DOCSLIB.ORG

Dasymetric Mapping

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Dasymetric Mapping Dasymetric Mapping Some geographical distributions are best mapped as ‘volumes’ that represent surfaces characterized by plateaus of relative uniformity separated from one another by relatively steep slopes or escarpments where there is a marked change in statistical value. There are two techniques for defining this stepped surface, the choroplethic and the dasymetric. The choroplethic technique requires only grouping of similar values, and detail is constrained by the boundaries of enumeration units which rarely have to do with the variable being mapped. In choroplethic mapping, emphasis in the graphic statement is placed upon comparing relative magnitudes across the surface of the map. In contrast, the dasymetric method highlights areas of homogeneity and areas of sudden change and is produced by refining the values estimated by the choroplethic technique. Your task is to produce a dasymetric map of cropland in south central Ohio counties, using four variables to refine the enumerated data. Prepare this map and all related maps using ArcView GIS. It might be a good idea to keep a journal log while you are working through this exercise, annotated with hardcopy maps if you prefer, personal notes describing in your own words what the commands you performed in ArcView do, for later reference. Data for the initial distribution is given on the base map on page 4. Initial files for the assignment are in the Handouts folder in the usual Csiss folder on \\ubar\labs\. Copy the folder labeled dasy_ohio from the Handouts directory to your personal directory (for example to E:). Open the file dasy.apr in your directory with ArcView. When opening the project file you might be asked where certain missing files are. If so, select the files in your directory that have the same name as the missing ones, and click each time OK to continue. Once all the data is reassigned you should see an open view, labeled Cropland in Ohio 1959 with the five themes described below. Go to FILE: SET WORKING DIRECTORY... type in: your_local_directory:/your_personal _directory/dasy_ohio/ to set your working directory to your home directory and the lab folder you copied over. ArcView will produce a bunch of files and you want to make sure that are all stored in the lab directory. Make sure that any folder or file associated with this lab in your directory does not have any spacing in the label, nor Uppercase. Please Note: You might not be able to finish this lab in one sitting. So, don’t worry if you don’t. You can find the result maps at the end of this handout just in case. page: 2 For much of the exercise, the following classes will be used: 0 – 9% coverage of cropland 10 – 29% 30 – 49% 50 – 69% 70 – 89% 90 – 100% On any maps you print out, include the following information: • an appropriate title • a legend • a scale • your name • data source and year Use partial spectral progressions to indicate the classes of your map and for all worksheet maps except for economic activity which should be mapped using a full spectral progression. We have briefly touched on this concept in lectures. Part spectral progressions are generated as linear transects across or through the color wheel; full spectral progressions are generated as arcs of full or partial circumference. Think about the layer you are mapping (e.g. coverage by woodland, coverage by urban areas, terrain configuration, and coverage by cropland) and apply a meaningful progression. The following materials are provided to help you complete this exercise. The files are scanned or digitized from 1:100,000 scale copies of 1:24,000 maps. The stated resolution (.60 km per cell) is meaningful, and should guide your decisions about how much detail to include or simplify. This handout A discussion of the dasymetric technique and the methods to employ, including step-by-step procedures for preparing a dasymetric map. Maps • choropleth % crop A base map displaying the percentage of total area classed as cropland for each county. The single figure for each county includes several classes of cropland: cropland harvested, cropland used only for pasture, cropland not harvested and not pastured, areas in grasses and legumes for soil improvement, and idle cropland (fallow) and crop failure. For each county, these values have been summed and the total divided by total county acreage to give the percentage. The remaining area in each county consists of urban and rural non-agricultural landuses, woodland (pastured and non pastured), and non urban built-up land (e.g. farmsteads). Data are derived from the 1959 Census of Agriculture. The county names are included in the theme’s attribute table. In addition, there is a theme with county labels called county names. • towns A map of urban and rural non-agricultural land use areas. The areas outlined are occupied by residential, commercial, industrial, transportation, mining and similar land uses. Areas of many sizes have been shown, from the completely built-up city and metropolitan areas to crossroads settlements, hamlets, and galaxies of strip mines which dot the Ohio countryside. The page: 3 areas are shown as close to scale as possible, but crude reproduction techniques do involve some exaggeration. These data are derived from 1:24,000 topographic sheets. Maps from the 1960 Census were used to outline the urban areas. • woodland A map showing the amount of woodland cover throughout the area in six classes. It was derived from the same topographic sheets and reflects a series of estimates based on USGS 7.5 minute quadrangles. • terrain A ‘surface configuration’ map shows in four classes the nature of the terrain. This map was generalized using the work of Guy-Harold Smith, “The Relative Relief of Ohio”, Geographical Review vol.25, p. 272-284, 1935. • economy