W F 26: Delaware Data Inventory

Delaware_2008 and 2010 Ponds and Lakes (Ponds and lakes in Delco, was cool to see more in 2010)

Delaware_Address_Pts ( All the points where addresses are in Delaware)

Delaware_Annexations (Best guess is that this is land that was annexed by Delaware county from other cities, like Columbus, Delaware, Scioto or maybe areas that were annexed by those places)

Delaware_Archeological (Points in Delaware county of Archeological importance)

Delaware_Bench_Marks (GPS Benchmark coordinates throughout Delaware county)

Delaware_Building Outlines (Outlines of buildings in Delaware county)

Delaware_ Census_Biock (Population living within a census block)

Delaware_ Census_BiockGroup (Larger group of blocks for measuring population)

Delaware_ Census_ Tract (Looks the same as above but different color)

Delaware_Economic Development Layers (Shows different type of residential development, Two layers do not seem to make sense, not sure what they are)

Delaware_Farmlots (shows farm areas in Delaware county)

Delaware_Floodplain_1OOyr (100yr flood data)

Delaware_Floodplain_500yr (500 yr flood Data, shows area that would be effected)

Delaware_Floodplain_2009 (Have no idea why the whole map is pink? but think it is showing a flooding area of 2009)

Delaware_Floodways (Shows the major flood pathways)

Delaware_Historical_Local (Historical spots for Delaware or Ohio)

Delaware_Historicai_National (National Historic Sites)

Delaware_Hydro (Major bodies of water in Delaware, Great for our project)

Delaware_Hydro_Detail (Major bodies of water, including some ponds, and a freehand sketch of Delaware Run)

Delaware_Landmarks (Landmarks in Delaware)

Delaware_Master Point Coverage (I think this shows where all man made structures are in Delaware county, because there are many different types of structures)

Delaware_Municipalities (shows the different municipalities in Delco)

Delaware_Natural_Heritage_ ODNR ( Nature Sites? Points on the map)

Delaware_ Orthophoto _Detailed_2010 (Ariel photo of north and south Delaware county)

Delaware_Parcels (Parcels for Delco. Great for all projects to find additional data)

Delaware_Parks (Parks in Delaware, might be useful for bird projects)

Delaware_Places of Interest (as it implies, places of interest from police, to cemeteries, used this for hospitals)

Delaware_Precincts (Voting Precincts)

Delaware_Public Land Survey System ( I have no clue what this is, looks like polygon shapes in blue, no info in attributes other than coordinates)

Delaware_Railroad (railroads in Delco, good for projects that need some distinct visual clues to where the location is)

Delaware_Road_Center_Line (Center line of road, good for visual clues on maps)

Delaware_Road_RightOfWay (Shows roads that have Right of Way)

Delaware_School_Districts ( Shows regional school districts)

Delaware_Soils (Soil map for Delaware county, will be useful in Delaware Run project)

Delaware_Subdivision (all the subdivisions in Delco)

Delaware_ TaxDist (Tax ID for local taxes)

Delaware_ Topography (All the township’s topography for Delaware county)

Delaware_ Townships (The different townships in Delaware County)

Delaware_ Townships_Historical (Guessing historic names of the townships)

Delaware_ Watersheds_ ODNR (All the watersheds in Delaware county, how the water drains)

Delaware_ Wetlands (Shows Delaware county wetlands, which we hope to add to with our project)

Delaware_ Woodland_ ODNR (wooded areas)

Delaware_Zip_Codes ( Shows all the different zip codes in Delco)

Delaware_Zoning (Think this might be broken just a big square)

Ohio Wesleyan Parcels (OWU Land)

Watershed-Scioto (Watershed for the Scioto River)

W F 26: Tutorial ch. 16, 17, 18, 19, 20

Chap. 16:

Joining and relating data by attributes or location.

Chap. 17:

Creating features by location query.

Use in parcel mapping.

Chap. 18:

dissolving layers, clipping and exporting maps for analysis.

Explanation of tools toolbar.

Chap. 19:

buffering and overlaying data from attributes. Then creating graphics.

