workbooks together - because models are updated for each township in the year it is reassessed, users should expect inconsistencies within columns across time and townships. When working with Parcel Index Numbers (PINs) make sure to zero-pad them to 14 digits. Some datasets may lose leading zeros for PINs when downloaded. This data is property-level. Each 14-digit key PIN represents one commercial property. Commercial properties can and often do encompass multiple PINs. Additional notes: Current property class codes, their levels of assessment, and descriptions can be found on the Assessor's website. Note that class codes details can change across time. Data will be updated yearly, once the Assessor has finished mailing first pass values. If users need more up-to-date information they can access it through the Assessor's website. The Assessor's Office reassesses roughly one third of the county (a triad) each year. For commercial valuations, this means each year of data only contain the triad that was reassessed that year. Which triads and their constituent townships have been reassessed recently as well the year of their reassessment can be found in the Assessor's assessment calendar. One KeyPIN is one Commercial Entity. Each KeyPIN (entity) can be comprised of one single PIN (parcel), or multiple PINs as designated in the pins column. Additionally, each KeyPIN might have multiple rows if it is associated with different class codes or model groups. This can occur because many of Cook County's parcels have multiple class codes associated with them if they have multiple uses (such as residential and commercial). Users should not expect this data to be unique by any combination of available columns. Commercial properties are calculated by first determining a property’s use (office, retail, apartments, industrial, etc.), then the property is grouped with similar or like-kind property types. Next, income generated by the property such as rent or incidental income streams like parking or advertising signage is examined. Next, market-level vacancy based on location and property type is examined. In addition, new construction that has not yet been leased is also considered. Finally, expenses such as property taxes, insurance, repair and maintenance costs, property management fees, and service expenditures for professional services are examined. Once a snapshot of a property’s income statement is captured based on market data, a standard valuation metric called a “capitalization rate” to convert income to value is applied. This data was used to produce initial valuations mailed to property owners. It does not incorporate any subsequent changes to a property’s class, characteristics, valuation, or assessed value from appeals.Township codes can be found in the legend of this map. For more information on the sourcing of attached data and the preparation of this datase

This dataset is about books. It has 1 row and is filtered where the book is A commercial enterprise. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about books. It has 226 rows and is filtered where the book subjects is Commercial statistics. It features 9 columns including author, publication date, language, and book publisher.

This dataset is about books. It has 1 row and is filtered where the book is The economics of commercial property markets. It features 7 columns including author, publication date, language, and book publisher.

SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage and properties depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

SafeGraph provides clean and accurate geospatial datasets on 51M+ physical places/points of interest (POI) globally. Hundreds of industry leaders like Mapbox, Verizon, Clear Channel, and Esri already rely on SafeGraph POI data to unlock business insights and drive innovation.

The table ND-Commercial-2024-03-25 is part of the dataset L2 Consumer Dataset, available at https://stanford.redivis.com/datasets/k4nf-5qq685c0c. It contains 487133 rows across 667 variables.

This file includes data pertaining to Commercial Properties. The "ParcelNumber" field can be joined to the "ParcelNumber" field in the "Parcel Area Details" data set for mapping purposes (current parcels only). This data set is updated on a daily basis and reflects the Real Estate system as of the previous business day.

Note: Active and Retired parcels are included in this dataset so joining this dataset to the datasets: ‘Real Estate (Base Active)’ or ‘Parcel Area Details’ will not result in all parcels matching since ‘Real Estate (Base Active)’ and ‘Parcel Area Details’ only contain currently active parcels.Please refer to this Data Guide for details on how to access and join Real Estate data

The Register includes the following information in respect of all commercial leases entered into since 1 January 2010: The Address of the Commercial Property the Subject of the Lease; The Date of the Lease of the Property; The Term of Years of the Lease, and The Rent Payable in Respect of the Property. All of the above-mentioned information is available free of charge. With regard to commercial leases entered into on or after 3 April 2012, the Act imposes an obligation on the tenants of such properties, to furnish the following additional information to the PSRA: The Commencement Date of the Terms of the Lease; Any Capital Contribution Paid in respect of the Property; The Frequency of Rent Reviews; Liability for Rates, Insurance, Service Charges and Repairs; Particulars of Rent-free Periods, Fitting-out Time, Fit-out Allowances and Capital Considerations, and Particulars of any Break Clause in the Lease. The additional information is available directly from the Property Services Regulatory Authority website and can be accessed through the following url: https://propertypriceregister.ie/Website/NPSRA/pprweb-com.nsf/page/ppr-home-en

The table PA-Commercial-2024-03-25 is part of the dataset L2 Consumer Dataset, available at https://stanford.redivis.com/datasets/k4nf-5qq685c0c. It contains 9528816 rows across 667 variables.

