The residential construction boom in 2020 and 2021 led to robust growth for fence contractors, driven by low interest rates that spurred increased housing starts and private home improvements. While the landscape shifted when the Federal Reserve raised interest rates in 2022 and 2023, slowing construction activity, rate cuts in 2024 began to reverse trends and boost construction activity. Also, fence contractors have been buoyed by growth in commercial markets, particularly because of the expansion of warehouse and data center construction. Still, challenges like labor shortages and fluctuating material costs have hindered profit. Industry revenue has been climbing at a CAGR of 1.5% over the past five years to total an estimated $14.8 billion in 2025, including an estimated increase of 2.3% in 2025. The surge in demand from residential construction early on and later from certain commercial markets boosted opportunities for fencing projects. Simultaneously, federal investments in infrastructure provided a steady stream of projects, particularly through the Infrastructure Investment and Jobs Act. Yet, growth wasn’t without its hurdles. Labor shortages exacerbated by an aging workforce and perceptions of the construction sector as an unattractive field deterred younger workers, driving up wages to retain talent. In addition, soaring prices of raw materials like steel and wood affected costs, squeezing profit. Looking ahead, fence contractors are poised for promising opportunities over the next five years, as continued rate cuts by the Federal Reserve are expected to boost residential construction. Low housing stock will further fuel growth in new single-family and multifamily housing. Meanwhile, commercial construction projects, especially hotels and AI data centers, will rise, benefitting fence contractors. The trend towards sustainable and tech-integrated fencing solutions presents contractors with innovative avenues to attract a growing market segment interested in eco-friendly and advanced property protection. Falling material costs are likely to alleviate some price pressures. However, labor shortages and increased competition may continue challenging profit, necessitating strategic adjustments by fence contractors to maintain growth. Revenue is forecast to increase at a CAGR of 1.9% to total an estimated $16.3 billion through the end of 2030.

Feature layer containing authoritative park fence lines for Sioux Falls, South Dakota.

This layer shows the location of Walls and Fences on the State Road Network. The Wall or Fence are a continuous vertical structure usually of brick, stone or wood, used as a noise barrier or boundary fence.Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

The planimetric data was compiled by The Sanborn Map Company, Inc for the Metropolitan District and is based on an aerial flight performed in April 2015. In addition, the City's GIS staff has been updating limited planimetric features based on information on file in various City departments. The planimetric data has also been updated in 2016 and yearly to current based on spring aerial flights by EagleView.

The layer refers to lines and symbols indicating the location of various types of Fences (Bollards, Perimeter Fences, Barrier Fences, etc.) on public land in the Gold Coast area. Please note that as part of the atttribution of this data under the CC BY licence terms with which it is supplied, users should include the following statement: 'The information is provided to assist in field investigations. All locations, dimensions and depths shown are to be confirmed on site'.

The City of Gold Coast is not a professional information provider and makes no representations or warranties about the accuracy, reliability, completeness or suitability for any particular purpose of the Data provided here. This Data is not provided with the intent that any person will rely on it for the purpose of making decisions with financial or legal implications. Persons who place such reliance on the Data do so at their own risk.

