• Shurman chapter 1
• Shurman Chapters  2 and 3

Other

• W J 13 Interest in GIS
• W J 20 Geospatial Analysis and Environmental Justice
• W J  20 Green Maps
• M J 25  Green Map Project Ideas
• W J  27 Group Green Map Idea

• Group Reading Presentation “blank dominos” Mitchel Chapter 4

Group timeline Jack’s blog

Why map Density?

• emphasizes patterns rather than individual locations
• mapping areas of different sizes

When the location of each individual feature is mapped it may be difficult to distinguish how many are in a highly populated area.

Deciding What to map

Areas can be shaded based on density value

OR by creating a density surface

What kind of Data do you have?

1. density surface- uses GIS  to map the density of points or lines
2. density value- maps data already in defined areas like counties or forest districts

Do you want to map the density of  features, or feature values?

• features- are things like the location of businesses
• feature values- values like the number of employees at each business

Two ways of mapping Density

Mapping Density for Defined Areas

1. map graphically with a dot map
2. map density value for each area

Dot map

• each dot refers to specified number of features

each dot = five business

• dots are distributed randomly within defined area
• do not represent actual locations of features
• closer together dots mean higher density of features in an area

• This method is good for showing clustered features.

Density value= total# or value of features/area of polygon

• each area shaded based on density value

Downside: can see which areas have higher density but not specific distribution of density within area

Creating a Density Surface

created with GIS raster layer

each cell gets a density value based on # of features within a specified radius of the cell number of businesses per square mile

Data may be locations of features

OR data collected from sample points

Choosing a Method

Map density by area

• for data already summarized by area or to compare data within defined borders

– Trade-off : easy BUT doesn’t show exact centers of density

Create a density Surface

• for looking at concentration of point or line features

–Trade-off: more precise and shows centers of density BUT not as easy and requires more data processing

Mapping Density for Defined Areas

Calculate density values for areas

• Density value= total#  features/area of polygon
• pay attention to units, make sure that the density units are the same as the area units.

Note: density value fro each polygon area applies to the entire area and the actual density for any given point may be very different from the overall value.

Creating a Density Surface

GIS defines  a neighborhood around each cell

• based on an input search radius

GIS totals the number of features within neighborhood and divides this number by the area of the neighborhood = value assigned to each cell

This can also be done with  data values instead of features. GIS totals the value for all features within the neighborhood.

Important features that influence appearance of patterns:

• Cell size- how coarse or fine details of patterns appear

• Search radius

larger radius = more generalized patterns

smaller radius = more local variation ( but a really small radius may not show broader patterns)

• Calculation method

–simple method- value based on features within radius of each cell

• cells with no features within radius are not given a value

–weighted method- cell value derived from function that gives more importance to features near the center of each cell. Every cell is assigned a value even if it is very distant from any features. This results in smoother density surface.

• Units- choose units of appropriate magnitude for the size of the features being mapped

In order to create a density surface from data summarized by defined areas use: centroids- center points of the defined areas to create density surface.

Displaying a Density Surface

Graduated Colors

• custom class ranges- setting a range high and low value for each color
• choose a standard classification scheme

Common schemes include:

• Natural breaks- class ranges based on
• Quantile- each class has the same number of cells in it.
• Equal Interval- difference between high and low value is the same for each class
• Standard Deviation- classes defined by the number of standard deviations from the mean

More classes create a smoother looking map

BUT using too many classes may result in blending of colors making classes difficult to distinguish

Density surfaces are usually done in shades of a single color.

For classes defined by standard deviation shades of two different colors are used for values above and below the mean

In general,   Darker = “more” (higher density)  and  Lighter = “less” (lower density)

Contour lines

–connect points of equal density.

–show rate of change.

Contour intervals- specify how far apart in density value the line will appear.              Closer lines = more rapid change

Shaded area and contour lines can be used together on the same map to communicate both areas of highest density and rate of change

Looking at Results

• patterns on a map can vary depending on how a density surface was created
• in the process of making a density surface data is smoothed and generalized so that patterns stand out. Maps created this way are not meant to give detailed information about a specific location.

To create a map that compares the amount of electricity used by each building on the OWU Campus.  Additional information included in this map would be electrical appliances and facilities sucking power even when unused and some sort of visualization of the amount of coal it takes to power each building for a month or a year.  In order to determine the unused power sucking factors some field work would be involved first in determining everything that falls in this category from lights to unused but continually plugged in cell phone charger and there are probably other things that fall outside the usual spectrum of consideration like open ventilation hoods in science labs. This aspect of the project would also include some field surveys of academic buildings, campus offices, and dormitories.  We also thought that data from the campus power house might be available in regards to the energy used by each building.