A map of ‘economic regions’ outlines areas by agricultural economic type. This is based on work by Alfred J. Wright, “Types of Farming Areas”, Economic Geography of Ohio Columbus Division of the Geological Survey of Ohio, Bulletin #50, 1953, Figure 13. It is important to realize that the concern in this exercise is with the dasymetric procedure and not with cropland in Ohio. Since this exercise marks the first time that you are presented with this particular mapping technique (map overlay) the methods are set down in considerable detail. After gaining understanding of the nature of the technique from the following section, the simplest way to proceed is simply to follow the steps in the order of presentation. Once accomplished here, map overlay techniques as used in conventional geographical information systems or for spatial modeling should be more readily understood. page: 4 page: 5 page: 6 page: 7 The Dasymetric technique The dasymetric technique provides one solution to the problem of mapping data gathered on the basis of enumeration areas whose boundaries bear no direct relation to the variable being mapped. It is based on certain assumptions. Assumption 1 the variable being mapped occurs non-uniformly over the statistical unit area (in this case, counties). Assumption 2 even though wide overall variations exist for the distribution, it basically consists of areas of relative uniformity separated by sudden changes in value. Assumption 3 other variables may be collected in association with the variable in question whose relation to the variable may be determined and expressed as a set of rules. These variables will enable the cartographer to adjust and refine the given data to form homogeneous regions whose boundaries are independent of the enumeration unit boundaries. The variable in question for this exercise is cropland (density). The four other variables are urban and non agricultural land use, woodland area, terrain, and economic activity. These variables may be characterized as being either “limiting variables” or “related variables”. We will tackle the limiting variables first. The limiting variables In some degree limiting variables restrict the possible occurrence of cropland. That is, a certain percentage of a limiting variable occurring in an area will set an absolute upper limit on the percentage of the mapped variable (cropland) that can occur in the same area. Two limiting variables are being employed in this exercise – urban landuse and woodland. An area devoted to urban landuse precludes the occurrence of cropland. In a categorical fashion, this may be expressed by the following rule: if urban landuses are present, then there can be no cropland. If there are no urban landuses, then cropland can exist (see Figure 1). In terms of the six categories of cropland, note that the presence of urban land use restricts an area to cropland category 1, namely, 0% cropland. All six classes are possible where no urban land use occurs. cropland classes (%) 123456 urban land use 0-9 10-29 30-49 50-69 70-89 90-100 present not present cropland not possible Figure 1: the limiting variable urban landuse It is also possible to compute a precise adjusted percentage for the cropland density, using a formula first published by J.K. Wright. It is called the computation of fractional parts of densities. In the formula below, D stands for Density, A stands for Area: DDA− 52−∗(.) 0 0 1 52 D = omm= ==57. 7 n − − 1 Am 101..09 page: 8 Thus, for a county with 52% cropland, 10% of the county covered by urban landuse, the percentage of cropland in the remaining county area must be approximately 58%. TASK 1 Revise estimates of county cropland density using this formula and the layer of urbanized areas. The first step is to generalize this layer, and the second is to do the computations. Tools You will be working with ArcView’s Spatial Analyst, a raster-based GIS module.
Load more

Recommended publications
  • Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series
    Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series Cynthia A. Brewer* and Linda Pickle** *Department of Geography, The Pennsylvania State University **National Cancer Institute Our research goal was to determine which choropleth classification methods are most suitable for epidemiological rate maps. We compared seven methods using responses by fifty-six subjects in a two-part experiment involving nine series of U.S. mortality maps. Subjects answered a wide range of general map-reading questions that involved indi- vidual maps and comparisons among maps in a series. The questions addressed varied scales of map-reading, from in- dividual enumeration units, to regions, to whole-map distributions. Quantiles and minimum boundary error classi- fication methods were best suited for these general choropleth map-reading tasks. Natural breaks (Jenks) and a hybrid version of equal-intervals classing formed a second grouping in the results, both producing responses less than 70 percent as accurate as for quantiles. Using matched legends across a series of maps (when possible) increased map-comparison accuracy by approximately 28 percent. The advantages of careful optimization procedures in choropleth classification seem to offer no benefit over the simpler quantile method for the general map-reading tasks tested in the reported experiment. Key Words: choropleth, classification, epidemiology, maps. horopleth mapping is well suited for presenta- a range of classification methods in anticipation of in- tion and exploration of mortality-rate data. Of creased production of mortality maps at the NCHS, the C the many options available, epidemiologists cus- National Cancer Institute (NCI), and other health agen- tomarily use quantile-based classification in their map- cies through desktop geographic information systems ping (see Table 5 in Walter and Birnie 1991).
    [Show full text]
  • Cartography. the Definitive Guide to Making Maps, Sample Chapter
    Cartograms Cartograms offer a way of accounting for differences in population distribution by modifying the geography. Geography can easily get in the way of making a good Consider the United States map in which thematic map. The advantage of a geographic map is that it states with larger populations will inevitably lead to larger numbers for most population- gives us the greatest recognition of shapes we’re familiar with related variables. but the disadvantage is that the geographic size of the areas has no correlation to the quantitative data shown. The intent However, the more populous states are not of most thematic maps is to provide the reader with a map necessarily the largest states in area, and from which comparisons can be made and so geography is so a map that shows population data in the almost always inappropriate. This fact alone creates problems geographical sense inevitably skews our perception of the distribution of that data for perception and cognition. Accounting for these problems because the geography becomes dominant. might be addressed in many ways such as manipulating the We end up with a misleading map because data itself. Alternatively, instead of changing the data and densely populated states are relatively small maintaining the geography, you can retain the data values but and vice versa. Cartograms will always give modify the geography to create a cartogram. the map reader the correct proportion of the mapped data variable precisely because it modifies the geography to account for the There are four general types of cartogram. They each problem. distort geographical space and account for the disparities caused by unequal distribution of the population among The term cartogramme can be traced to the areas of different sizes.
    [Show full text]
  • Numbers on Thematic Maps: Helpful Simplicity Or Too Raw to Be Useful for Map Reading?
    International Journal of Geo-Information Article Numbers on Thematic Maps: Helpful Simplicity or Too Raw to Be Useful for Map Reading? Jolanta Korycka-Skorupa * and Izabela Małgorzata Goł˛ebiowska Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmiescie 30, 00-927 Warsaw, Poland; [email protected] * Correspondence: [email protected] Received: 29 May 2020; Accepted: 26 June 2020; Published: 28 June 2020 Abstract: As the development of small-scale thematic cartography continues, there is a growing interest in simple graphic solutions, e.g., in the form of numerical values presented on maps to replace or complement well-established quantitative cartographic methods of presentation. Numbers on maps are used as an independent form of data presentation or function as a supplement to the cartographic presentation, becoming a legend placed directly on the map. Despite the frequent use of numbers on maps, this relatively simple form of presentation has not been extensively empirically evaluated. This article presents the results of an empirical study aimed at comparing the usability of numbers on maps for the presentation of quantitative information to frequently used proportional symbols, for simple map-reading tasks. The study showed that the use of numbers on single-variable and two-variable maps results in a greater number of correct answers and also often an improved response time compared to the use of proportional symbols. Interestingly, the introduction of different sizes of numbers did not significantly affect their usability. Thus, it has been proven that—for some tasks—map users accept this bare-bones version of data presentation, often demonstrating a higher level of preference for it than for proportional symbols.
    [Show full text]
  • Beyond Choropleth Maps: a Review of Techniques to Visualize Quantitative Areal Geodata
    INFOVIS READING GROUP WS 2015/16 Beyond choropleth maps: A review of techniques to visualize quantitative areal geodata Alsino Skowronnek– University of Applied Sciences Potsdam, Department of Design Abstract—Modern digital technologies and the ubiquity of spatial data around us have recently led to an increased output and visibility of geovisualizations and digital maps. Maps and »map-like« visualizations are virtually everywhere. While areal thematic geodata has traditionally often been represented as choropleth maps, a multitude of alternative techniques exist that address the shortcomings of choropleths. Advances of these alternative techniques derive both from the traditional domains of geography and cartography, as well as from more recent disciplines such as information visualization or even non-academic domains such as data journalism. This paper will review traditional and recent visualization techniques for quantitative areal geodata beyond choropleths and evaluate their potential and limitations in a comparative manner. Index Terms—Choropleth Maps, Geovisualization, Cartograms, Grid Maps, Spatial Treemaps ––––––––––––––––––– ✦ ––––––––––––––––––– 1INTRODUCTION choropleths exhibit a number of shortcomings (i.e. sup- port of specific data types and certain visual inadequa- cies) in different situations. eople have been fascinated with maps and geospatial This paper will briefly review the literature on choro- representationsP of the world as long as we can think. This pleth maps in the following part, before contrasting the is not only due to maps’ inherent potential for storytelling technique with three alternative approaches, their main and identification with places, but also due to humans’ characteristics and implications for visualizing areal geo- excellent spatio-cognitive abilities, which allow us to easi- data in more general terms.
