Notice that what you have is a single raster surface representing a theme 'Lithic density' from different GPS derived files.

Since Sites are bigger than Loci spatially, we want SiteDens at the bottom. Also, the order of the GRIDs in the list is important, so the GRID1 is on top of GRID2 in the output. Unlike the Mosaic command you used previously, the Merge command does NOT blend the GRIDs together and the cell values remain discrete. In this case, GRID1 should be 'Reclass of LociDens and GRID2' is 'SiteDens' Use the Merge command to put these two rasters together.Īnd so on, substituting the GRID names for GRID1 etc.

In Spatial Analyst > Raster Calculator….

Now we want a single continuous surface from the Site and the reclassified Locus GRID files. The default results in a temp GRID file called “Reclass of LociDens”, which is fine for now. The actual m 2 values were mentioned above: Low = 3 m 2, Med = 7 m 2, and High = 15 m 2. Recall that 1 = High density, 2 = Med density, and 3 = Low density. We will use the Reclassify command to change these values into m 2 values that we want. 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. Continuous data is represented in the form of cellular or “gridded point” in a GIS.

Raster and vector data model in gis