Normalized raster output of Population Density (people per sq km of land area) by block group in the State of Iowa based on U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates. Used in the Transit Dependency Analysis as part of the 2020 Iowa DOT Public Transit Long Range Plan update. This factor was one of seven utilized in the analysis that was based on MTI Report 12-30 "Investigating the Determining Factors for Transit Travel Demand by Bus Mode in US Metropolitan Statistical Areas" by the Mineta Transportation Institute of San José State University (SJSU) in May 2015. https://transweb.sjsu.edu/research/investigating-determining-factors-transit-travel-demand-bus-mode-us-metropolitan

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 feature class contains the current urban area boundaries for the State of Iowa. An urban area will comprise a densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,500 people, at least 1,500 of which reside outside institutional group quarters.

description: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Iowa. 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 Iowa. 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 Iowa. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7CR5RCJ; abstract: This shapefile contains landscape factors representing human disturbances summarized to local and network catchments of river reaches for the state of Iowa. 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 Iowa. 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 Iowa. National Fish Habitat Partnership Data System. http://dx.doi.org/doi:10.5066/F7CR5RCJ

Composite of transit ridership dependency factor median values across all transit agency sizes, generated using the 'Weighted Overlay' geoprocessing tool using a '1 to 10 by 1' evaluation scale based on percentage values provided by participating transit agencies in the State of Iowa. Weighted scores from four large urban transit agencies (Des Moines Area Regional Transit, Iowa City Transit, University of Iowa CamBus, Sioux City Transit, Bettendorf Transit), three small urban transit agencies (Mason City Transit, Marshalltown Transit, Muscatine Transit), and seven regional transit agencies (Southern Iowa Trolley, ECICOG, INRCOG, SWIPCO, SEIRPC, River Bend Transit, CIMPCO) where utilized in the generation of this overlay. Each agency provided a percentage weight for each of the seven factors (gas prices, household income, carless households, language, race, college enrollment, population density) as it pertains to how much each factor impacts transit dependency.

ABSTRACT Chrysodeixis includens is an important pest of soybean crop who has gained more visibility in the Brazilian Cerrado due to damage caused in this region. Foliar consumption, feeding period and mortality level of soybean loopers in laboratory, as well as their control in the field conditions, were evaluated after application of the ChinNPV virus in soybean plants. In the laboratory, were tested six concentrations of isolate Chin-IA (I-A) (1 × 1011, 2 × 1011, 4 × 1011, 6 × 1011, 8 × 1011 and 10 × 1011 PIB ha-1), one dose of methomyl chemical insecticide (172 g ai ha-1) and distilled water (control). The field experiment was carried out in the 2016/2017 season using the same cultivar and laboratory treatments, except for the lowest virus concentration. The population density of small and large larvae was evaluated before and at 5, 8 and 12 days after application (DAA) of the treatments in soybean plants. All concentrations of the isolate Chin-IA (I-A) have reduced the soybean loopers consumption and their feeding period, showing 100% of mortality after 3 – 4 days without differing from treatment with the chemical insecticide. After eight DAA of virus in the field, the population density of small and large larvae was reduced, providing satisfactory levels of control. These results showed the evident potential of ChinNPV in the reduction of defoliation power and maintenance the soybean loopers population under of control level, and thus may be used as complementary method in the integrated management of this pest in soybean crops.

This MSOA atlas provides a summary of demographic and related data for each Middle Super Output Area in Greater London. The average population of an MSOA in London in 2010 was 8,346, compared with 1,722 for an LSOA and 13,078 for a ward.

The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, births, deaths, health, housing, crime, commercial property/floorspace, income, poverty, benefits, land use, environment, deprivation, schools, and employment.

If you need to find an MSOA and you know the postcode of the area, the ONS NESS search page has a tool for this.

The MSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5).

CURRENT MSOA BOUNDARIES (2011)

PREVIOUS MSOA BOUNDARIES (2001)

NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard.

Tips:

1. - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data.

2. - To view data just for one borough*, use the filter tool.

3. - The legend settings can be altered by clicking on the pencil icon next to the MSOA tick box within the map legend.

4. - The areas can be ranked in order by clicking at the top of the indicator column of the data table.

Themes included here are Census 2011 Population, Mid-year Estimates, Population by Broad Age, Households, Household composition, Ethnic Group, Country of Birth, Language, Religion, Tenure, Dwelling type, Land Area, Population Density, Births, General Fertility Rate, Deaths, Standardised Mortality Ratio (SMR), Population Turnover Rates (per 1000), Crime (numbers), Crime (rates), House Prices, Commercial property (number), Rateable Value (£ per m2), Floorspace; ('000s m2), Household Income, Household Poverty, County Court Judgements (2005), Qualifications, Economic Activity, Employees, Employment, Claimant Count, Pupil Absence, Early Years Foundation Stage, Key Stage 1, GCSE and Equivalent, Health, Air Emissions, Car or Van availability, Income Deprivation, Central Heating, Incidence of Cancer, Life Expectancy, and Road Casualties.

• The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster.

These profiles were created using the most up to date information available at the time of collection (Spring 2014).

You may also be interested in LSOA Atlas and Ward Atlas.

The LSOA atlas provides a summary of demographic and related data for each Lower Super Output Area in Greater London. The average population of an LSOA in London in 2010 was 1,722 compared with 8,346 for an MSOA and 13,078 for a ward.

