60,07 (persons per sq. km) in 2022.

The High-quality Stream Watersheds dataset was created to define areas within the New Hampshire Coastal Watersheds with potentially high water-quality streams. Watershed boundaries are based on the USGS SPARROW water quality model. The definition for each tier is as listed below: Tier 1: Population Density <20 persons per sq mile, <1% of the area is developed, and <5% of the landuse is agriculture.Tier 2: Population Density <36 persons per sq miles, <2% of the area is developed, and < 5% of the landuse is agriculture. Tier 3: Population Density is <64 persons per sq mile, <3% of the area is developed, and <5% of the landuse is agriculture. Tier 4: Population Density is <90 persons per sq mile, <3% of the area is developed, and < 5% of the landuse is agriculture. A complete description of the dataset may be found in The Land Conservation Plan for New Hampshire's Coastal Watersheds (section III) or in the excerpt "Coastal_Plan-App_D-2-Freshwater.pdf".

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of New Hampshire. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of New Hampshire. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for New Hampshire. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7K935JC; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of New Hampshire. This dataset is the result of clipping the feature class 'NFHAP 2010 HCI Scores and Human Disturbance Data for the Conterminous United States linked to NHDPLUSV1.gdb' to the state boundary of New Hampshire. Landscape factors include land uses, population density, roads, dams, mines, and point-source pollution sites. The source datasets that were compiled and attributed to catchments were identified as being: (1) meaningful for assessing fish habitat; (2) consistent across the entire study area in the way that they were assembled; (3) representative of conditions in the past 10 years, and (4) of sufficient spatial resolution that they could be used to make valid comparisons among local catchment units. In this data set, these variables are linked to the catchments of the National Hydrography Dataset Plus Version 1 (NHDPlusV1) using the COMID identifier. They can also be linked to the reaches of the NHDPlusV1 using the COMID identifier. Catchment attributes are available for both local catchments (defined as the land area draining directly to a reach; attributes begin with "L_" prefix) and network catchments (defined by all upstream contributing catchments to the reach's outlet, including the reach's own local catchment; attributes begin with "N_" prefix). This shapefile also includes habitat condition scores created based on responsiveness of biological metrics to anthropogenic landscape disturbances throughout ecoregions. Separate scores were created by considering disturbances within local catchments, network catchments, and a cumulative score that accounted for the most limiting disturbance operating on a given biological metric in either local or network catchments. This assessment only scored reaches representing streams and rivers (see the process section for more details). Please use the following citation: Esselman, P., D.M. Infante, L. Wang, W. Taylor, W. Daniel, R. Tingley, J. Fenner, A. Cooper, D. Wieferich, D. Thornbrugh and J. Ross. (April 2011) National Fish Habitat Action Plan (NFHAP) 2010 HCI Scores and Human Disturbance Data (linked to NHDPLUSV1) for New Hampshire. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7K935JC

The 2016 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files.

The records in this file allow users to map the parts of Urban Areas that overlap a particular county.

After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.

The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities.