Overview

Mountain Dew is running a $1,000,000 counting contest. Computer Vision can help you win.

:fa-spacer:

Watch our video explaining how to use this dataset.

https://i.imgur.com/ED4jpM3.png" alt="Mountain Dew">

During Super Bowl LV, Mountain Dew sponsored an ad that encourages viewers to count all unique occurrences of Mountain Dew bottles. You can watch the full ad here. The first person to tweet the exactly correct count at Mountain Dew is eligible to win $1 million (see rules here).

Counting things is a perfect place for where computer vision can help.

We uploaded the Mountain Dew video to Roboflow, created three images per each second of the commercial (91 images from ~30 seconds of commercial), and annotated all bottles we could see. This dataset is the result.

We trained a model to recognize the Mountain Dew bottles, and then ran the original commercial back through this model. This helps identify Mountain Dew bottles that the human eye may have missed when completing counts.

https://i.imgur.com/rjZCS2a.png" alt="Image example">

Getting Started

Click "Fork" in the upper right hand corner or download the raw annotations in your desired format.

Note that while the images are property of PepsiCo, we are using them here as fair-use for educational purposes and have released the annotations under a Creative Commons license.

About Roboflow

Roboflow enables teams to use computer vision. :fa-spacer: Our end-to-end platform enables developers to collect, organize, annotate, train, deploy, and improve their computer vision models -- all without needing to hire a new ML engineering team. :fa-spacer:

The table WA-Commercial-2024-03-25 is part of the dataset L2 Consumer Dataset, available at https://stanford.redivis.com/datasets/k4nf-5qq685c0c. It contains 5437859 rows across 667 variables.

The commercial and industrial waste (C&I) data represents total waste produced per year by ABS statistical area 2 (SA2) boundaries. The figures represent the total C&I waste that is produced by institutions and businesses; including waste from schools, restaurants, offices, retail and wholesale businesses, and industries including manufacturing for the 2014-15 period. The SA2 population data was obtained from the 2016 ABS census. Statistical Areas Level 2 (SA2) are medium-sized general purpose areas built up from whole Statistical Areas Level 1. Their purpose is to represent a community that interacts together socially and economically and generally represent populations of >25000 and where possible will mirror suburb and LGA boundaries though this will not always be the case.

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

This dataset collection contains one or more tables sourced from the website of Luke in Finland.

Commercial Aircraft New

## Overview

Commercial Aircraft New is a dataset for object detection tasks - it contains Aircraft annotations for 2,105 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

These are commercial corridors, centers, districts, and projects that provide consumer-oriented goods and services, including retail, food and beverage, and personal, professional, and business services.

This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also uses the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Hourly load profiles are available for over all TMY3 locations in the United States here.

Browse files in this dataset, accessible as individual files and as commercial and residential downloadable ZIP files. This dataset is approximately 4.8GiB compressed or 19GiB uncompressed.

July 2nd, 2013 update: Residential High and Low load files have been updated from 366 days in a year for leap years to the more general 365 days in a normal year.

A real-world radio frequency (RF) fingerprinting dataset for commercial off-the-shelf (COTS) Bluetooth and WiFi emitters under challenging testbed setups is presented in this dataset. The chipsets within the devices (2 laptops and 8 commercial chips) are WiFi-Bluetooth combo transceivers. The emissions are captured with a National Instruments Ettus USRP X300 radio outfitted with a UBX160 daughterboard and a VERT2450 antenna. The receiver is tuned to record a 66.67 MHz bandwidth of the spectrum centered at the 2.414 GHz frequency.

This dataset is about books. It has 26 rows and is filtered where the book subjects is Commercial law-Ireland. It features 9 columns including author, publication date, language, and book publisher.

Table containing authoritative commercial project values for Sioux Falls, South Dakota.