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.

About this dataset

Context

The dataset tabulates the data for the Fence, Wisconsin population pyramid, which represents the Fence town population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Fence, Wisconsin, is 18.8.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Fence, Wisconsin, is 66.4.
• Total dependency ratio for Fence, Wisconsin is 85.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Fence, Wisconsin is 1.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Fence town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Fence town for the selected age group is shown in the following column.
• Population (Female): The female population in the Fence town for the selected age group is shown in the following column.
• Total Population: The total population of the Fence town for the selected age group is shown in the following column.

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/.

Recommended for further research

This dataset is a part of the main dataset for Fence town Population by Age. You can refer the same here

This dataset contains all the raw data sets, processing code, and analysis for reproducing and replicating the analysis for the article: Virtual fencing in remote boreal forests: performance of commercially available GPS collars for free-ranging cattle. In total there are 21 files included, from which '01_Analysis.html' and '01_Analysis.pdf' describes the final output of all analysis and includes the figures as published in the article. '01_Analysis.qmd' is a quarto markdown file (Quarto is a multi-language, next generation version of R Markdown from Posit, see https://quarto.org/) which makes it possible to rerun the analysis. This file is dependent on the other files and the original folder structure. The dependent files include spatial information from the GPS collars ('collars.csv' and 'collars_new.csv'), measures from the differential GPS ('dGPS.csv' and 'dGPS_new.csv'), observations from field personnel ('kobo_forms.csv'), environmental information (all '.tiff' files), and other supporting information. Furthermore, data pre processing is conducted in the R-script '02_preparation_data.R' creating two output files ('processed_data_mob.txt' and 'processed_data_stat.txt'). This script can be optionally sourced from '01_Analysis.qmd'. Article abstract: Background The use of virtual fencing in cattle farming is beneficial due to its flexibility, not fragmenting the landscape or restricting access like physical fences. Using GPS technology, virtual fence units emit an audible signal and a low-energy electric shock when crossing a predefined border. However, animal welfare concerns arise from potential stress and confusion caused by GPS errors. Especially in large remote grazing areas and complex terrains, where the performance of the GPS units can be affected by landscape structure, errors can lead to unnecessary shocks to the animals. This study aimed to explore factors affecting the GPS performance of commercially available virtual fence collars for cattle (NoFence©), both using static tests and mobile tests, i.e. when deployed on free-ranging cattle. Results The static tests revealed generally high fix success rates (% successful positioning attempts), and a lower success rate at four of 30 test locations was most likely due to a lack in GSM coverage. On average the GPS precision and accuracy errors were 3.3 m ±2.5 SD and 4.6 m ±3.2 SD, respectively. We found strong evidence that the GPS precision and accuracy errors were affected by the canopy cover, with increased errors under closed canopies. We also found evidence for an effect of the sky-view on the GPS performance, although at a lesser extent than canopy. The direction of the accuracy error in the cartesian plane was not uniform, but biased, depending on the aspect of the test locations. With an average of 10.8 m ±6.8 SD, the accuracy error of the mobile tests was more than double that of the static tests. Furthermore, we found evidence that more rugged landscapes resulted in higher GPS accuracy errors. However, the error was not affected by canopy cover, sky-view, or behaviors during the mobile tests. Conclusions This study showed that GPS performance can be negatively affected by landscape complexity, such as increased ruggedness and covered habitats, resulting in reduced virtual fence effectiveness and potential welfare concerns for cattle. These issues can be mitigated through proper pasture planning, such as avoiding rugged areas for the virtual fence border.

About this dataset

Context

The dataset presents a breakdown of households across various income brackets in Fence, Wisconsin, as reported by the U.S. Census Bureau. The Census Bureau classifies households into different categories, including total households, family households, and non-family households. Our analysis of U.S. Census Bureau American Community Survey data for Fence, Wisconsin reveals how household income distribution varies among these categories. The dataset highlights the variation in number of households with income, offering valuable insights into the distribution of Fence town households based on income levels.

