Business Analyst Layer: Color-coded map '2023 Population Density'

Author: S Wicklund, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): population, mapsRegion: worldStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Use maps of population distribution to examine the history of world population growth.
2. Construct a dot map to show current world population distribution.
3. Describe the difference between arithmetic and physiological densities.
4. Craft a response to a prompt to evaluate the Negative Population Growth perspective. Summary: Students will use maps of population distribution to examine the history of world population growth. They will also examine current world population distribution. Students will role-play the difference between arithmetic and physiologic densities using Egypt as an example. They will then craft a response to a prompt where they evaluate the Negative Population Growth perspective.

Geospatial Analysis of Population Demographics and Traffic Density in MinneapolisIntroductionThis interactive web map provides a geospatial analysis of population distribution and traffic density for the city of Minneapolis, Minnesota. By integrating demographic data at the census tract level with real-time traffic information, the application serves as a critical tool for urban planning, transportation management, and sociological research.Data Visualization and SymbologyThe map employs distinct color schemes to represent the core datasets, allowing for intuitive visual analysis: Traffic Density: The city's road network is symbolized using a color gradient to indicate traffic volume. Segments rendered in deep red represent a high traffic density index, signifying areas of significant vehicular congestion. This transitions to a light yellow for segments experiencing lower traffic flow. Population Density: The demographic landscape is visualized using a green color ramp applied to census tract polygons. Dark green shades correspond to areas with a high population concentration, whereas lighter green shades denote regions with a lower population density. Analytical Utility and ApplicationsThe juxtaposition of these datasets reveals spatial correlations between residential density and transportation bottlenecks. This allows for data-driven inquiry into key urban challenges. The patterns visualized can help city planners and transportation authorities identify specific corridors where infrastructure investment could be most effective. Strategic improvements in these areas have the potential to optimize traffic flow, reduce commuter travel times, and decrease vehicle fuel consumption and emissions, thereby enhancing the overall sustainability and livability of Minneapolis.Interactive Features and Data ExplorationUsers are encouraged to engage with the map's interactive features for a deeper understanding of the data: Layers and Legend: Utilize the "Layers" and "Legend" tools to deconstruct the map's composition and understand the specific values associated with the color symbology. Pop-up Information: Click on individual census tracts or road segments to activate pop-up windows. These provide detailed attribute information, such as total population counts, demographic breakdowns, household income statistics, and spatial relationship metrics like nearest neighbor analysis. This application is built upon a foundational demographic data layer for Minneapolis and is enhanced by the integration of a dynamic traffic layer from the ArcGIS Living Atlas of the World.

The Census Bureau has completed the delineation of the Census 2000 urbanized areas (UA) and urban clusters (UC). The Census Bureau identifies and tabulates data for the urban and rural populations and their associated areas solely for the presentation and comparison of census statistical data. For Census 2000, the Census Bureau classifies as urban all territory, population, and housing units located within an urbanized area (UA) or an urban cluster (UC). It delineates UA and UC boundaries to encompass densely settled territory, which consists of:

- core census block groups or blocks that have a population density of at least 1,000 people per square mile and

- surrounding census blocks that have an overall density of at least 500 people per square mile

In addition, under certain conditions, less densely settled territory may be part of each UA or UC.

The Census Bureau's classification of rural consists of all territory, population, and housing units located outside of UAs and UCs.

For more information about the 2000 Urbanized Area please go to:
https://www.census.gov/geo/maps-data/maps/ua2kmaps.html

In 2008, the Quality Deer Management Association (QDMA) developed a map of white-tailed deer density with information obtained from state wildlife agencies. The map contains information from 2001 to 2005, with noticeable changes since the development of the first deer density map made by QDMA in 2001. The University of Minnesota, Forest Ecosystem Health Lab and the US Department of Agriculture, Forest Service-Northern Research Station have digitized the deer density map to provide information on the status and trends of forest health across the eastern United States. The QDMA spatial map depicting deer density (deer per square mile) was digitized across the eastern United States. Estimates of deer density were: White = rare, absent, or urban area with unknown population, Green = less than 15 deer per square mile, Yellow = 15 to 30 deer per square mile, Orange = 30 to 40 deer per square mile, or Red = greater than 45 deer per square mile. These categories represent coarse deer density levels as identified in the QDMA report in 2009 and should not be used to represent current or future deer densities across the study region. Sponsorship: Quality Deer Management Association; US Department of Agriculture, Forest Service-Northern Research Station; Minnesota Agricultural Experiment Station. Resources in this dataset:Resource Title: Link to DRUM catalog record. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/178246