Fencing is a major anthropogenic feature affecting human relationships, ecological processes, and wildlife distributions and movements, but its impacts are difficult to quantify due to a widespread lack of spatial data. We created a fence model and compared outputs to a fence mapping approach using satellite imagery in two counties in southwest Montana, USA to advance fence data development for use in research and management. The model incorporated road, land cover, ownership, and grazing boundary spatial layers to predict fence locations. We validated the model using data collected on randomized road transects (n = 330). The model predicted 34,706.4 km of fences with a mean fence density of 0.93 km/km2 and a maximum density of 14.9 km/km2. We also digitized fences using Google Earth Pro in a random subset of our study area in survey townships (n = 50). The Google Earth approach showed greater agreement (K = 0.76) with known samples than the fence model (K = 0.56) yet was unable to map fences in forests and was significantly more time intensive. We also compared fence attributes by land ownership and land cover variables to assess factors that may influence fence specifications (e.g., wire heights) and types (e.g., number of barbed wires). Private lands were more likely to have fences with lower bottom wires and higher top wires than those on public lands with sample means at 22 cm and 26.4 cm, and 115.2 cm and 110.97, respectively. Both bottom wire means were well below recommended heights for ungulates navigating underneath fencing (≥ 46 cm), while top wire means were closer to the 107 cm maximum fence height recommendation. We found that both fence type and land ownership were correlated (χ2 = 45.52, df = 5, p = 0.001) as well as fence type and land cover type (χ2 = 140.73, df = 15, p = 0.001). We provide tools for estimating fence locations, and our novel fence type assessment demonstrates an opportunity for updated policy to encourage the adoption of “wildlife-friendlier” fencing standards to facilitate wildlife movement in the western U.S. while supporting rural livelihoods. Methods For the fence model and fence density layers, the data was adapted from publicly available spatial layers informed by local expert opinion in Beaverhead and Madison Counties, MT. Data used included Montana Department of Transportation road layers, land ownership data from Montana State Library cadastral database, land cover data from the 2019 Montana Department of Revenue Final Land Unit (FLU), and railroad data from the Montana State Library. The data was processed in ArcMap 10.6.1 to form a hierarchical predictive fence location and density GIS model. For the Google Earth mapped fences, data was collected by examining satellite imagery and tracing visible fence lines in Google Earth Pro version 7.3.3 (Google 2020) within the bounds of 50 random survey township polygons in Beaverhead and Madison Counties.

Financial overview and grant giving statistics of Freedom Fences

About this dataset

Context

The dataset tabulates the Fence town household income by age. The dataset can be utilized to understand the age-based income distribution of Fence town income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Fence, Wisconsin annual median income by age groups dataset (in 2022 inflation-adjusted dollars)
• Age-wise distribution of Fence, Wisconsin household incomes: Comparative analysis across 16 income brackets

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Fence town income distribution by age. You can refer the same here

This table contains 174 series, with data for years 1961 - 1992 (not all combinations necessarily have data for all years), and was last released on 2000-02-18. This table contains data described by the following dimensions (Not all combinations are available): Geography (12 items: Canada; Atlantic Region; Nova Scotia; Eastern Canada ...), Price index (8 items: Building and fencing; Building replacement; Materials; Labour ...), Index year (2 items: 1981=100; 1986=100 ...).

Overview The IITKGP_Fence dataset is designed for tasks related to fence-like occlusion detection, defocus blur, depth mapping, and object segmentation. The captured data vaies in scene composition, background defocus, and object occlusions. The dataset comprises both labeled and unlabeled data, as well as additional video and RGB-D data. The contains ground truth occlusion masks (GT) for the corresponding images. We created the ground truth occlusion labels in a semi-automatic way with user interaction.

Key Dataset Features:

Fence Detection: Designed for detecting fences or fence-like structures that might occlude objects. Defocus Blur: Also contains images and videos with blurred objects, likely to challenge detection and segmentation algorithms. RGBD Data: Offers depth information alongside RGB images, which can be used for tasks like 3D reconstruction or occlusion handling. Unlabeled and Labeled Data: Facilitates both supervised and unsupervised learning tasks. The Labeled folder data provides ground truth occlusion masks, while the Unlabeled folder data allows for further experimentation or self-supervised methods.

