Bolstad Chapter 4- Data Sources and Data Entry

There are many sources of spatial data which can be divided into 2 forms: hardcopy and digital. Hardcopy forms include any drawn, written, or printed documents and were the most common storage for spatial data until the 1980s when GIS came onto the scene in a major way. Digital forms of spatial data are those in computer-compatible format. Digital data are often from hardcopy maps that have been converted over to digital format. The chapter goes into specifics about the process of digitizing data.

This chapter also talks about different types of maps such as feature, choropleth, dot-density, and isopleth (or contour) maps as well as map scale and map generalization.

Chapter 19

Digital data

This is a summary of the Chapter 7 lecture on digital data.

There are 4 things we need to consider with digital data:
1. who produces the data?
2. is it vector or raster data?
3. what is the scale and accuracy of the data?
4. what is the content of the data?
* also, data is not usually in the shape and size you need. have to manipulate it for your purpose.

There are many different digital data sources:

* NLCD (National Land Cover Datasets)
*DRG (Digital Raster Graphic)
*DLG (Digital Line Graphs)
*DOQ (Digital Orthophoto Quads)
*National Wetlands Inventory (NWI)
*Digital Soils Data

*DEM (Digital Elevation Models)- these are really neat; raster data set, resolution is down to 20' b.c of recent flooding and the need to better map flood zones, each cell has a value of elevation, derived from LIDAR, it's the highest resolution data we've ever had for elevation-type stuff, *hillshade is cool*

*Buncombe County Digital Data?

* TIGER/Census data (Topologically Integrated Geographic......)

Chapter 13

Chapter 3 lecture

This lecture kinda blew my mind; it was the first time I'd ever heard the word datum before (that I know of). So a lot of terminology and concepts were new and somewhat intimidating. But now that I've done the ESRI tutorial, read some in the book, and gone over my lecture notes again... I'm starting to get it. It really did take 3 times of pouring over the same information, but in different formats, for it to sink in. The sinking in part is exciting though, and here's a very brief summary of some things, but not everything.

Geodesy is the study of the size and shape of the Earth. Sounds simple enough- but it's not! There are spheroids and ellipsoids involved (apparently the same thing) which are smooth mathematical models of the Earth, while a geoid is a geographic model of the Earth which approximates gravitational pull. Datums are a set of coordinate locations which have been measured horizontally or vertically and tell us the latitude and longitude of a set of points on an ellipsoid. Also called a reference surface. Some common ones are NAD 27 and NAD 83. Projecting maps is whole other monster which always involves distortion of one or all of these: shape, area, distance, and direction. Here in the US we are likely to use either the Lambert Conformal Conic or the Universal Transverse Mercator projections. Counties often use State Plane as their projection of choice. North Carolina only has one state plane, but many states have more than one. Now I need to study for tomorrow's test...

Oregon

Chapter 2 lecture

I'm going to rewrite my notes from class here to get things to sink in.

Data Models
On Monday we had a lecture on data models! These exciting things include objects in a spatial database plus the relationship among these objects. We work basically with 2 types of data models: vector and raster. Also with Triangulated Irregular Networks (TIN) but not as much.

Vector data models use coordinates (x,y) the "where" and attribute data (descriptive info about features) the "what" to define discrete objects- things with definite starting and stopping points. The three types of vector objects are points, lines, and polygons. Points might represent a light pole, gas wells, a tree, survey points. Attribute data are associated with each point and describe the non-spatial characteristics of the points. Lines might represent roads or rivers. They are usually represented as sets of coordinates. Attributes can be associated with the whole line, line segments, or nodes (starting and stopping points of line). Polygons can represent parks, buildings, or counties and are created by a set of connected lines. Attribute data can include perimeter, land cover type, or county name. (Representation depends on many things, including detail, accuracy, and use of data.) Vector data models are complex data structure defined by many x,y coordinates.

Vector data can be topological, which definitely doesn't mean topographic. Topology refers to the spatial relationships among features. It is a branch of geometry that I've not heard of before today. It makes up the rules that allow us to work in GIS. Words such as "connected, adjacent, and contained" are associated with topology. Computers need topology because they don't have experiences to make connections nor do they have nifty intuition so they need rules.

On to raster data models....they are simple data structure, work with jpeg formatting (digital photographs), involve continuous spatial features such as precipitation, elevation, and slope. They define the world in a regular grid pattern with a regular set of cells. Each cell has the same dimensions. "The phenomena or entities of interest are represented by attribute values associated with each cell location" (Bolstad 40). Usually each cell has one value; it is a minimum mapping unit and it cannot map anything smaller. If water and land exist in the same cell, the computer will decide which value to assign to the cell. The book explains that there is a trade off with raster data sets dealing with spatial data and data volume. Talks more on 41. Resolution and pixels come into play the raster data. Downtown Asheville is mapped at 6" resolution which apparently is very good.

Most data may be represented in either raster or vector models and can be converted between the two.

Triangulated Irregular Networks are good for representing surfaces, like elevation. Actually uses vector data generated from coordinates. Starts with points, then connects them to form a connected network of triangles. More on 50 and 51.

Data Formats
We use specific data formats to store and display information. Examples are .doc (text, images) .txt (text only, easier and quicker to open, don't need MS word). All have advantages and disadvantages. Spatial data formats are no different:

A geodatabase is a central storage location for all GIS data and relationships among them.
1. personal database (.mdb)
2. file based database (.gdb) *preferred*
3. scalable geodatebases (3)

My notes get fuzzy here, I should ask some clarification questions before I continue.