Map of gray wolf and red wolf current and historic range and suitable habitat across the U.S. and Mexico. Produced by Defenders of Wildlife (2021). All data sources listed below:Gray Wolf:Historic Range: The historic range for the gray wolf was delineated with the help of peer reviewed sources: Rutledge et al. 2010. Genetic and morphometric analysis of sixteenth century Canis skull fragments: implications for historic eastern and gray wolf distribution in North America.Current Range: Range delineation was based on range data from IUCN and USFWS, expert knowledge, and personal communications from Defenders of Wildlife field teams, academia, and federal agencies. Details of delineations focused mostly on the United States and Mexico as ranges north of that couldn’t be confirmed due to controversies.Suitable Habitat:Bennett, L.E. 1994. Colorado Gray Wolf Recovery: A biological feasibility study. Final Report. U.S. Fish and Wildlife Service and University of Wyoming Fish and Wildlife Cooperative research unit, Laramie, Wyoming, USA. Available at: https://babel.hathitrust.org/cgi/pt?id=umn.31951p00672031a;view=1up;seq=146California Department of Fish and Wildlife. 2016b. Potential Suitable Habitat in California. Pages 153-160 in Conservation Plan for Gray Wolves in California Part 2. Carroll, C., Phillips, M.K., Lopez-Gonzalez, C.A., and Schumaker, N.H. 2006. Defining Recovery Goals and Strategies for Endangered Species: The Wolf as a Case Study. BioScience 56(1): 25–37, https://doi.org/10.1641/0006-3568(2006)056[0025:DRGASF]2.0.CO;2Carroll, C. 2003. Impacts of Landscape Change on Wolf Viability in the Northeastern U.S. and Southeastern Canada. Wildlands Project Special Paper No. 5, available at https://www.klamathconservation.org/docs/wolfviabilitypaper.pdf.Carroll, C. 2007. Application of habitat models to wolf recovery planning in Washington. Unpublished report.Defendersof Wildlife. 2006. Places for Wolves: A Blueprint for Restoration and Recovery in the Lower 48 StatesDefenders of Wildlife. 2013. Places for WolvesHarrison, D. J., and T. G. Chapin. 1998. An assessment of potential habitat for eastern timber wolves in the northeastern United States and connectivity with occupied habitat in southeastern Canada. Wildlife Conservation Society, Working Paper Number 7.Harrison, D. J., and T. G. Chapin. 1998. Extent and connectivity of habitat for wolves in eastern North America. Wildlife Society Bulletin 26: 767-775, available at https://wolfology1.tripod.com/id207.htmHearne D., Lewis K., Martin M., Mitton E., and Rocklen C. 2003. Assessing the Landscape: Toward a Viable Gray Wolf Population in Michigan and Wisconsin. Hendricks, S.A., Schweizer, R.M., Harrigan, R.J., Pollinger, J.P., Paquet, P.C., Darimont, C.T., Adams, J.R., Waits, L.P., vonHoldt, B.M., Hohenlohe1, P.A. and R.K. Wayne. 2018. Natural recolonization and admixture of wolves (Canis lupus) in the US Pacific Northwest: challenges for the protection and management of rare and endangered taxa. The Genetics Society. Heredity. https://doi.org/10.1038/s41437-018-0094-x.Jimenez, M.D. et al. 2017. Wolf Dispersal in the Rocky Mountains, Western United States: 1993–2008. The Journal of Wildlife Management 81(4):581–592.Larson, T. and W.J. Ripple. 2006. Modeling Gray Wolf (Canis lupus) habitat in the Pacific Northwest, U.S.A. Journal of Conservation Planning 2:17-33.Maletzke, B.T. and R.B. Wielgus. 2011. Development of wolf population models for RAMAS© analysis by the Washington Department of Fish and Wildlife.Martinez-Meyer E., Gonzalez-Bernal A., Velasco J.A., Swetnam T.L., Gonzalez-Saucedo Z.Y., Servin J., Lopez-Gonzalez C.A., Oakleaf, J.A., Liley S., and Heffelfinger J.R. 2020. Rangewide habitat suitability analysis for the Mexican wolf (Canis lupus baileyi) to identify recovery areas in its historical distribution. Diversity and Distributions 00:1-13.McNab, W.H., Cleland, D.T., Freeouf, J.A., Keys, Jr., J.E., Nowacki, G.J., Carpenter, C.A., comps. 