Dataset Repository

GitHub Repository: Occlusion-Removal Paper: Deep Generative Adversarial Network for Occlusion Removal from a Single Image Authors: Sankaraganesh Jonna, Moushumi Medhi, Rajiv Ranjan Sahay

Contact medhi.moushumi@iitkgp.ac.in

Number of Businesses statistics on the Fence Construction industry in United States

Fences have recently been recognized as one of the most prominent linear infrastructures on earth. As animals traverse fenced landscapes, they adjust movement behaviors to optimize resource access while minimizing energetic costs of coping with fences. Examining individual responses is key for connecting localized fence effects with population dynamics. We investigated the multi-scale effects of fencing on animal movements, space use, and survival of 61 pronghorn and 96 mule deer on a gradient of fence density in Wyoming, USA.
Taking advantage of the recently developed Barrier Behavior Analysis, we classified individual movement responses upon encountering fences (i.e. barrier behaviors). We adopted the reaction norm framework to jointly quantify individual plasticity and behavioral types of barrier behaviors, as well as behavior syndromes between barrier behaviors and animal space use. We also assessed whether barrier behaviors affect individual survival.
Our results highlighted a high level individual plasticity encompassing differences in the degree and the direction of barrier behaviors for both pronghorn and mule deer. Additionally, these individual differences were greater at higher fence densities. For mule deer, fence density determined the correlation between barrier behaviors and space use, and was negatively associated with individual survival. Yet, these relationships were not statistically significant for pronghorn.
By integrating approaches from movement ecology and behavioral ecology with the emerging field of fence ecology, this study provides new evidence that an extraordinarily widespread linear infrastructure uniquely impacts animals at the individual level. Managing landscape for lower fence densities may help prevent irreversible behavioral shifts for wide-ranging animals in fenced landscapes.

This dataset is a polyline layer that depicts fencing within Mono County, CA.

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of fence (fence, guardrail, or hedge)

Broken Fence

## Overview

Broken Fence is a dataset for object detection tasks - it contains Fence annotations for 1,208 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).

Financial overview and grant giving statistics of Mended Fences Inc

Western Australia's State Barrier Fence (SBF) plays an important role in preventing animal pests such as wild dogs from moving into the State's agricultural areas from pastoral areas in the east. The fence is a state asset set within a 20 metre reserve, which is managed by the DPIRD. This dataset defines the current SBF location. It does not include abandoned or proposed sections. Show full description

Workplace safety and injury data for Rio Grande Fence Co. of Nashville

The Plastic Wood Fence market has emerged as a significant player in the fencing industry, combining durability and sustainability to cater to diverse consumer needs. These innovative fences, made from recycled plastic and wood fibers, provide an eco-friendly alternative to traditional wooden fencing, offering enhan

Net-wire fencing built to confine livestock is common on rangelands in the Southwestern USA, yet the impacts of livestock fencing on wildlife are largely unknown. Many wildlife species cross beneath fences at defined crossing locations because they prefer to crawl underneath rather than jump over fences. Animals occasionally become entangled jumping or climbing over fences, leading to injury or death. More commonly, repeated crossings under net-wire fencing by large animals lead to fence damage, though the damage is often tolerated by landowners until the openings affect the ability to enclose livestock. The usage, placement, characteristics, and passage rates of fence crossings beneath net-wire fencing are poorly understood. We monitored 20 randomly selected fence crossings on net-wire livestock fencing across two study sites on rangelands in South Texas, USA, from April 2018–March 2019. We assessed characteristics of fence-crossing locations (openings beneath the fence created by animals to aid in crossing) and quantified crossing rates and probability of crossing by all species of animals via trail cameras. We documented 10,889 attempted crossing events, with 58% (n = 6,271) successful. Overall, 15 species of medium- and large-size mammals and turkey (Meleagris gallopavo) contributed to crossing events. Crossing locations received 3–4 crossing attempts per day on average, but the number of attempts and probability of successful crossing varied by location and fence condition. Probability of crossing attempts was most consistently influenced by opening size of the crossing and season; as crossing size (opening) increased, the probability of successful crossing significantly increased for all species. Peaks in crossing activity corresponded with species' daily and seasonal movements and activity. Density and size of fence-crossing locations were dependent on fence maintenance and not associated with vegetation communities or habitat variables. However, crossing locations were often re-established in the same locations after fence repairs. This is one of the few studies to monitor how all animal species present interacted with net-wire livestock fencing in rangelands. Our results will help land managers understand the impact of net-wire livestock fencing on animal movement.