The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, diversity, households, health, housing, crime, benefits, land use, deprivation, schools, and employment.

Due to significant population change in some areas, not all 2011 LSOA boundaries are the same as previous LSOA boundaries that had been used from 2001. A lot of data is still only available using the 2001 boundaries therefore two Atlases have been created - one using the current LSOA boundaries (2011) and one using the previous boundaries (2001).

If you need to find an LSOA and you know the postcode of the area, the ONS NESS search page has a tool for this.

The LSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5).

CURRENT LSOA BOUNDARIES (2011)

NOTE: There is comparatively less data for the new boundaries compared with the old boundaries

PREVIOUS LSOA BOUNDARIES (2001)

For 2011 Census data used in the 2001 Boundaries Atlas: For simplicity, where two or more areas have been merged, the figures for these areas have been divided by the number of LSOAs that used to make that area up. Therefore, these data are not official ONS statisitcs, but presented here as indicative to display trends.

NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard.

IMPORTANT: Due to the large amount of data and areas, the LSOA Atlas may take up to a minute to fully load. Once loaded, the report will work more efficiently by using the filter tool and selecting one borough at a time. Displaying every LSOA in London will slow down the data reload.

Tips:

1. - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data.

2. - To view data just for one borough, use the filter tool.

3. - The legend settings can be altered by clicking on the pencil icon next to the LSOA tick box within the map legend.

4. - The areas can be ranked in order by clicking at the top of the indicator column of the data table.

Beware of large file size for 2001 Boundary Atlas (58MB) alternatively download Zip file (21MB).

Themes included in the atlases are Census 2011 population, Mid-year Estimates by age, Population Density, Households, Household Composition, Ethnic Group, Language, Religion, Country of Birth, Tenure, Number of dwellings, Vacant Dwellings, Dwellings by Council Tax Band, Crime (numbers), Crime (rates), Economic Activity, Qualifications, House Prices, Workplace employment numbers, Claimant Count, Employment and Support Allowance, Benefits claimants, State Pension, Pension Credit, Incapacity Benefit/ SDA, Disability Living Allowance, Income Support, Financial vulnerability, Health and Disability, Land use, Air Emissions, Energy consumption, Car or Van access, Accessibility by Public Transport/walk, Road Casualties, Child Benefit, Child Poverty, Lone Parent Families, Out-of-Work families, Fuel Poverty, Free School Meals, Pupil Absence, Early Years Foundation Stage, Key Stage 1, Key Stage 2, GCSE, Level 3 (e.g A/AS level), The Indices of Deprivation 2010, Economic Deprivation Index, and The IMD 2010 Underlying Indicators.

The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster.

These profiles were created using the most up to date information available at the time of collection (Spring 2014).

You may also be interested in MSOA Atlas and Ward Atlas.

The Marsh Ecology Research Program (MERP) was a long-term interdisciplinary study on the ecology of prairie wetlands. A scientific team from a variety of disciplines (hydrology, plant ecology, invertebrate ecology, vertebrate ecology, nutrient dynamics, marsh management) was assembled to design and oversee a long-term experiment on the effects of water-level manipulation on northern prairie wetlands. Ten years of fieldwork (1980 -1989), combining a routine long-term monitoring program and a series of short-term studies, generated a wealth of new and diverse information on the ecology and function of prairie wetlands (Murkin, Batt, Caldwell, Kadlec and van der Valk, 2000). This data set includes muskrat population data, collected as part of the vertebrate monitoring program of MERP. Re-colonizing muskrat (Ondatra zibethicus) populations in the MERP experimental cells were monitored during the 1985-1989 sampling seasons to explore the effects water level and associated vegetation characteristics had on muskrat density, population size, habitat use, body condition, and survival and reproductive rates (Clark and Murkin, 1989).

For further information on the Marsh Ecology Research Program (MERP), please visit: http://www.ducks.ca/conserve/research/projects/merp/index.html

References: Clark, W.R., and H.R. Murkin. 1989. Vertebrates. In: Marsh Ecology Research Program: Long-term Monitoring Procedures Manual. (Eds.) E.J. Murkin and H.R. Murkin, pp. 35-38. Manitoba, Canada: Delta Waterfowl and Wetlands Research Station. Murkin, H.R., B.D.J. Batt, P.J. Caldwell, J.A. Kadlec and A.G. van der Valk. 2000a. Introduction to the Marsh Ecology Research Program. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds) H.R. Murkin, A.G. van der Valk and W.R. Clark. pp. 3-15. Ames: Iowa State University Press.

Resulting Publications on Muskrat Populations Clark., W.R. 1990. Compensation in furbearer populations; current data compared with a review of concepts. Transactions of the North American Wildlife and Natural resources Conference 55: 491-500. Clark, W.R. 1994. Habitat selection by muskrats in experimental marshes undergoing succession. Canadian Journal of Zoology 72: 675-680. Clark, W.R., and D.W. Kroeker. 1993. Population dynamics of muskrats in managed marshes at Delta, Manitoba. Canadian Journal of Zoology 71: 1620-1628. Clark, W.R. 2000. Ecology of muskrats in prairie wetlands. In Prairie Wetland Ecology: The Contribution of the Marsh Ecology Research Program. (Eds.) H.R. Murkin, A.G. van der Valk, and W.R. Clark, pp. 37-54. Iowa: Iowa State University Press.

The 2019 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.