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# Calculate scale of a raster

A raster has a cell size of 200 meters. What is the corresponding scale of the raster?

New to GIS, using ESRI, semester is almost over but this one question is confusing. I didn't think you could calculate the scale of the raster in that you are given cell size of 200 meters and to my knowledge, or lack there of, the corresponding scale would be whatever the data set is using or that you adjust the scale to.

Rasters don't really have a scale, they have a resolution, which in your case is 200m. You can calculate an appropriate viewing scale for a given raster resolution based on the ability to distinguish features.

The ArcGIS Blog post On map scale and raster resolution discusses this:

In 1987, Waldo Tobler, renowned analytical cartographer (now emeritus from University of California-Santa Barbara) wrote, “The rule is: divide the denominator of the map scale by 1,000 to get the detectable size in meters. The resolution is one half of this amount.”

(snip)

So here is the appropriate resolution for a select set of map scales:

For example, if you were not sure what resolution imagery you needed to acquire to detect features at a map scale of 1:50,000, using Tobler's rule above, you can determine that imagery of approximately 25m [50000/ (1000*2)] resolution would be sufficient.

Similarly, if you need to find out the mapping scale from a known imagery resolution you can do so using the formula below:

Map Scale = Raster resolution (in meters) * 2 * 1000

Two, as used in the equation above, doubles the resolution. Then that number is multiplied by 1000 to get the map scale.

Here's an example. Say you have a raster with a resolution of 30 meters. Each pixel is 30 meters on a size (an area of 900 square meters). You double that to get four pixels (two rows and two columns) with a resolution of 60 meters on a size (an area of 3600 square meters). Then you multiply that 60 meter resolution by 1000 to get a map scale of 60,000.

(snip)

according to our good friend "Tobler," whom stated in order to calculate map scale, multiply the raster resolution (which should be in meters) by 2 multiplied by 1000. So in my above question my equation ended up like this here:

map scale = 200 meters x 2 x 1000

map scale = 1:400,000

there ya go

of course all great data sources deserve the appropriate credits so after juggling the reverse of another equation I sort of derived the answer I need! Thanks ESRI, in class and for lab instruction. Thanks Rajnagi!!

the reference I used was originally posted by rajnagi on December 12, 2010

"On map scale and raster resolution"

By Rajinder Nagi, Esri Cartographic Product Engineer

http://blogs.esri.com/esri/arcgis/2010/12/12/on-map-scale-and-raster-resolution/

• Accepts and directly processes continuous input values without requiring the values to be grouped into categories.
• Allows for linear and nonlinear continuous functions to be applied to the input data.
• Rescales the input values onto a continuous floating-point evaluation scale.

Reviewing the essential vocabulary for this tool may help with understanding the following explanations.

1. Apply the specified function to the input raster values.
2. Linearly transform the function values to a specified evaluation scale. In an ascending evaluation scale, the minimum and maximum function values are set to the specified minimum ( From scale ) and maximum ( To scale ) of the evaluation scale, respectively. However, the evaluation scale can also be reversed for a descending scale.

The following illustration shows an example of a Power function graph to introduce the general concepts and terminology associated with applying a transformation function.

An example plot of the Power function, with a value of 2 for the exponent and an evaluation scale of 1 to 10

The input data range for this example is from 3,000 to 5,000. The lowest value in the input raster is set to the Lower threshold value and the highest to the Upper threshold (seen on the x-axis), with the Power function being constrained (fit) between the thresholds. Shape-controlling parameters define the shape of the function--such as Input shift and Exponent for the Power function--allowing you to control where the function begins and how steeply it will rise. The resulting function values from the Power function are then linearly transformed to the evaluation scale to assign the output values. In the graph above, a 1 to 10 evaluation scale has been defined using the From scale and To scale parameters, as can be seen on the y-axis.

By default, the minimum value from the Input raster is assigned to the Lower threshold value and the maximum value to the Upper threshold value.

• Linear and Symmetrical Linear functions, by definition, are fitted since the minimum and maximum of the functions are set to the minimum (the lower threshold) and maximum (the upper threshold) of the Input raster .
• A fitted version of the Exponential and Logarithm functions is applied to the values from the Input raster .
• As many of the parameters as possible are derived from the Input raster (for example, the Midpoint , Factor , and Input shift ) to obtain the best fit for Gaussian, Near, Small, MS Small, Large, MS Large, Power, Logistic Growth, and Logistic Decay functions.
1. If an input cell has a value lower than the Lower threshold , it will be assigned to the value set in the Value below threshold parameter.
2. If an input cell has a value greater than the Upper threshold , it will be assigned to the value set in the Value above threshold parameter.
3. All cell values, including and between the Lower threshold and Upper threshold , will be assigned to the corresponding evaluation scale based on the function value, f(x). In certain cases, when a shape-controlling parameter (for example, Spread and Exponent ) is altered, the output raster may not have any cells assigned to the From scale or To scale values.

By default, the parameters defining the shape of the function (for example, Spread or Base factor ) will be calculated to best fit (constrain) the function to the minimum and maximum of the Input raster . However, the shape-controlling parameters can be altered to refine the fit of the function to the preference interaction of the phenomenon to the criterion values. When a value is specified for any shape-controlling parameter, the resulting function, in conjunction with the lower and upper threshold values, will be used in rescaling the Input raster values the fitted version on the function will not be used.

The From scale and To scale define the upper and lower values of the continuous evaluation scale. The smallest function value is assigned to the value set for From scale and the largest function value is assigned to the value set for the To scale . All function values in between are assigned to the appropriate evaluation values.

The evaluation scale can be set to range from low to high (for example, 1 to 10) or from high to low (for example, 10 to 1).

The Value below threshold and Value above threshold values are assigned to all cells that have an input value below and above the thresholds, respectively. These values are assigned directly to the final output raster, and these cells are not considered in the processing of the transformation function.

In the tool dialog box, generally when the Lower threshold or Upper threshold is altered, the shape-controlling parameters—parameters defining the shape of the function, for example, Spread or Base factor —are automatically recalculated. However, when a shape-controlling parameter is altered, the Lower threshold and Upper threshold values will not change automatically, and if the Lower threshold or Upper threshold is subsequently changed, the altered shape-controlling parameter (and any other associated shape-controlling parameters) will retain the changed setting and it will not be recalculated.

The shape-controlling parameters for the function (for example, Spread or Base factor ) and the lower and upper thresholds are based on statistics calculated for the current processing extent, cell size, and snap raster environment settings. If none of these are set, the statistics associated with the full extent of the input raster are used.

• The environment processing extent, cell size, or snap raster is set prior to launching the tool dialog box.
• The input raster does not have statistics.

In the tool dialog box, if the environment processing extent, cell size, or snap raster is changed after an input raster is entered and a function has been specified, the function parameters may be set to empty (no value in the parameter). Click the Calculate Stats button to repopulate the parameters to view the values for the new extent. If the Lower threshold , Upper threshold , or any shape-controlling parameters are changed by typing in a value, the tool will track that these parameters have been altered. If the processing extent is changed, the values for these parameters will remain as specified after clicking the Calculate Stats button.

1. Run Rescale by Function applying a linear function to the input values from 1,500 to 3,200 and set the values above and below the thresholds to 0.
2. Run the tool a second time on the same input raster, this time, applying an Exponential function to the values greater than 3,200 and up to 5,000, and set the values above and below the thresholds to 0.
3. Add the two resulting output rasters together with the Plus tool.

See Analysis environments and Spatial Analyst for additional details on the geoprocessing environments that apply to this tool.

The dataset faithfuld already have a column for density which is the estimates of the 2D density for waiting and eruptions. You can find that the eruptions and waiting in the dataset are points in a grid. When you use geom_raster , it doesn't compute the density for you. Instead, it plots the density according to the x, y coordinates, in this case, is the grid. Hence, if you just apply the log transformation on y, it will distort the difference between y (originally they are equally spaced) and this is why you see the space in your plot. I used points to visualize the effects:

If you really want to transform y to log scale and produce the density plot, you have to use the faithful dataset with geom_density_2d .

Update: Use geom_rect and supply custom xmin, xmax, ymin, ymax values to fit the spaces of the log scale.

Since the geom_raster use the same size of tiles, you probably have to use geom_tile or geom_rect to create the plot. My idea is to calculate how large (width) each tile should be and adjust the xmin and xmax for each tile to fill up the gap.

## GIS: Geographical Information Systems - Introduction

The aim of the course is to give basic theoretical and practical knowledge about concepts and methods for treatment and analysis of geographic data with geographic Information systems, GIS. It also provides and an introduction to cartography and geodesy. The course highlights general geographic problems within environment and society through practical GIS applications, Swedish and international conditions and vary in scale from local to regional.

The course “Geographical Information System, introduction” gives you basic understanding for representation and analysis of spatial elements. The course highlights general geographical problems in the society and environment and how they can be handled using GIS. You learn basic cartography including projections and geographical reference systems, digital geographical data (maps, images and tables), basic analysis of data in vector and raster format, and presentation of geographical data in map format using applications at regional and local scales. Databases and attribute data handling are also included in the course. After finishing the course you will have a solid basic knowledge on how to work with digital geographical data and GIS.

## 5. Raster Scanning

Remote sensing systems commonly work in much the same way as the digital scanner you may have attached to your personal computer. Scanners like the one pictured below create a digital image of an object by recording, pixel by pixel, the intensity of light reflected from the object. The component that measures reflectance is called the scan head, which consists of a row of tiny sensors that convert light to electrical charges. Color scanners may have three light sources and three sets of sensors, one each for the blue, green, and red wavelengths of visible light. When you push a button to scan a document, the scan head is propelled rapidly across the image, one small step at a time, recording new rows of electrical signals as it goes. Remotely sensed data, like the images produced by your desktop scanner, consist of reflectance values arrayed in rows and columns that make up raster grids.

After the scan head converts reflectances to electrical signals, another component, called the analog-to-digital converter, converts the electrical charges into digital values. Although reflectances may vary from 0 percent to 100 percent, digital values typically range from 0 to 255. This is because digital values are stored as units of memory called bits. One bit represents a single binary integer, 1 or 0. The more bits of data that are stored for each pixel, the more precisely reflectances can be represented in a scanned image. The number of bits stored for each pixel is called the bit depth of an image. An 8-bit image is able to represent 2 8 (256) unique reflectance values. A color desktop scanner may produce 24-bit images in which 8 bits of data are stored for each of the blue, green, and red wavelengths of visible light.

As you might imagine, scanning the surface of the Earth is considerably more complicated than scanning a paper document with a desktop scanner. Unlike the document, the Earth's surface is too large to be scanned all at once, and so must be scanned piece by piece, and mosaicked together later. Documents are flat, but the Earth's shape is curved and complex. Documents lie still while they are being scanned, but the Earth rotates continuously around its axis at a rate of over 1,600 kilometers per hour. In the desktop scanner, the scan head and the document are separated only by a plate of glass satellite-based sensing systems may be hundreds or thousands of kilometers distant from their targets, separated by an atmosphere that is nowhere near as transparent as glass. And while a document in a desktop scanner is illuminated uniformly and consistently, the amount of solar energy reflected or emitted from the Earth's surface varies with latitude, the time of year, and even the time of day. All of these complexities combine to yield data with geometric and radiometric distortions that must be corrected before the data are used for analysis. Later in this chapter, we'll discuss some of the image processing techniques that are used to correct remotely sensed image data.

## Zonal Operations

A zonal operation is employed on groups of cells of similar value or like features, not surprisingly called zones (e.g., land parcels, political/municipal units, waterbodies, soil/vegetation types). These zones could be conceptualized as raster versions of polygons. Zonal rasters are often created by reclassifying an input raster into just a few categories (see Section 8.2.2 "Neighborhood Operations"). Zonal operations may be applied to a single raster or two overlaying rasters. Given a single input raster, zonal operations measure the geometry of each zone in the raster, such as area, perimeter, thickness, and centroid. Given two rasters in a zonal operation, one input raster and one zonal raster, a zonal operation produces an output raster, which summarizes the cell values in the input raster for each zone in the zonal raster (Figure 8.7 "Zonal Operation on a Raster Dataset").

Figure 8.7 Zonal Operation on a Raster Dataset

Zonal operations and analyses are valuable in fields of study such as landscape ecology where the geometry and spatial arrangement of habitat patches can significantly affect the type and number of species that can reside in them. Similarly, zonal analyses can effectively quantify the narrow habitat corridors that are important for regional movement of flightless, migratory animal species moving through otherwise densely urbanized areas.

## About Raster Images in Drawings

By:

Raster images consist of a rectangular grid of small squares or dots known as pixels. For example, a photograph of a house is made up of a series of pixels colorized to represent the appearance of a house. A raster image references the pixels in a specific grid.

Raster images, like many other drawing objects, can be copied, moved, or clipped. You can modify an image with grip modes, adjust an image for contrast, clip the image with a rectangle or polygon, or use an image as a cutting edge for a trim.

The image file formats supported by the program include the most common formats used in major technical imaging application areas: computer graphics, document management, engineering, mapping, and geographic information systems (GIS). Images can be bitonal, 8-bit gray, 8-bit color, or 24-bit color. Images with 16-bit color depth are not supported.

Several image file formats support images with transparent pixels. When image transparency is set to on, the program recognizes those transparent pixels and allows graphics in the drawing area to “show through” those pixels. (In bitonal images, background pixels are treated as transparent.) Transparent images can be gray-scale or color.

## Conversión de raster a vectorial¶

En nuestra discusión de datos vectoriales, explicamos que a menudo los datos ráster se utilizan como una imagen de fondo, que luego se utilizada como una base desde la cual se pueden digitalizar objetos espaciales.

Another approach is to use advanced computer programs to automatically extract vector features from images. Some features such as roads show in an image as a sudden change of colour from neighbouring pixels. The computer program looks for such colour changes and creates vector features as a result. This kind of functionality is normally only available in very specialised (and often expensive) GIS software.

## Publications Before 2010

Emch, M. E. (1999) Diarrheal Disease Risk in Matlab, Bangledesh. Social Science and Medicine 49 , 519-530.

Ali, M., M. Emch, C. Ashley & P. K. Streatfield (2001) Implementation of a Medical Geographic Information Information System: Concepts and uses. Journal of Health, Population, and Nutrition 19, 100-110.

Ali, M., M. E. Emch, M. Tofali & A. H. Baqui (2001) Implications of Health Care Provision on Acute Lower Respiratory Infection Mortality in Bangladeshi Children. Social Science and Medicine, 52, 267-277.

Emch, M. E. 2001. The Human Ecology of Deforestation in Bangladesh. In Deforestation, Environment and Sustainabe Development: A Comparative Analysis ed. D. Vajpeyi, 71-90. Westpoint, Connecticut Praeger.

Emch, M. E. & M. Ali (2001) Spatial and Temporal Patterns of Diarrheal Disease in Matlab, Bangladesh. Environment and Planning A, 33, 339-350.

Ali, M., M. Emch, J. P. Donnay, M. Yunus & R. B. Sack (2002) identifying Environmental Risk Factors for Endemic Cholera in Bangladesh. Health & Place, 8, 201-210.

Ali, M., M. Emch, J. P. Donnay, M. Yunus & R. B. Sack (2002) The Spatial Epidemiology of Cholera in an Edemic Area in Bangladesh. Social Science and Medicine, 55, 1015-1024.

Ali, M., M. E. Emch & J. P. Donnay (2002) Spatial Filtering Using a Raster Geographic Information System: Methods for Scaling Health and Environmental Data. Health & Place, 8,85-92.

Ali, M., M. E. Emch, M. Yunus & R. B. Sack (2002) Are the Environmental Niches of Vibrio Cholerae 0139 Different from those Vibrio Cholerae El Tor? International Journal of Infecious Disease, 5, 214-219.

Emch, M. E., A. Naheed & M. Ali. 2002. Tropical Disease. In Encyclopedia of Modern Asia eds. D. Levinson & K. Christensen. New York: Charles Scribner’s Sons.

Salim, M. D., T. Strauss & M. Emch (2002) A GIS-Integrated Intelligent System for Optimization of Asset Managment for Maintenance of Roads and Bridges Applied Artifical Intelligence, 628-637.

Ali, M., Y. Wagatsuma, M. Emch & R. F. Breiman (2003) Use of Geographic Infromation System for Defining Spatial Risk for Dengue Transmission in Bangladesh: Roles for Aedes Albopictus in an Urban Outbreak. American Journal of Tropical Medicine and Hygiene, 69, 634-640.

Emch, M. E. (2003) The Human Ecology of Mayan Cacao Farming in the Toledo District, Belize Human Ecology 31, 111-131.

Emch, M. E. & M. Ali. 2003. Spatial Cluster Analysis for Etiological Research and Identification of Socio-Environmental Risk Factors. In Geographic Information Systems and Health Application eds. O. Khan & R. Skinner, 172-187. Hershey, Pennsylvania: Idea Group Publishing

Ali, M., M. Emch, L. v. Seidlein, M. Yunus, D. A. Sack, J. Holmgren, M. Rao & J. D. Clemens (2005) Herd Immunity Conferred by Killed Oral Cholera Vaccines in Bangladesh.Lancet, 366, 44-49.

Ali, M., J. K. Park, D. Thiem, L. v. Seidein, D. Canh, M. E. Emch & J. D. Clemens (2005) Neighborhood Size and Local Geographic Veriation of Health Events: Uisng Hartley’s Test of Homogeneity to Select Optimal Neighborhood Size. International Journal of Health Geographies 4.

Emch, M., J. Quinn, M. PetersonM & M. Alexander (2005) Forest Cover Change in the Toledo District, Belize from 1975-1999: A Remote Sensing Perspective. The Professional Geographer 57, 256-267.

Ali, M., P. Goovaerts, N. Nazia, M. Z. Haq, M. Yunus & M. Emch (2006) Application of Poisson Kriging to the Mapping of Cholera and Dysentery Incidence in an Endemic Area of Bangladesh. International Journal of Health Geographies, 5, 1-11.

Emch, M., M. Ali, M. Yunus, J. K. Park, D. Sack & J. D. Clemens (2006) Relationahip Between Neighborhood-level Killed Oral Cholera Vaccine Coverage and Pretective Efficacy: Evidence for Herd Immunity. International Journal of Epidemiology, 35, 1044-1050.

Emch, M. & M. Peterson (2006) Mangrove Forest Cover Change in Bangladesh Sundarbans from 1989-2000: A Remote Sensing Approach. Geocarto International 21, 5-12.

Martn, N. & N. Theodore. 2006. Planned Manufacturing Districts In Planning and Urban Design Standards ed. A. P. Association. Hoboken, NL: Wiley.

McGrath, S. & N. Martin. 2006. Unregulated Work: Is Enforcement the Next Battle in the Fight for Workers’ Rights? In Real World Micro: A Microeconomics Reader from Dollars & Sense eds. D. Fireside & C. Tilly, 41-44. Boston: Dollars & Sense

Theodore, N., N. Martin & R. Hollon (2006) Securing the City: Emerging Markets in the Private Provision of Security Services in Chicago. Social Justice 33, 85-100.

.Emch, M., M. Ali, M. Yunus, D. Sack, C. Acosta & J. D. Clemens (2007) Efficacy Calculation in Ramdomized Vaccine Trials: Golbal or Local Measures? Health & Place, 13, 238-248.

Martin, N., S. Morales & N. Theodore (2007) Migrant Worker Centers: Contending the Downgrading in the Low-Wage Labor Market. GeoJournal 68, 155-165.

Theodore, N. & N. Martin (2007) Migrant Civil Society: New Voices in the Struggle Over Community Development Journal of Urban Affairs, 29, 269-287.

Theodore, N., N. Martin & R. Hollon. 2007. Der öffentliche Sektor als Sicherheitsmarkt in Chicago In Kontrollierte Urbanität. Zur Neoliberalisierung städtischer Sicherheitspolitikeds. V. Eick, J. Sambale & E. Töpfer, 83-105. Bielefeld: Transcript Verlag.

Wan, X.-F., G. Chen, F. Luo, M. Emch & R. Donis (2007) A Quantitative Genotype Alforithm Reflecting H5N1 Avian Influenza Niches. Bioinfomatics 23, 2368-2375.

Wan, X. F., X. M. Wu, G. LIn, S. B. Holton, R. A. Desmone, C. R. Shyu, Y. Guan & M. Emch (2007) Computational Identification of Reassortments in Avian Influenza Viruses.Avian Doseases, 51, 434-439.

Ali, M., M. Emch, M. Yunus, D. Sack, A. Lopez, J. Holmgren & J. Clemens (2008) Vaccine Protection of Bangladesh Infants and Young Children Against Cholera: Implications for Vaccine Deployment and Person-to-Person Trnasmission. The Pediatric Infectious Disease Journal, 27, 33-37.

Cordero-Guzman, H., N. Martin, V. Quiroz-Becerra & N. Theodore (2008) Voting with Their Feet: Nonprofit Organizations and Immigrant Mobilization American Behavioral Scientist, 52, 598-617.

Emch, M., M. Ali & M. Yunus (2008) Risk Areas and Neighborhood-level Risk Factors for Shigella dysenteriae 1 and Shigella flexneri: Implications for Vaccine DevelopmentHealth & Place, 14, 96-105.

Emch, M., C. Feldacker, M. S. Islam & M. Ali (2008) Seasonality of Cholera from 1974 to 2005: A Review of Global Patterns International Journal of Health Geographies, 7, 1-33.

Emch, M., C. Feldacker, M. Yunus, P. K. Streatfield, V. Thiem, D. Canh & M. Ali (2008) Local Environmental Drivers of Cholera in Bangladesh and Vietnam. American Journal of Tropical Medicine and Hygiene, 78, 823-832.

Mena, C. F., S. J. Walsh & R. E. Bilsborrow (2008) Secondary Forest Succession in the Northern Ecuadorian Amazon: Interactions among People, Place, and Environment. Regional Environmental Change.

Wan, X.-F., T. Nguyen, C. Smith, Z. Zhao, M. Carrel, C. Davis, A. Balish, F. Luo, M. Emch, A. Klimov & R. Donis (2008) Evolution of Highly Pathogenic H5N1 avian Influenza Viruses in Vietnam Between 2001 and 2007. PLoS One, 3, 10.

Ali, M., M. Emch, M. Yunus & J. Clemens (2009) Modeling Spatial Heterogenity of Disease Risk and Evaluation of the Impact of Vaccinaion Vaccine, 27, 3724-3729.

Arévalo, E. B. & M. A. Ros-Tonen (2009) Discourses, power negotiations and indigenous political organization in forest partnerships: The case of Selva de Matavén, Colombia.Human Ecology, 37, 733-747.

Carrel, M., M. Emch, K. Streatfield & M. Yunus (2009) Spatio-temporal Clustering of Cholera: The Impact of Flood Control in Matlab, Bangladesh, 1983-2003. Health & Place, 15,771-781.

Cravey, A. J. (2009) Creating a cultural connection. Tar Heel Junior Historian, 49, 30-32.

DeFilippis, J., N. Martin, A. Bernhardt & S. McGrath (2009) On the Character and Organization of Unregulated Work in the Cities of the United States. Urban Geography, 30, 63-90.

Emch, M., M. Ali, E. D. Root & M. .Yunus (2009) Spatial and Environmental Connectivity Alalysis in Vaccine Trials Social Science and Medicine, 68, 631-637.

Hwang, T., L. Band & T. Hales (2009) Ecosystem processes at the watershed scale: Extending optimality theory from plot to catchment. Water Resource Research 45, 1-20.

Root, E. D., R. Meyer & M. Emch (2009) Evidence for Localized Clustering of Gastroschisis Births in North Carolina, 1999-2004. Social Science and Medicine, 68, 1361-1367.

Wan, X.-F., M. E. Emch & Z.-M. Zhao. 2009. Advances in Molecular Evolution of Influenza A Virus In Global View of the Fight Against Influenza, ed. P. M. Mitrasinovic. New York: Nova Science Publishers Inc.

## Calculate scale of a raster - Geographic Information Systems

Raster dataset showing the probability of elevated concentrations of nitrate in ground water in Colorado, hydrogeomorphic regions and fertilizer use estimates not included. 1.0 Raster digital data

Draft Federal regulations (U.S. Environmental Protection Agency, Pesticides and Ground Water State Management Plan Regulation Proposed Rule, U.S. Federal Register, v. 61, no. 124, June 26, 1996, p. 33260-33301) may require that each State develop a State Pesticide Management Plan (PMP) for the herbicides atrazine, alachlor, metolachlor, and simazine. The Colorado Agricultural Chemicals and Groundwater Protection Program--a cooperative effort of the Colorado Department of Agriculture (CDA), the Colorado Department of Public Health and Environment (CDPHE), and the Colorado State University Cooperative Extension (CSUCE)--is developing a PMP for each of the herbicides and would benefit from a map that could be used to predict the probability of detecting atrazine and DEA in ground water. The map could be incorporated into the PMP and provide a sound hydrogeologic basis for atrazine management in Colorado. Other organizations and programs that could benefit from maps that predict the probability of detecting atrazine, DEA, and elevated concentrations of nitrate in ground water include the agri-chemical industry, county and city governments, farmers, planning and zoning commissions, education programs for applicators, and State programs related to Wellhead Protection, Drinking Water, Home-A-Syst, and Best Management Plans (BMP&aposs). To address these needs, the U.S. Geological Survey (USGS), in cooperation with the CDA, CDPHE, and CSUCE, conducted a study to develop maps to predict the probability of detecting atrazine and(or) DEA and elevated concentrations of nitrate in ground water in Colorado.

None Planned -109.813 -101.475 41.574 36.424 USGS Thesaurus Ground water Ground-water vulnerability Ground-water probability Ground-water susceptibility Vulnerability Susceptibility Probability Nitrate Atrazine Desethyl-atrazine Ground-water quality Ground-water contamination Probability of ground-water contamination inlandWaters ISO 19115 Topic Categories elevation geoscientificInformation inlandWaters

Geographic Names Information System

Michael G. Rupert U.S. Geological Survey Hydrologist Mailing 201 W. 8th Street, Suite 200 Pueblo Colorado

USA 1-888-275-8747 719-544-7155 [email protected]

This dataset was produced through a cooperative effort with Rob Wawrzynski of the Colorado Department of Agriculture, Bradford Austin of the Colorado Department of Public Health and Environment, and Regan Waskom and Troy Bauder of the Colorado State University Cooperative Extension. Microsoft Windows XP Professional Operating System Version 5.1.2600 ArcInfo version 8.2 GRID cells were randomly selected from this dataset and checked to ensure the correct probability values were calculated using the logistic regression equation and the input datasets on herbicide use, fertilizer use, hydrogeomorphic regions, land cover, and soils. Not applicable for raster data The extent of this dataset is the entire State of Colorado

This dataset was developed by combining information from multiple datasets the horizontal accuracy is dependent on the combined accuracy from all of the input datasets, which is impossible to quantify. Probably the best estimate of the horizontal accuracy is determined by the STATSGO soils data, developed at 1:250,000 scale.

Spatial data in GIS format on agricultural chemical use, land use, and cropping practices in the United States Vector digital data U.S. Geological Survey Water-Resources Investigations Report U.S. Geological Survey Water-Resources Investigations Report 94-4176

2,000,000 Online 1987 Based upon 1987 Census of Agriculture cropping data USGS WRIR 94-4176 Atrazine use for each county in Colorado Cederstrand, J.R., and Becker, M.F., U.S. Geological Survey

Digital map of aquifer boundary for the High Plains aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming Version 1.0 Vector digital data U.S. Geological Survey Open-File Report U.S. Geological Survey Open-File Report 99-267

1000000 Online 1980 Other_Citation_Details These data were compiled from paper maps developed by McGrath Online_Linkage http://water.usgs.gov/GIS/metadata/usgswrd/ofr99-267.htm) USGS OFR 99-267 One of three datasets used to delineate hydrogeomorphic regions in Colorado. Green, G.N., U.S. Geological Survey

The digital geologic map of Colorado in ARC/INFO format Version 1.0 Vector digital data U.S. Geological Survey Open-File Report U.S. Geological Survey Open-File Report 92-507

These data were digitized from the original scribe sheets used to prepare the published Geologic Map of Colorado (Tweto, Ogden, 1979, Geologic map of Colorado: U.S. Geological Survey, scale 1:500,000, 1 sheet. http://geo-nsdi.er.usgs.gov/metadata/open-file/92-507 /metadata.faq.html 500,000 Online 1979 Based on 1979 geologic map USGS OFR 92-507 One of three datasets used to delineate hydrogeomorphic regions in Colorado. David Lorenz, U.S. Geological Survey

Estimates of nitrogen fertilizer use, 1997 Vector digital data Data not formally published Data not published

David Lorenz, U.S. Geological Survey

Unpublished data on file at U.S. Geological Survey office, Mounds View, Minnesota 2,000,000 Online 1997 Based on 1997 estimates Not formally published Estimates of fertilizer use in each county of Colorado during 1997. Schwarz, G.E., and Alexander, R.B., U.S. Geological Survey

State Soil Geographic (STATSGO) database for the conterminous United States. Version 1.1 Vector digital data U.S. Geological Survey Open-File Report U.S. Geological Survey Open-File Report 95-449

Soils data in this coverage were originally obtained from the State Soil Geographic (STATSGO) database (U.S. Department of Agriculture, 1991, State Soil Geographic (STATSGO) data base: U.S. Department of Agriculture, Soil Conservation Service, Miscellaneous Publication 1492, 88 p.). Schwarz and Alexander (1995) performed weighted averaging of many of the soil characteristics contained in the original STATSGO database. https://water.usgs.gov/lookup/getspatial?ussoils 250,000 Online 1991 STATSGO data published in 1991 USGS OFR 95-449 Soils data for the State of Colorado U.S. Geological Survey

National land cover dataset Version 20000909 Raster digital data online data online data

Data should be considered preliminary because they were still being assessed for accuracy at the time of this publication. http://edc2.usgs.gov/lccp/nlcd/show_data.asp?code=CO&state=Colorado 30-meter GRID cells Online 1992 Based on 1992 satellite imagery NLCD This dataset supplied land use/land cover data for the State of Colorado. U.S. Geological Survey

Boundary of valley-fill aquifer in the lower Arkansas River Basin 1 Vector digital data not published not published

Data not published. Data were digitized from original mylar sheets produced by Hurr and Moore (1972, Hydrogeologic characteristics of the valley-fill aquifer in the Arkansas River valley, Bent County, Colorado: U.S. Geological Survey Hydrologic Investigations Atlas HA-0461, scale 1:62,500, 2 sheets), Nelson, G.A., Hurr, R.T., and Moore, J.E. (1989a, Hydrogeologic characteristics of the valley-fill aquifer in the Arkansas River Valley, Prowers County, Colorado: U.S. Geological Survey Open-File Report 89-254, scale 1:62,500, 3 sheets), Nelson, G.A., Hurr, R.T., and Moore, J.E. (1989b, Hydrogeologic characteristics of the valley-fill aquifer in the Arkansas River Valley, Crowley and Otero Counties, Colorado: U.S. Geological Survey Open-File Report 89-255, scale 1:62,500, 3 sheets), and Nelson, G.A., Hurr, R.T., and Moore, J.E. (1989c, Hydrogeologic characteristics of the valley-fill aquifer in the Arkansas River Valley, Pueblo County, Colorado: U.S. Geological Survey Open-File Report 89-256, scale 1:62,500, 3 sheets). None 62,500 digital 1989 Based on 1989 publications not published One of three datasets used to delineate hydrogeomorphic regions in Colorado.

Projected all input datasets to a common projection and datum (Colorado Albers, NAD 27)

The 30-meter NLCD GRIDS were too large for the computers available for this study to manipulate in raw form, so the files were generalized to 60-meter resolution. The "RESAMPLE" command with the "nearest neighbor assignment" option in GRID was used to generalize the NLCD GRIDS to 60 meters.

One of the independent variables used in the logistic regression models was the percentage of certain land-cover classifications within 500-meter and 2,000-meter buffers around each well. To transfer these logistic regression models to the probability GRIDS, GRIDS were constructed that contained the percentage of certain land-cover classifications within 500-meter and 2,000-meter buffers around each individual grid cell. These GRIDS were produced from the 60-meter NLCD GRID produced by the previous step. These GRIDS were made in three substeps. 1) First, make individual GRIDS coded as one for "land cover present" and zero for "land cover not present" for the following land-cover classifications: low-intensity residential, shrubland, pasture/hay, row crops, and small grains land-cover classifications. As an example, low-intensity residential land cover is coded as "21" in the NLCD GRID, so the following equation was used in GRID to convert the dataset: low_res_g = con(nlcd_60m_g eq 21,1,0) 2) Second, add up the number of cells of low-intensity residential cells in each buffer: temp_num_g = focalsum(low_res_g,circle,33) where 33 is the 2,000-meter radius in number of cells. Use 8 if you are calculating 500-meter radius. 3) Third, calculate the percentage of low-intensity residential cells by dividing the number of cells in each buffer by the total possible: low_res_pct_g = (temp_num_g / float(3409)) * 100 where 3409 is the total number of 60-meter cells within a 2,000-meter buffer. Use 197 if you are calculating 500-meter buffers.

Convert polygon coverages of atrazine use, fertilizer use, hydrogeomorphic regions, and the various soils factors to 60-meter GRIDS. Use the "setwindow" and "setcell" commands in GRID to assure that all new GRIDS line up with the NLCD data, then use the "polygrid" command to convert the coverages to GRIDS.

Construct new atrazine-use and fertilizer-use GRIDS where atrazine and fertilizer use estimates are only assigned to agricultural lands all other land-cover categories are assigned values of zero. To do this, construct grids that are assigned 1 for agricultural land cover and zero for all other land-cover types. Multiply those GRIDS by the original atrazine and fertilizer use GRIDS. The resultant GRIDS have atrazine and fertilizer use estimates only for GRID cells in agricultural lands.

Next, construct the actual probability GRIDS. The GRIDS were constructed by calculating the probability of detecting atrazine/desethyl-atrazine or nitrate in ground water using the equation developed with logistic regression (see Rupert, 2003, Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado: U.S. Geological Survey Water-Resources Investigations Report 02-4269, 35 p., http://water.usgs.gov/pubs/wri/wri02-4269/). The following is an example of the logistic regression equation: Prob = (e**(a + b*chem_use + c*land_cover + d*soils))/(1+e**(a + b*chem_use + c*land_cover + d*soils)) In other words, the probability of detecting the compound in ground water is equal to "e raised to the regression equation" divided by "one plus e raised to the regression equation," where "a," "b," "c," and "d" are the model coefficients calculated by logistic regression. The probability GRIDS were constructed in four steps. The first was to calculate the regression equation: "temp1 = (a + b*Chem_use + c*land_cover + d*soils)" The second step was to calculate the actual probability value for every 60-meter cell in Colorado, times 100 to convert it to percent: "temp2 = 100 * (exp (temp1)/(1+exp(temp1))" The third step was to clip out portions of the State that weren&apost included in the model (only alluvial aquifers were mapped, so the mountainous regions of the State were clipped out). temp3 = temp2 * clip_grid The fourth step was to set all the non-mapped grid cells to "null" final_grid = setnull(temp3 eq 0, temp3) The regression coefficients used for the eight probability GRIDS constructed by this project are listed in Rupert (2003)

Randomly selected at least 20 GRID cells in each of the 8 probability GRIDS and calculated probability value by hand to ensure that the probability values were correctly calculated.