We want these Locus values to reflect the m 2 lithic density as well. Look at the Loci values in the attribute table.It is a good idea to check rasters in this way to confirm that you have gotten what you expect. Get the ruler tool and measure the size of the grid square.Zoom in extremely close so only a few grid squares fill the screen.Zoom in close enough to see the raster resolution. Let’s change the Vector data layers to thick hollow outlines and put them on top so you can see how the Vector was converted to Raster. You’ve just created to different Raster files from GPS derived Vector polygon data. Save it into the /callalli/lithics/ folder under the name “SiteDens” Now you can create a Raster with the DENS value using the Convert Features to Raster… technique you just used with the Lithic_A field. Back in the Attrib table right click the field heading and choose “Calculate Values…” and then type in the value (1) of the density per m 2 for the Site_A in the DENS= field. Now fill that column with the density (per m 2) of the Site_A raster value mentioned above.Create a Short Integer field named “DENS”.Output Raster: Add Field… at the bottom of the table Make sure nothing is selected and go to Spatial Analyst toolbar > Convert… > Features to Raster….With the GPS running I walked around different densities of artifacts I perceived on the ground. These data were gathered using a Mobile GIS system. Display Lithic_A using a density choropleth: Right-click on Lithic_A > Properties… > Symbolize and choose Show: Categories, Unique Values.Display the data layers by bringing in both feature classes with Site_A underneath Lithic_A.You should write down these densities for later reference below. Low = 3 flakes / m 2, Med = 7 flakes/m 2, and High = 15 flakes/m 2. Here the density is determined from the C1_DENS field. For the purposes of this demonstration all the sites in Site_A have a minimum density of 1 flake per square meter. Start a new map in Arcmap and load two themes from the Callalli geodatabase.This exercise works with two different scales of raster data. Working with raster data is less intuitive than vector data for many users. The GoogleEarth and imagery, the Landsat scene, and the SRTM elevation data were examples of raster data. You’ve already worked with Rasters in this class in previous labs. With Raster data we are moving beyond the simple data management, cartographic applications of GIS into the more interesting realm of theoretical and analytical problems that you could not accomplish without a computer. Data is organized logically into layers or surfaces. Generally the rasters are thematically simple: one attribute value is shown varying across space. In archaeological applications a raster theme might include all artifacts of a particular class, such as obsidian flakes, across a space, or all the occupation areas from a particular time period. Examples include: elevation, vegetative cover, temperature, and barometric pressure. This data model is suitable for working with themes that consist of continuous values across spaces. Apply basic raster surface analyses to terrain mapping applications.Continuous data is represented in the form of cellular or “gridded point” in a GIS.Understand the concepts and terms related to GIS surfaces, how to create them, and how they are used to answer specific spatial questions.Describe how local, neighborhood, zonal, and global analyses can be applied to raster datasets.Explain basic-single and multiple raster geoprocessing techniques.Describe the concepts and terms related to the implementation of basic multiple-layer operations and methodologies used on vector feature datasets.Describe the concepts and terms related to the variety of single-overlay analysis techniques available to analyze and manipulate the spatial attributes of the vector feature dataset.Determine how vector and raster data models, satellite imagery, and aerial photography are implemented in GIS applications.Two primary data models are available to complete this task: raster data models and vector data models. Data models are a set of rules and/or constructs used to describe and represent aspects of the real world in a computer. In order to visualize natural phenomena, one must first determine how to represent geographic space best.
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