2007. Description of ecological subregions: sections of the conterminous United States [CD-ROM]. Gen. Tech. Report WO-76B. Washington, DC: U.S. Department of Agriculture, Forest Service. 80 p.McNab, W.H. and P.E. Avers. 1995. Ecological subregions of the United States. Washington, DC: U.S. Department of Agriculture, Forest Service, available at https://www.fs.fed.us/land/pubs/ecoregions/.Mladenoff, D.J., Sickley, T.A., Haight, R.G. and Wydeven, A.P. 1995. A Regional Landscape Analysis and Prediction of Favorable Gray Wolf Habitat in the Northern Great Lakes RegionMladenoff, D.J. and T.A. Sickley. 1998. Assessing Potential Gray Wolf Restoration in the Northeastern United States: A Spatial Source. Journal of Wildlife Management 62(1): 1-10.Minnesota Dept. of Natural Resources. 2001. Minnesota Wolf Management Plan. Minnesota Dept. Natural Resources. 2017a. Gray Wolf, available at https://www.dnr.state.mn.us/mammals/wolves/mgmt.html.Montana Fish Wildlife & Parks. 2004. Montana Gray Wolf Conservation and Management Plan.Montana Fish,Wildlife & Parks. 2018. Montana Annual Report 2018: Wolf Conservation and Management.Oakleaf J.K., Murray D.L., Oakleaf J.R., Bangs E.E., Mack C.M., Smith D.W., Fontaine J.A., Jimenez M.D., Meier T.J., and C.C. Niemeyer. 2006. Habitat Selection by Recolonizing Wolves in the Northern Rocky Mountains of the United States. Journal of Wildlife Management 70(2):554-563.Oregon Department of Fish and Wildlife. 2015. Updated mapping potential gray wolf range in Oregon.Potvin M.J., Drummer T.D., Vucetich J.A., Beyer E. Jr., and J.H. Hammill. 2005. Monitoring and Habitat Analysis for Wolves in Upper Michigan. Journal of Wildlife Management 69(4):1660-1669.Treves A., Martin K.A., Wiedenhoeft J.E., Wydeven A.P. (2009) Dispersal of Gray Wolves in the Great Lakes Region. In: Wydeven A.P., Van Deelen T.R., Heske E.J. (eds) Recovery of Gray Wolves in the Great Lakes Region of the United States. Springer, New York, NY. https://doi.org/10.1007/978-0-387-85952-1_12USGS Gap Analysis Project Species Range and Predicted Habitat: Gray wolf: https://gapanalysis.usgs.gov/apps/species-data-download/Washington Dept. of Fish and Wildlife (WDFW). 2017. Washington Gray Wolf Conservation and Management 2017 Annual Report.Wiles, G. J., H. L. Allen, and G. E. Hayes. 2011. Wolf conservation and management plan for Washington. Washington Department of Fish and Wildlife, Olympia, Washington. 297 pp.Red Wolf:Historic Range:Red wolf historic range established by USFWS based on information provided by the 2016 Wildlife Management Institute report [ Wildlife Management Institute: A Review and Evaluation of the Red Wolf (Canis rufus) Historic Range, Final Report – 5/25/2016]. The historic range layer is a combination of the following Level II EPA Ecoregions: 1) Mississippi Alluvial and Southeast USA Coastal Plains, 2) Ozark/Ouachita-Appalachian Forests, 3) South Central Semi-Arid Prairies, 4) Southeastern USA Plains, and 5) Texas-Louisiana Coastal PlainsCurrent Range (Recovery Area):Red wolf recovery area adapted from the USFWS current range information.Suitable Habitat:Toivonen L.K. (2018) Assessing red wolf conservation based on analyses of habitat suitability and human perception of carnivores.Karlin M., Vaclavik T., Chadwick J., and R. Meentemeyer. (2016) Habitat use by adult red wolves, Canis rufus, in an agricultural landscape, North Carolina, USA. Mammal Study 41:87-95.