Audience:

OWU campus including students, faculty, and staff

Format:

At first we thought that strategically placed large posters would be sufficient. But this project could also work in the collector card format discussed in class, where each building has a card with its power and coal usage stats in addition to the power sucking appliances that are the biggest unnecessary contributors.

Goal:

The goal of this map would be to raise awareness on campus of the unnecessary energy waste and to hopefully change the habits of people who can do simple things like unplug something or close the fume hood at the end of the day.

Reading:

Types of Geographic Features:

• Discrete- exact locations and lines
• C0ntinuous Phenomena- values can be measured anywhere and blanket the entire map. Examples: temperature, annual rainfall. Data collected from evenly spaced sample points and GIS interpolates values between these points. Can be presented as a continual surface or as boundaries.

2 Ways of Representing Geographic Features

• Vector- feature represented by a row in a table with x,y location. Lines are a series of coordinate pairs and areas are represented by closed  polygons. Analysis occurs in the table values
• Raster- features are represented by a matrix of cells. Each layer represents a different attribute. Analysis involves the combination of cell layers to create new layers with new cell values.  Appropriate cell size is important.

University of Victoria Community Green Map, British Columbia

Clackmannanshire Community Green Map Alloa, UK

http://www.opengreenmap.org/greenmap/clackmannanshire-community-green-map

Intro and Terms

GIS software has been developed for many different uses. Some of these are so different from the common understanding of GIS that their distributers may not classify their products a GIS.  The primary use of GIS might be to solve spatial problems with maps created as “an incidental but essential part of their operation.”

I found it interesting that information to make a GIS map isn’t necessarily collected by the GIS researcher and can be compiled from other scientific studies or government information like property deeds.

• A map of how many properties effected by addition of an extra highway lane
• A map that defines the boundaries of communities with varying health outcomes.

GIS is used in one to delineate definite boundaries of properties whereas the other maps “fuzzy” indefinite and changeable boundaries.

The developement of GIS and how it came to be what it now is can be traced back to a Canadian computer cartography system called Canada Geographic Information System.  From there developement of GIS occured in simultaneous projects in both the United States and United Kingdom.  Within developement of GIS there were initially two somewhatm separate motivations. One spawning from cartographers developing means of creating digital maps and the other from quatitative geographers developing computer programs to solve spatial problems.

The advantage of graphics created by GIS is that they are a means of displaying data that is more accessible than numerical means as images are found to be processed in decision making in a different way than numerical and written information.

“black box” GISystem

GIS is not one program or one method of data collection and presentation, rather it is a collection of programs made up of various algorithms and computer codes. The term “black box” delineates technologies that are frequently relied on and accepted but rarely questions as to their accuracy.

I found this section very interesting because it very likely refers to many ecological studies that utilize GIS software assuming that it creates infallible distinctions between areas.

GIScience

GI Science refers to geographers that analyze the premis and  accuracy of the information created by existing systems under varying conditions. Examples of GIScience Questions:  How well does one system work in a glacial as apposed to a wetland environment?  Does  it use vector (polygon) or raster (gridded) data?   How do different input parameters and measurements effect the resulting GIS analysis? and which version of data is better or more accurate?

Article Title: Using a GIS model to asses terrestrial salamander response to alternative forest management plans.

This study combines salamander abundance data with maps generated by GIS to predict the abundance of salamander populations in areas with different timber harvesting forest management plans. The findings of the study indicated that the frequency per decade that an area was harvested had a greater influence on salamander abundance than the pattern of timber harvesting or the distance a harvested area is from an unharvested area with high salamander abundance.

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WJ7-45817HC-6&_user=4990008&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1165254803&_rerunOrigin=google&_acct=C000062829&_version=1&_urlVersion=0&_userid=4990008&md5=b4884423240632d79aaba2b5590b2aa4

I’m a senior zoology major.  I’m very interested in environmental issues especially relating to conservation biology.  I currently live in the Tree House which is the environmentally themed SLU.

My URL is stinkyturkey which is a common name for the Hoatzin, a species of bird I saw in Ecuador on the Island Biology course trip.  I don’t have any special reason for choosing this name other than that for some reason it was the first thing that came to mind.

Hoatzin picture from:  http://www.complete-encyclopedie.nl/Dieren/Vogels/hoatzin/