Goals and Objectives:
The goals of this exercise were set to familiarize students with various web sources which can be used to obtain and download data for mapping. Through the completion of this exercise, students will demonstrate their ability to locate and download data from various web sources, import the data into ArcGIS, use Pyscripter to project and clip the data within the program, and finally, build a geodatabase for the purposes of storing the data.
General Methods:
Data was collected by download from a total of five web sources, including:
The US Department of Transportation- Bureau of Transportation Statistics
The US Geological Survey's National Geospatial Program- The National Map
The US Department of Agriculture- Geospatial Data Gateway
The US Department of Agriculture- Web Soil Survey:
& The Trempealeau County Land Records Department
Once the files were obtained, it was easy to unzip them and begin building the Trempealeau County geodatabase in ArcCatalog. A designated folder was created for each of the data sources and filed appropriately in the exercise folder. These files would be accessed for clipping to the county outline later within Pyscripter (for more on this, please refer to "Blog Post 2: Using Python Script"). Put together, the data contained a total of three raster images for the county. These, plus an additional reference vector county outline containing recreation trails, are pictured in Figure 1 below:
Figure 1: Trempealeau County Data Downloads
Data Accuracy:
---
|
Department
of Transportation
|
USGS:
Landcover
|
USGS:
Elevation
|
USDA
Data
Gateway
|
USDA
Web
Soil Survey
|
Trempealeau
County
Land Records
|
Scale
|
1:24 - 1:100
|
-- |
-- |
1:100
|
1:12,000 |
-- |
Effective
Resolution
|
-- |
1 arc second
(~30 m)
|
1/3 arc second
(~10 m)
|
-- |
400 DPI |
-- |
Minimum
Mapping Unit
|
2-5m |
5 pixels |
-- |
5m |
-- |
-- |
Planimetric
Coordinate
Accuracy
|
-- |
-- |
-- |
-- |
10% or .01inches |
-- |
Lineage
|
Federal Railroad Administration |
USGS National Landcover Database 2006-2011 amended in 2014 |
USGS 2006-2011 amended in 2014 |
Metadata imported, Dataset copied |
||
Temporal Accuracy
|
N/A |
-- |
-- |
--
|
-- |
|
Attribute Accuracy
|
-- |
Unknown |
-- |
10-20 / 85
| -- |
Table 1: Determining Data Accuracy
Conclusions:
From the Data Accuracy table pictured above, one is able to make judgments about whether or not the data is reliable for use after assessing the data's temporal and attribute accuracy, as well as it's lineage. The temporal accuracy will denote as to whether or not the information was collected at a date reasonable enough for current use. Attribute accuracy determines whether or not the information displayed in the attributes or symbology is true to that of the real world. Finally, the lineage provides information as to the source of the data. All of these must be taken into account to first determine if a data set is credible enough for use.
The next question to ask is whether or not the data set is able to be used for the desired mapping variable. This can be determined based on the scale, effective resolution, and minimum mapping unit of the data set. The scale of the data must be set to a reasonable level for the focal area. Based on the scale, the minimum mapping unit will determine the smallest size data that can visually be plotted on the map. For example, the USGS National Landcover Database reported in its metadata to have a minimum mapping unit of five pixels, anything below this amount would not be able to be effectively visualized on the map. Finally, the effective resolution determines how clearly and accurately the data will be visualized on the map.