Chap. 20:

creating and combing raster surfaces.


M F 24: Tutorial ch. 10, 11, 12, 13, 14, 15

Chap. 10:

Works mainly with layout screen and creating final maps for presentations.

Chap. 11

Creating Geo data bases

Chap. 12

How to create new features.

Drawing features.

How to use the feature tool.

Chap. 13

How to edit modify and add information to features in the attributes table.

Chap. 14

All about geocoding

Chap. 15

Using the query tool.

Most important, that I had to come back to was creating features by attributes.  This is one of my favorite lessons and saved a lot of time creating new layers.



W F 19: Tutorial ch. 3, 4, 5, 6, 7, 8, 9

Chap. 3:

Displaying map data:

Worked in ArcMap: Basic concept was to learn layers and general settings.

Basic Navigation

Basic Searches and finding distances.

In this chapter we began working with attribute tables.

Learned basics of sorting and selecting features.

Chap. 4:

Used ArcMap to create a thumbnail graphic.

at step 25 had a crash.

Learned how to add data to a map.

Extended abilities with map layers.

created a finished map with legend.

Chap. 5:

Skipped this section on Online resources. (due to time restraints with work and midterms.

Chap. 6:

Teaches how to work with coordinate systems.

Importance of spatial data.

Chap. 7:

Teaches how to change symbols, colors, and additional layer options.

Found out how to add layers from attributes and create new layers.

Chap. 8:

works on classifying features and mapping densities.

Chap. 9:

Labeling features and using graphic labels.

M F 10: Mitchell ch. 6 &7

Chap. 6:

Mapping what’s nearby:

Basic concept, Using GIS you can find out what features are within a certain distance or within a boundary.

Useful to show affects may be had by event or activity.
Defining Analysis. (liquor stores sending out mails to everyone in 500 ft distance, or fire departments determining coverage.) Distance, Time, Money, basic statistical information.

3 Ways of finding whats nearby: Straight Line Distance or cost over a network. Cost over a surface.

3 Ways of finding whats nearby: Straight Line Distance or cost over a network. Cost over a surface.

K: Two layers to select one on the other layers. Idea of a network would be road directions on Google set up on a huge network.

Use feature to feature: finding specific areas between two points on a map.

Distance surfaces, EX: take a river and do a buffer zone at 10/20

Measuring Distance or cost over a network.
Calculating Cost over a Geographical Network.

**read over chapter 7**

Time series? Tracking time like a bird flying around town.

Tacking features like wildfires, magnitudes?, wind speeds. Showing patterns and features. ?

Calculates over two dates, or times? Showing how much has changed.

K: Data that changes over time, like county parcel data, subtract from today, compare that to a map two years ago, see how things have developed or gotten smaller. Look at when the parcel was divided.


W J 29: Mitchell ch. 2 & 3

Chap. 2


Mapping helps you decide where things are and how you define objects. This is important to help define patterns or other features that might stand out.

Importance of knowing what you are mapping, asking the right questions and retaining what information is valid and important to the project.

Understanding that everything represents a coordinate that exists at a specific location.

After mapping you can add categories. This will help define the data and group like values.

Chap. 3

Mapping the most and the least:

When mapping the most and the least it adds additional features to be able to gather data.

Importance could be used to find locations of business or number of employees in a certain area, this data in turn could be used to find weak spots in coverage etc..

First you would need to know what you are mapping.

Count and amounts are important and understanding ratios or relations between different features.

Then you can choose the type of map you would like to do,

Ranks are a way to put features in order.

Mapping individual values using classes:

Natural Breaks: This helps find groups or patterns

Quantile: All classes have the same number of features

Equal Interval: All cases have the same range of values

Standard deviations: basically all classes are defined by their distance from the mean value of all the features


This is the data that is the high and low values and can skew the data substantially.

One way of dealing with this issue is to put outliers in their own categories.

You could also group them with their closest counterpart.

Once you get all your values then you can map your data:

Pay close attention to colors, symbols, charts. This will make the data easier to read and understand.