Key observations

• For Family Households: In Fence town, the majority of family households, representing 30.86%, earn $45,000 to $49,999, showcasing a substantial share of the community families falling within this income bracket. Conversely, the minority of family households, comprising 0.0%, have incomes falling $10,000 to $14,999, representing a smaller but still significant segment of the community.
• For Non-Family Households: In Fence town, the majority of non-family households, accounting for 36.36%, have income $50,000 to $59,999, indicating that a substantial portion of non-family households falls within this income bracket. On the other hand, the minority of non-family households, comprising 0.0%, earn $10,000 to $14,999, representing a smaller, yet notable, portion of non-family households in the community.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Income Level: The income level represents the income brackets ranging from Less than $10,000 to $200,000 or more in Fence, Wisconsin (As mentioned above).
• All Households: Count of households for the specified income level
• % All Households: Percentage of households at the specified income level relative to the total households in Fence, Wisconsin
• Family Households: Count of family households for the specified income level
• % Family Households: Percentage of family households at the specified income level relative to the total family households in Fence, Wisconsin
• Non-Family Households: Count of non-family households for the specified income level
• % Non-Family Households: Percentage of non-family households at the specified income level relative to the total non-family households in Fence, Wisconsin

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/.

Recommended for further research

This dataset is a part of the main dataset for Fence town median household income. You can refer the same here

The human footprint is rapidly expanding, and wildlife habitat is continuously being converted to human residential properties. Surviving wildlife that reside in developing areas are displaced to nearby undeveloped areas. However, some animals can co-exist with humans and acquire the necessary resources (food, water, shelter) within the human environment. This may be particularly true when development is low intensity, as in residential suburban yards. Yards are individually managed “greenspaces” that can provide a range of food (e.g., bird feeders, compost, gardens), water (bird baths and garden ponds), and shelter resources (e.g., brush-piles, outbuildings) and are surrounded by varying landscape cover. To evaluate which residential landscape and yard features influence the richness and diversity of mammalian herbivores and mesopredators; we deployed wildlife game cameras in 46 residential yards in summer 2021 and 96 yards in summer 2022. We found that mesopredator diversity had a negative relationship with fences and was positively influenced by the number of bird feeders present in a yard. Mesopredator richness increased with the amount of forest within 400m of the camera. Herbivore diversity and richness were positively correlated to the area of forest within 400m surrounding yard and by garden area within yards, respectively. Our results suggest that while landscape does play a role in the presence of wildlife in a residential area, homeowners also have agency over the richness and diversity of mammals occurring in their yards based on the features they create or maintain on their properties. Methods 2.1 Study Sites Our study took place from 4 April to 4 August 2021 and 2022 within an 80.5 km radius of downtown Fayetteville, Arkansas USA. Northwest Arkansas is a rapidly developing area with a current population of approximately 349,000 people. Fayetteville is located in the Ozark Highlands ecoregion and the landscape is primarily forested by mixed hardwood trees with open areas used for cattle pastures and some scattered agriculture. Our study took place in residential yards ranging from downtown Fayetteville to yards situated in more rural areas. We solicited volunteers from the Arkansas Master Naturalist Program and the University of Arkansas Department of Biological Sciences who allowed us to place cameras in their yards. We attempted to choose yards that represented the continuum of urban to rural settings and provided a range of yard features to which wildlife was likely to respond to. 2.2 Camera Setup To document the presence of wildlife in residential yards, we deployed motion-triggered wildlife cameras (Browning StrikeForce or Spypoint ForceDark) in numerous residential yards (46 yards in 2021 and 96 yards in 2022). We placed cameras approximately 0.95 m above the ground on either a tripod or a tree and at least 5 m from houses and at most 100 m from houses. When possible, we positioned cameras near features such as compost piles, water sources (natural or man-made), and fence lines to maximize detections of wildlife. We coordinated with homeowners