Cellular Tower Locations Dataset

Inaccurate Licensing Data for Cellular Tower Locations

By Homeland Infrastructure Foundation [source]

About this dataset

The dataset includes locational identifiers such as X and Y coordinates, representing the geographical position of each cellular tower. Additionally, there are columns specifying the direction (North or South) for latitude (LATDIR) and direction (East or West) for longitude (LONDIR). Detailed descriptions of each cellular tower's address can be found in LOCADD column.

Moreover, important details regarding licensing are also provided in the dataset. LICENSEE column indicates the organization or entity holding the license for a particular cellular tower location. Meanwhile, CALLSIGN represents a unique identifier assigned to individual towers. Each specific location is further identified with a location number in LOCNUM column.

For precise geographical positioning of towers leveraging degrees-minutes-seconds format on separate columns LAT_DMS and LON_DMS provide detailed latitude and longitude values respectively.

To assess potential environmental impacts associated with these towers based on National Environmental Policy Act criteria defied by NEPA status offered under spell NEPA category classification helps analyze their nature.

This dataset also identifies administrative divisions where each tower is situated through LOCCOUNTY column indicating county name while LOCCITY representing city names provides finer granularity within administrative boundaries similarly tactic followed with states available under LOCSTATE label which specifies state names accomodating diversity throughout USA geographically.

Further insightful data includes QZONE field which allocates terminals into definitive zones known as Quadrangle Zones according to engineering standards resulting precise charting turnouts facilitating manangement across operational territorries

Detailed explanations about type of structures attached to cellulaar towers prevailing at diverse sites have been addressed using two types categorical features ALLSTRUC and STRUCTYPE.

To track the tower registration number details required in order to adhere certain regulatory comliance TOWREG field hands out peculiar identification numbers.

Considering diverse factors relevant to cellular tower locations such as supporting structure and licensing information, this comprehensive dataset offers valuable insights for various stakeholders. Whether it is conducting environmental assessments, understanding geographical distribution, or studying license holders' data, this dataset serves as a treasure trove of granular information for analysis and decision-making purposes

How to use the dataset

Here is a guide on how to effectively use this dataset:

• Familiarize Yourself with the Columns:

  • Begin by understanding the different columns in the dataset. Each column represents a specific attribute or characteristic associated with cellular towers.
  • The important columns to note are:
    • X, Y: The coordinates of the cellular tower.
    • LICENSEE: The entity or organization that holds the license for the cellular tower.
    • CALLSIGN: The unique identifier assigned to each cellular tower.
    • LOCNUM: The location number assigned to each cellular tower.
    • LAT_DMS, LON_DMS: The latitude and longitude coordinates represented in degrees, minutes, and seconds format.
    • LATDIR, LONDIR: The directions (North/South/East/West) associated with latitude and longitude coordinates respectively.

• Analyze Geographical Distribution:

  • Use X and Y coordinates along with other location-related attributes (LOCADD, LOCCITY, LOCCOUNTY) to analyze the geographical distribution of cellular towers on maps or visualizations.
  • Identify clusters or patterns of cellular towers in specific areas or regions.

• Identify Licensing Information Errors:

  • Pay attention to potential errors or inconsistencies in licensing information within LICENSEE and CALLSIGN columns.
  • Compare these fields across multiple records to identify discrepancies that could indicate inaccuracies or mistakes.

• Determine Tower Types and Structures:

  • Examine ALLSTRUC and STRUCTYPE columns to understand different types of structures associated with each cell tower (e.g., monopole, lattice). -* Gain insights into the structural characteristics of cellular towers within the dataset.

• Assess Environmental Impact:

  • NEPA column indicates the National Environmental Policy Act status of each cellular tower.
  • Analyze this attribute to evalu...

Data from an integrated butterfly monitoring dataset the United States from 2000-2020. Data span 19 butterfly monitoring programs and the integrated dataset contains over 1.2 million count records, from 65,000 surveys, representing over 12.6 million individual butterflies. Data sharing policies vary across programs so data have been anonymized as appropriate per each program's request. In addition to the integrated dataset, we share the code used to compile and clean raw data, harmonize taxonomy, and validate the final dataset. This dataset is a more granular version of the data used in the publication "Rapid butterfly declines across the United States during the 21st Century". The data for that publication is available here: https://doi.org/10.6084/m9.figshare.27934629.v1The Code map file in the parent directory provides general descriptions of the provided code. A key note is that much of the provided code will not run because we are not sharing non-anonymized data. Some programs allow raw data to be shared, others require that latitude and longitude are rounded to obscure exact sampling locations. Similarly with site IDs some programs share siteIDs as reported in the original data, others require anonymized site IDs. The details of each program's data sharing permissions can be found in '1_raw_data.' Despite the fact that we cannot share all raw data with this data release, we shared all the code used to assemble the original integrated dataset. A guide to the code, data, and sequence of analysis can be found in '3_scripts/clean-run.R'. If a user were to request and obtain the original data files from program directors, this script will assemble the original, integrated dataset. The one script in the directory that will run here is '3_scripts/data-figures-eh.R'; it will replicate the figure from the publication. The taxonomic dictionaries we used to harmonize taxonomy across programs can be found in '2_data_wrangling/dictionaries' and the script in which the dictionaries are used is in '3_scripts/data-cleaning-scripts/final-data-integration.R'. These dictionaries and scripts may be useful for other work harmonizing names/taxonomies.This work is a product of the Status of Butterflies working group funded by the USGS John Wesley Powell Center for Analysis and Synthesis.