The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

The 2015 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

When we were first informed about doing a project with ArcGIS, I immediately thought of a project about the Chinese Mystery Snail. I had become mildly interested in these snails in the summer of 2019 after an invasives program, and I thought that it would be a great topic to research and collect data on. I knew that they were an invasive in Squam Lake, and I had seen them in the Squam River, so I was interested in finding out how common they were throughout the Squam area. Procedure:The first step in my procedure was doing some research on the Chinese Mystery Snail in New Hampshire. I wanted to find out the basic details about this snail as well as where it has been found. To my surprise, there didn't seem to be too much research on the extent of the spread. Since I had seen the snails in the Squam River, I initially thought to do my data collection in Little Squam Lake. However, as I checked the shores of public access points around the lake, I could not find any evidence of these snails. This caused me to move my study to the Squam River where I knew I would find snails. I then decided what sort of information I wanted to collect along with tracking the number and location of snails. I knew that I wanted to collect the depth where they were found, and I also decided to track the type of substrate they were found in. I also thought that I might be able to collect water samples from each location to test what pH, temperature, and other qualities of the water the snails may prefer, but I did not have the materials available to do so. I created a survey that included the depth, substrate, if they were alive or dead, the location, and a place to take photos of my findings. As I started to record data, I realized that some of the features on my survey were not best for the data I was trying to collect. As a result, I had to type the number of snails I found in the comments section rather than in a specific spot. I also did not have enough variation in my type of substrate, so I had to type in the different substrates I found in the "other" category. I only had time to primarily record data for one side of the river, so there are probably other points along the river on the other side where the snails are located. Also, the weather and light conditions were not the best on the day where I collected data, so there could also be spots where I missed snails due to my lack of visibility in the area. I recorded the data by using the Survey123 app on my phone, imputing my data there along with taking the pictures with each point. When I was out recording data, I used a kayak to get around, going slowly along the banks of the river to see if I could see any snails. When I found a snail, I would stop my kayak, take a picture of the snail(s) that I found, and then have my mom, who was kayaking with me, measure the depth of the snails while I inputted the other data into the survey. After I collected all of my data, I uploaded it into a map on ArcGIS Online, creating all of the points where I had found snails. Since I was kayaking and therefore did not have any wifi or service, there were some points that I had to manually shift a small amount because the GPS had been a few feet off and placed the points on land instead of in the water. I then searched for layers that showed the outline of the river and included that in my map. I also wanted to show the bathymetry of the Squam River, but there were no layers where this was recorded in the river, only in the lakes. After adjusting my points and starting to make the map, I realized that there were too many categories for just one map. This caused me to create three maps in order to show the data I wanted to include in my project. I chose to show the population density, the depth, and the type of substrate the snails were found in. For the maps showing the depth and the type of substrate, I also included the population density in order to show which depths and substrates the snails tended to prefer. I then created my Story Map, putting all of the maps in, along with quickly creating a few extra maps to help visualize the information. I included all of the research I had previously done, and did a little more research in areas where I felt I needed a bit more information. I included photos from my day collecting data into the Story Map and formatted it in order to make the most sense with clear transitions between subjects.

description: Lake Umbagog s Common Loon population has been monitored for 24 years. In 1993, the level of resolution of reproductive performance was improved with the initiation of a program to uniquely color-mark individuals. Since 1976, the number of territorial pairs has increased from 9 to 28 and reflects a similar increase in loon populations across New Hampshire. However, long-term reproductive measures indicate two major concerns for loon conservation. First, the number of fledged young per territorial pair has significantly declined in the past four years (0.28) and is well below the 24- year mean for Lake Umbagog (0.46). Second, the discrepancy of reproductive measures between the north and south ends of the lake continue to widen. Observations of marked individuals indicate site fidelity (84.5%) and survivorship (93.5%) are above or near average levels of other breeding populations, thereby indicating density dependent factors are not the sole source of impacts for current loon demographics. Water level manipulation and methylmercury availability were investigated and found to be potential sources of impact on healthy breeding populations. Although mean mercury exposure to loons and their prey fish on Lake Umbagog are moderate compared to watershed-wide levels, certain areas of the lake such as the southern end and the Magalloway River carry Hg loads potentially damaging at the molecular, organism, and population levels. The changes in water levels have a dual effect on the viability of Lake Umbagog s loon population. Although positive steps have been taken to minimize waterlevel impacts on loon nesting success, 13% of their nests in the past seven years failed due to floodings and strandings. Changes in water levels, especially during the summer, are also known to exaggerate methylmercury production and create artificially high levels.; abstract: Lake Umbagog s Common Loon population has been monitored for 24 years. In 1993, the level of resolution of reproductive performance was improved with the initiation of a program to uniquely color-mark individuals. Since 1976, the number of territorial pairs has increased from 9 to 28 and reflects a similar increase in loon populations across New Hampshire. However, long-term reproductive measures indicate two major concerns for loon conservation. First, the number of fledged young per territorial pair has significantly declined in the past four years (0.28) and is well below the 24- year mean for Lake Umbagog (0.46). Second, the discrepancy of reproductive measures between the north and south ends of the lake continue to widen. Observations of marked individuals indicate site fidelity (84.5%) and survivorship (93.5%) are above or near average levels of other breeding populations, thereby indicating density dependent factors are not the sole source of impacts for current loon demographics. Water level manipulation and methylmercury availability were investigated and found to be potential sources of impact on healthy breeding populations. Although mean mercury exposure to loons and their prey fish on Lake Umbagog are moderate compared to watershed-wide levels, certain areas of the lake such as the southern end and the Magalloway River carry Hg loads potentially damaging at the molecular, organism, and population levels. The changes in water levels have a dual effect on the viability of Lake Umbagog s loon population. Although positive steps have been taken to minimize waterlevel impacts on loon nesting success, 13% of their nests in the past seven years failed due to floodings and strandings. Changes in water levels, especially during the summer, are also known to exaggerate methylmercury production and create artificially high levels.

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

The 2019 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the ""urban footprint."" There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.