to choose locations that would not interfere with yard maintenance or compromise homeowner privacy. We placed cameras in both front and back yards, although most cameras were placed in backyards. Backyards often had more features predicted to be of interest to wildlife (Belaire et al., 2015) and cameras placed in backyards were less likely to have false triggers associated with vehicular traffic or be vulnerable to theft. When necessary, we removed small amounts of vegetation that may have blocked the view of the camera or triggered the camera although this was always done on such a small scale as not alter the environment but just to clear the view of the camera. We set cameras to trigger with motion and take bursts of 3 photographs per trigger with a 5 s reset time. We did not use any bait or lures. We checked and downloaded cameras every 2 weeks to check batteries and download data. We moved cameras around the same yard upwards of 3 times within the season to ensure we captured the full range of wildlife present in each yard. At each yard, we recorded eight variables associated with food, water, or shelter features in the yard area surrounding the camera, these variables were recorded in both front and backyard (Table 1). First, we recorded the area of maintained gardens occurring in each yard. Next, we recorded the volume of potential den sites available in each yard. Potential denning sites included the total available area under sheds and outbuildings as well as decking that was less than 0.3 meters off the ground and provided opportunities for wildlife to burrow beneath and be sheltered. Similarly, we also measured the volume of all brush and firewood piles present in each yard that could be used by smaller wildlife species for shelter or foraging. We counted the number of bird feeders in each yard that were regularly maintained during the study period. We also counted the number of water sources available including bird baths and garden ponds (any human subsidized water source on the ground usually within a lined basin or container). We distinguished between these types of water sources in analyses because bird baths were likely not available to all wildlife because of their height. We also categorized the presence and type of natural water source present in each yard including vernal streams, permanent streams or ponds, rivers, or lakes. We also recorded the presence of agricultural animals (such as chickens or ducks) and pets (type and indoor/outdoor) present in each yard (although we ultimately excluded the presence of pets from analyses – see below). We documented whether the part of the yard where each camera was deployed was surrounded by a fence and if so, we categorized the fence type based upon its permeability to wildlife. We categorized fences into one of four categories ranging from those that posed little barrier to wildlife movement to those that were impassable to most species. For example, fences in our first category presented relatively little resistance to wildlife movement (i.e., barbed wire). A second category of fence consisted of fences made of semi-spaced wood slats or beams that offered enough room for most animals to squeeze through but that may have prevented passage of the largest bodied of the species. Fences that were about at least 1 m in height, but were closed off on the bottom (i.e., privacy or chain-link), meaning that few wildlife would be able to pass through without climbing or jumping over were placed in a third category. Finally, the fourth category of fences were those that were 1.8 m or greater in height and were made from a solid material that would prevent all wildlife except capable climbers from entering. 2.3 Landscape Variables We used a GIS (ArcGIS Pro 10.2; ESRI, Inc. Redlands Inc) to plot the location of all cameras and to quantify the composition of the surrounding landscape. We first created 400m buffers around each camera, to encompass the average home range area of most wildlife species likely to occur in suburban yards (e.g., Trent and Rongtad, 1974; Hoffman and Gotschang, 1977; Atkins and Stott, 1998). Within each buffer, we calculated the amount of forest cover, developed open land (e.g., cemeteries, parks, and grass lawns), agriculture, and development using the 2016 National Land Cover Database (Dewitz 2019). We also quantified the maximum housing unit density (HUD) around each camera using the SILVIS Housing Data Layer (Hammer et al., 2004). Finally, we calculated the straight-line distance from each camera to the nearest downtown city center (Fayetteville, Rogers, Bentonville, or Eureka Springs). Distance to downtown is an additional index of urbanization and human activity that has been correlated with animal behavior in this area (DeGregorio et al. 2021). Table 1 Description of all variables predicted to affect diversity and richness of mammals in residential yards within 80km of downtown Fayetteville, Arkansas USA during the April- August of 2021 and 2022.