    [Show full text]
  • Simple Thematic Mapping
    The Stata Journal (2004) 4, Number 4, pp. 361–378 Simple thematic mapping Maurizio Pisati Department of Sociology and Social Research, Universit`adegli Studi di Milano-Bicocca, Italy [email protected] Abstract. Thematic maps illustrate the spatial distribution of one or more variables of interest within a given geographic unit. In a sense, a thematic map is the spatial analyst’s equivalent to the scatterplot in nonspatial analysis. This paper presents the tmap package, a set of Stata programs designed to draw five kinds of thematic maps: choropleth, proportional symbol, deviation, dot, and label maps. The first three kinds of maps are intended to depict area data, the fourth is suitable for representing point data, and the fifth can be used to show data of both types. Keywords: gr0008, tmap choropleth, tmap propsymbol, tmap deviation, tmap dot, tmap label, thematic map, choropleth map, proportional symbol map, deviation map, dot map, label map 1 Introduction Thematic maps represent the spatial distribution of one or more variables of interest within a given geographical unit (Monmonier 1993; Kraak and Ormeling 1996). For example, a sociologist could use a choropleth map (a.k.a., shaded map) to show how the percent of families below the poverty line varies across the states or the provinces of a given country. In turn, a police officer could be interested in analyzing a dot map showing the locations of drug markets within a given city. As Bailey and Gatrell (1995) have put it, in a sense, a thematic map is the spatial analyst’s equivalent to the scatter plot in nonspatial analysis.
    [Show full text]
  • 1 'Intelligent' Dasymetric Mapping and Its Comparison to Other Areal
    ‘Intelligent’ Dasymetric Mapping and Its Comparison to Other Areal Interpolation Techniques Jeremy Mennis Department of Geography and Urban Studies, 1115 W. Berks St., 309 Gladfelter Hall, Temple University, Philadelphia, PA 19122 Phone: (215) 204-4748, Fax: (215) 204-7388, Email: [email protected] Torrin Hultgren Department of Geography, UCB 360, University of Colorado, Boulder, CO 80309 Email: [email protected] 1 Abstract In previous research we have described a dasymetric mapping technique that combines an analyst's subjective knowledge with a sampling approach to parameterize the reapportionment of data from a choropleth map to a dasymetric map. In the present research, we describe a comparison of the proposed dasymetric mapping technique with the conventional areal weighting and 'binary' dasymetric mapping techniques. The comparison is made using a case study dasymetric mapping of U.S. Census tract-level population data in the Front Range of Colorado using land cover data as ancillary data. Error is quantified using U.S. Census block- level population data. The proposed dasymetric mapping technique outperforms areal weighting and certain parameterizations of the proposed technique outperform 'binary' dasymetric mapping. 1 Introduction A dasymetric map depicts a statistical surface as a set of enumerated zones, where the zone boundaries reflect the underlying variation in the surface and represent the steepest surface escarpments (Dent, 1999). Dasymetric mapping has its roots in the work of Russian cartographer Semenov Tian-Shansky (Bielecka, 2005) and American J.K. Wright (1936). Later significant contributions to dasymetric mapping were made by O'Cleary (1969) and, concurrent with more recent advances in GIS and environmental remote sensing, researchers in spatial analysis (Goodchild et al., 1993; Wu et al., 2005).
    [Show full text]
  • An Evaluation of Visualization Methods for Population Statistics Based on Choropleth Maps
    An Evaluation of Visualization Methods for Population Statistics Based on Choropleth Maps Lonni Besanc¸on, Matthew Cooper, Anders Ynnerman, and Fred´ eric´ Vernier Fig. 1. Choropleth map (A) augmented with 3D extrusion (C), contiguous cartogram (D), and rectangular glyphs (E) at the same level of granularity and with 3D extrusion (B), Heatmap (F) and dot map (G) at a finer level of granularity. Abstract— We evaluate several augmentations to the choropleth map to convey additional information, including glyphs, 3D, cartograms, juxtaposed maps, and shading methods. While choropleth maps are a common method used to represent societal data, with multivariate data they can impede as much as improve understanding. In particular large, low population density regions often dominate the map and can mislead the viewer as to the message conveyed. Our results highlight the potential of 3D choropleth maps as well as the low accuracy of choropleth map tasks with multivariate data. We also introduce and evaluate popcharts, four techniques designed to show the density of population at a very fine scale on top of choropleth maps. All the data, results, and scripts are available from osf.io/8rxwg/ Index Terms—Choropleth maps, bivariate maps 1INTRODUCTION A perennial issue with mapping statistical information onto choro- said to be harder to understand [102]. It is thus still unclear which pleth maps is that they tend to overemphasize large, yet often strategies should be adopted to create bivariate maps to properly convey sparsely populated, administrative areas because of their strong vi- population information in addition to the measured variable usually sual weight [1, 44, 64, 88, 94].
    [Show full text]
  • Mapping Crime: Understanding Hot Spots
    U.S. Department of Justice Office of Justice Programs AUG. AUG. National Institute of Justice 05 Special REPORT Mapping Crime: Understanding Hot Spots www.ojp.usdoj.gov/nij U.S. Department of Justice Office of Justice Programs 810 Seventh Street N.W. Washington, DC 20531 Alberto R. Gonzales Attorney General Regina B. Schofield Assistant Attorney General Sarah V. Hart Director, National Institute of Justice This and other publications and products of the National Institute of Justice can be found at: National Institute of Justice www.ojp.usdoj.gov/nij Office of Justice Programs Partnerships for Safer Communities www.ojp.usdoj.gov AUG. 05 Mapping Crime: Understanding Hot Spots John E. Eck, Spencer Chainey, James G. Cameron, Michael Leitner, and Ronald E. Wilson NCJ 209393 Sarah V. Hart Director This document is not intended to create, does not create, and may not be relied upon to create any rights, substantive or procedural, enforceable by law by any party in any matter civil or criminal. Findings and conclusions of the research reported here are those of the authors and do not necessarily reflect the official position or policies of the U.S. Department of Justice. The products, manufacturers, and organizations discussed in this document are presented for informational purposes only and do not constitute product approval or endorsement by the U.S. Department of Justice. The National Institute of Justice is a component of the Office of Justice Programs, which also includes the Bureau of Justice Assistance, the Bureau of Justice Statistics, the Office of Juvenile Justice and Delinquency Prevention, and the Office for Victims of Crime.
    [Show full text]
  • Choropleth Map Accuracy and the Number of Class Intervals
    POSTER SESSIONS 173 CHOROPLETH MAP ACCURACY AND THE NUMBER OF CLASS INTERVALS. Franky A.N. Declercq Laboratory for Cartography - Katholieke Universiteit Leuven Redingenstraat 16,8-3000 Leuven (Belgium) e-mail: [email protected] Abstract For a large number of data classifications, performance of the Goodness of Variance Fit (GVF) as accuracy optimization measure was compared to two other measures. It is shown that in general a number of7 to 8 classes is preferred to generate accurate choropleth maps. Two newly developed classification methods are suboptimal solutions of the GVF optimal classification method and are designed to minimize image fragmentation and maximize rounding of class limits. Firm rounding of the optimal class limits to less than two significant digits decreases accuracy only slightly. Suboptimal classifications that yield a less fragmented image become rather inaccurate and need 9 to 10 classes to maintain classification accuracy. 1 Aim The Goodness of Variance Fit (GVF) is compared to the Goodness of Deviation around the Median Fit (GDMF) and Goodness of Absolute Deviation Fit (GAOF) as measures to optimize classification accuracy. Two fast iterative classification methods that are developed to run on PC's are introduced. One method determines class breaks with highly rounded digits, the other seeks for class limits that result in a less fragmented map image. Both methods are designed to maintain the highest possible classification accuracy. These new classification methods are applied to a variety of data sets and compared with the Jenks optimal method and the traditional equal interval method in terms of generated classification accuracy (GVF), fragmentation index <PI) and number complexity index (NCI).
    [Show full text]
  • CHOROPLETH MAPS on HIGH RESOLUTION Crts : the EFFECTS of NUMBER of CLASSES and HUE on COMMUNICATION
    CHOROPLETH MAPS ON HIGH RESOLUTION CRTs : THE EFFECTS OF NUMBER OF CLASSES AND HUE ON COMMUNICATION By: Patricia Gilmartin and Elisabeth Shelton Gilmartin, P. and E. Shelton (1990) Choropleth Maps on High Resolution CRTs: The Effects of Number of Classes and Hue on Communication. Cartographica, v. 26(2): 40-52. Made available courtesy of University of Toronto Press: http://www.utpress.utoronto.ca/ ***Reprinted with permission. No further reproduction is authorized without written permission from University of Toronto Press. This version of the document is not the version of record. Figures and/or pictures may be missing from this format of the document.*** Abstract: The research reported here was designed to determine how quickly and accurately map readers viewing choropleth maps on a high-resolution computer monitor are able to identify to which class an areal unit on the map belongs, when the map has between four and eight classes and is produced in shades of either gray, green or magenta. As expected, accuracy rates decreased and reaction times increased as the number of classes on the map increased, Accuracy rates ranged from 91.9% for four-class maps to 68.2% for eight-class maps (averaged for all three colors used in the study). Hue also affected accuracy rates and reaction times, the best results being obtained with achromatic (gray-shaded) maps: 84.5% correct, averaged over all numbers of classes. Maps shaded with magenta proved to be the least satisfactory with an accuracy rate of 72.8%. The study provides cartographers with empirical guidelines regarding what level of map-reading accuracy might be expected for choropleth maps designed with a given number of map classes, in a specific hue, and displayed on a high- resolution graphics monitor.
    [Show full text]
  • Alternative Strategies for Mapping ACS Estimates and Error of Estimation
    Alternative Strategies for Mapping ACS Estimates and Error of Estimation Joe Francis, Jan Vink, Nij Tontisirin, Sutee Anantsuksomsri and Viktor Zhong Cornell University Cornell Population Center Seminar, Ithaca, New York, February 2012 Alternative Strategies for Mapping ACS Estimates and Error of Estimation Joe Francis, Jan Vink, Nij Tontisirin, Sutee Anantsuksomsri and Viktor Zhong Cornell Program on Applied Demography The beloved “long form” is dead. Long live the American Community Survey! As Eathington (2011) recently exclaimed, “Beginning in 2011, regional scientists and other socio‐economic data users must finally come to terms with major changes in U.S. Census Bureau methodologies for collecting and disseminating socioeconomic data.” Eathington’s proclamation holds all the more true for 2012. The American Community Survey (ACS) is now the primary mechanism for measuring detailed characteristics of the population at the sub-state level, and especially smaller geographies like townships, places and tracts. It is the main vehicle for disseminating information about educational attainment, occupational status, income levels (including poverty) and much more. As Sun and Wong (2010) write “Census data have been widely used to support a variety of planning and decision making activities.” Additionally, during the past decade there is increasing interest among demographers, economists, planners and regional scientists in mapping census data including the ACS. The main reason is that a map can show the spatial distribution of demographic data better than any other medium. Maps add another tool to the demographer’s analytic toolbox. Compared to the past, mapping has become an easier and more straightforward task. The widespread availability of desktop GIS systems and trained GIS professionals assures that an increasing amount of decennial, ACS, Small Area Income and Poverty Estimates (SAIPE) and other survey data will become mapped.
    [Show full text]
  • Geography 281 Map Making with GIS Project Three: Viewing Data Spatially
    Geography 281 Map Making with GIS Project Three: Viewing Data Spatially This activity introduces three of the most common thematic maps: Choropleth maps Dot density maps Graduated symbol maps You will learn how, when and why to create each type along with some of the pitfalls to avoid when making them. Most of the work will take place within the Symbology tab of the Layer properties form in the ArcMap Data View. In ArcMap Layout View, you will learn how to Set Relative Paths Modify legend text The first part of the activity guides you though the steps and decisions needed to create a series of maps illustrating the spatial relationship between city population, state population and area in the lower 48 states. The On Your Own part lets you apply what you've learned to create a new map that combines information from two previous maps. The data needed for proj3 is located in the \\Geogsrv\data\geog281\proj3 directory. Project 3 files: Description: Feature Type: counties48 shapefile base map of US counties polygon us48_major_cities shapefile Major U.S. cities point us48_states shapefile base map of lower 48 states polygon Visual Variables Maps use symbols to portray data. By selecting the right type of symbol you can create an intuitive map that is easy to read. Choosing the wrong symbol can mislead and confuse the map reader. Depending on the data type (qualitative or quantitative) and the type of feature (point, line, or polygon) you are mapping, you have many choices with regards to selecting symbol color (hue and value), size, shape, and texture.
    [Show full text]