PredictLeads Job Openings Data provides real-time hiring insights sourced directly from company websites, ensuring the highest level of accuracy and freshness. Unlike job boards that rely on aggregated listings, our dataset delivers unmatched granularity on job postings, salary trends, and workforce demand - making it a powerful tool for HR, talent acquisition, and market analysis.

Use Cases: ✅ Job Boards Enhancement – Improve job listings with, high-quality postings. ✅ HR Consulting – Analyze hiring trends to guide workforce planning strategies. ✅ Employment Analytics – Track job market shifts, salary benchmarks, and demand for skills. ✅ HR Operations – Optimize recruitment pipelines with direct employer-sourced data. ✅ Competitive Intelligence – Monitor hiring activities of competitors for strategic insights.

Key API Attributes:

• id (string, UUID) – Unique job posting identifier.
• title (string) – Job title as posted by the employer.
• description (string) – Full job description.
• url (string, URL) – Direct link to the job posting.
• first_seen_at (ISO 8601 date-time) – When the job was first detected.
• last_seen_at (ISO 8601 date-time) – When the job was last detected.
• contract_types (array of strings) – Employment type (e.g., full-time, contract).
• categories (array of strings) – Job categories (e.g., engineering, sales).
• seniority (string) – Job seniority level (e.g., manager, entry-level).
• salary_data (object) – Salary range, currency, and converted USD values.
• location_data (object) – City, country, and region details.
• tags (array of strings) – Extracted skills and keywords from job descriptions.

PredictLeads Docs: https://docs.predictleads.com/v3/guide/job_openings_dataset

The Quarterly External Debt Statistics (QEDS/SDDS) database, launched by the World Bank in October 2014, provides comprehensive and detailed external debt data starting from the first quarter of 1998. This dataset adheres to the classifications and definitions outlined in the 2013 External Debt Statistics Guide and the Sixth Edition of the Balance of Payments and International Investment Position Manual (BPM6).

The QEDS/SDDS database covers a wide range of countries, including those that subscribe to the IMF's Special Data Dissemination Standard (SDDS) as well as General Data Dissemination System (GDDS) participating countries capable of producing the necessary external debt data. It includes information from various regions such as East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa.

Key features of this dataset include:

Geographical Coverage: Global, with detailed data from multiple regions and countries. Temporal Coverage: Data from 1997 to 2024. Periodicity: Quarterly updates. Granularity: National-level data. This dataset is an invaluable resource for researchers, policymakers, and analysts interested in understanding the external debt dynamics of countries and regions over time. The data is sourced from the World Bank's Data API and is updated quarterly, with updates scheduled for January, April, July, and October. The first publication date was July 17, 2010, and the dataset is continuously updated, with the latest update being on July 17, 2024.

This dataset is a Scottish Fuel Poverty Index created in the summer of 2023 by EDINA@University of Edinburgh as part of their student internship programme. The user guide provides descriptions of each data variable used in creating the index. The basic rationale was to replicate for Scotland work that had been conducted previously but only in respect to England and Wales. The two indices are not strictly directly comparable due to data availability and spatial granularity but provide standalone snapshots of relative fuel poverty across Great Britain. The Scottish Index is fully open source and for purposes of transparency and repeatability this guide provides an open methodology and is accompanied by the underlying data. Data are provided in good faith ’as is’ and is the sole product of student effort as part of mentoring activities conducted by EDINA at the University. Each variable that was used in the Index was normalised relative to the individual values for that variable - which means the values presented in the underlying FPI data table do not represent the actual numbers for each local authority - merely the percentage relative to the other local authorities in Scotland. A separate file ”Fuel-poverty-index-raw-data-with-calc.csv” is available which contains the raw percentages used for the index along with a table containing the calculations used to obtain the final score and the main FPI data table. Fuel Poverty Index Excel: This file contains each Scottish local authority's ability to pay score, demand score and final score which were all obtained from the several different variables. The raw data for these variables can be found in the Raw Data file and an explanation for each variable can be found in the User Guide document. The scores are between 1 to 100 and are normalised relative to each other. This means the final scores do not represent the actual physical values for each area. Fuel Poverty Index csv: This file contains the normalised processed data that makes up the Scottish fuel poverty index with variables being in range of 1 to 100. Some variables have been weighted depending on how important they are to the index. The final scores rating each Scottish local authority from 1 to 100 are also included. Raw data: This file contains the raw unprocessed data that the index was created from for all Scottish local authorities. User Guide: This file contains the documentation of the process to create the index as well as descriptions of what each column in the Fuel Poverty Index csv file contain. This file also provides some examples of the visualisation created from the index Fuel Poverty Index Shapefile: This folder contains the .shp shape file comprising all the data from Fuel Poverty Index csv, in addition to also having the geospatial polygons associated with each local authority boundary. For the best viewing, the British National Grid EPSG 27700 coordinate system should be used.

An Open Context "documents" dataset item. Open Context publishes structured data as granular, URL identified Web resources. This record is part of the "Murlo" data publication.

List of typical personally identifiable variables of health record data. Adapted from HL7 documentation [12] and Provincial Health Data Centre [13].

You can also access a zipped csv file version of this

dataset.TMS

(traffic monitoring system) daily-updated traffic counts CSVData reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new

contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites.

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

TMS

(traffic monitoring system) traffic – historic quarter hourly