Landscape Variables

Variable Statistics

Range

Average ( 1 SD)

Forest Cover

Area of forest cover within 400m buffer

0-0.45

0.18 0.13

Open Land

Area of open land, (parks, cemeteries, and lawns) within 400m buffer

0.003-0.31

0.09 0.06

Agricultural Land

Area of land used for agricultural purposes within 400m buffer

0-0.43

0.08 0.11

Developed Land

Area of developed land within 400m buffer

0-0.47

0.13

Housing Unit Density (HUD)

Maximum Housing Unit Density within 400m buffer of camera (houses/ )

1-5095

657

Yard Variables

Volume of Denning Sites

Volume under sheds/outbuildings and under decks less than 1m off the ground ( )

0-700

27.3

Volume of Brush/Firewood Piles

Total volume of denning sites including brush and firewood piles ( )

0-335.94

42.99 69.16

Water Source

Number of human-maintained water sources

0-7

1

• Bird Bath

Water source that is raised off the ground, so much so that animals that cannot climb or jump cannot access it

0-7

• Garden Pond

Water source on or embedded within the ground

0-3

0 1

Bird Feeder

Number of bird feeders present in yard

0-19

4

Garden

Area of total maintained gardens ( )

0-525

46.13 85.13

Compost Pile

Area of compost pile

0-12

0.64

Fence Type

If a camera was within a fence, it was given a score between 1-4, 1 being the most permeable fence and 4 being the most impassable to terrestrial mammals. 0: not in a fence 1: Barbed wire 2: Open slat fence 3: 1.2 m Chain-link or Privacy 4: 1.8 m chain-link or Privacy

NA

NA

Poultry Presence

Presence or absence of poultry being kept in yard

NA

NA

Water

Score of presence or absence of natural water source. 0: No natural water source 1: Vernal

This data shows anthropogenic polyline disturbance features. Features were digitized using high resolution satellite imagery and orthophotos. Features from the National Road Network (NRN) and the National Railway Network (NRWN) were adapted and included. The following data was not included in the dataset: proposed features. Table 1. A list of attributes, associated domains, and descriptions. Attribute Data Type Domains Description REF_ID Text (20) Unique feature reference ID DATABASE Text (20) Historic, Most Recent, Retired Sub-database to which the feature belongs TYPE_INDUSTRY Text (50) Table 2.3.2 Major classification of disturbance feature by industry TYPE_DISTURBANCE Text (50) Table 2.3.2 Sub classification of disturbance feature WIDTH_M Double Width of feature in meters WIDTH_CLASS Text (5) HIGH, MED, LOW Width of feature by classification SCALE_CAPTURED Long Scale at which the feature was digitized DATA_SOURCE Text (10) Imagery, GPS, Other Data source: digitized from imagery, captured by GPS, or obtained by other means IMAGE_NAME Text (100) Filename of source imagery IMAGE_DATE Date Date that imagery was captured (YYYYMMDD) IMAGE_RESOLUTION Double Resolution of source imagery in meters IMAGE_SENSOR Text (35) Name of sensor that captured source imagery WIDTH_M: Linear features must be attributed with a width measurement. The width of the feature can be estimated in meters, rounded to the nearest whole number. **WIDTH_CLASS: This field employs a classification scheme used by previous contractors. This classification scheme was discussed and agreed upon by Mammoth Mapping and the Project Manager in 2011-2013. The width values are the following. Table 2. Width classification breakdown. WIDTH_CLASS Anticipated Value Range (meters) LOW 8 Table 3. A list of disturbance feature types and their descriptions. TYPE_INDUSTRY TYPE_DISTURBANCE DESCRIPTION Mining Survey / Cutline A linear cleared area through undeveloped land, used for line-of-sight surveying; impossible to distinguish whether associated with quartz or placer mining (overlapping or unclear claims information) Survey / Cutline - Placer A linear cleared area through undeveloped land, used for line-of-sight surveying; associated with placer mining (identified using claims information and/or other indicators) Survey / Cutline - Quartz A linear cleared area through undeveloped land, used for line-of-sight surveying; associated with quartz mining (identified using claims information and/or other indicators) Trench A long, narrow excavation dug to expose vein or ore structure Unknown Unknown linear mining disturbance Oil and Gas Pipeline Visible pipeline or pipeline Right-of-Way (above- or below-ground) Seismic Line Seismic lines Rural Driveway A driveway in a rural area Fence A fence in a rural area Transportation Access Assumed A linear feature that is assumed to be an access road, but could also be a trail Access Road A road or narrow passage whose primary function is to provide access for resource extraction (i.e. mining, forestry) and may also have served in providing public access to the backcountry. Arterial Road A major thoroughfare with medium to large traffic capacity Local Road A low-speed thoroughfare, provides access to front of properties, including those with potential public restrictions such as trailer parks, First Nations land, private estate, seasonal residences, gravel pits (NRN definition for Local Street/Local Strata/Local Unknown). Shows signs of regular use. Right of Way For Road Rights as attributed in the land parcels ancillary data Trail Path or track (typically 1.5 m wide) that does not necessarily access remote resources Unknown Right of Way A right of way with unknown industry type Survey / Cutline A linear cleared area through undeveloped land, used for line-of-sight surveying. A cutline may not always be associated with mineral exploration, therefore, Type: Unknown was used to differentiate all cutlines that were outside of mineral exploration. Unknown Unclassified, or unable to identify type based on imagery, but suspected to be anthropogenic Utility Electric Utility Corridor Corridor usually running parallel to highway, where transmission lines or other utilities are visible Unknown Unknown linear feature assumed to be a utility corridor; ancillary data is unclear. Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca