A friendly introduction to Geographic Information Systems Marc Albrecht University of Nebraska at Kearney Department of Biology Context: Why GIS?  Many of the issues in our world have a critical spatial component! – Land management – Property lines, easements, right of ways – Data on land values, taxation, assessment – Business site selection, advertising – Proximity of ‘our’ land to other facilities (pollution, hunting, municipal, federal, state)  “I don’t know what’s over that hill” is a common problem. What is adjacent to the land we are using? The Space on Earth  The Earth is finite! – If not now, within our lifetimes there may be no natural ecosystems. – Land managers, natural resource workers, and politicians are and will continue to make decisions about biological systems. – Good information and tools are needed to do this. Enter GIS  A computer-based tool for holding, displaying, and manipulating huge amounts of spatial data. Outline of Presentations  “What is GIS?” Presentation – Part I: Maps and Mapping – Part II: Some GIS Operations  “GIS Resources and Projects” Presentation – Part III: Where do I get data? – Part IV: Ongoing Programs, other software – Part V: Project Ideas and Examples Part I: Map Concepts  What is a map? – What are some properties of maps? – Vector vs. raster: two digital mapping methods Maps reflect the databases we create  Mapping the third dimension: examples of 3-D maps  Representing the World: Projections  3-D to 2-D (at first) – Projections change a round world into a flat one. What is in a picture?  Example: The Mercator projection has straight meridians & parallels that intersect at right angles, as opposed to the Robinson projection. – Mercator preserves area only at the equator and at two standard parallels equidistant from the equator. – The Mercator projection is often used for marine navigation as all straight lines on the map are lines of constant azimuth. – Any one projection cannot simultaneously preserve all these qualities of the world: shape, area, direction, and distance. Projections and Metadata     There are dozens of types of projects, and about six that are commonly used. People choose a projection based on which qualities they most want to preserve in a map. Sites such as Dr. Dana’s and National Geographic discuss projections in more detail The point is that you need to know where your data (maps) come from and information about it. This is called METADATA – data about the data. Good metadata includes who collected the data, when, to what accuracy, how the data are projected, and the collector’s contact information. You should be a responsible GIS user and keep track of your metadata! The Projection Problem  When working with GIS systems you have to know about projections in general and what projection the different data you are using are in. – This is a metadata issue again. – It is possible to transform data from one projection to another, but is easier – especially when starting out – to have the different data layers in same projection when you obtain them! Agencies are generally helpful about doing this step for you if asked.   Another Problem are Datums – basically mathematical descriptions of the Earth’s size and shape. If either the projection or the datums of your map layers are not identical: YOUR MAP LAYERS WILL NOT LAY ON TOP OF EACH OTHER, BUT RATHER BE SHIFTED INTO DIFFERENT AREAS!! This is embarrassing and frustrating. This is what happens when projections mix!   Notice the boundary lines do not line up Points that are placed on the wrong projection will be misaligned as well Raster vs. Vector: types of GIS map representation     Vector vs. Raster Two basic ways that spatial data can be represented Raster: – Data represented by pixels with values, creating a grid – Allows certain types of operations not possible with vector data – Map algebra is possible with multiple data layers – creating index maps Vector: – Data stored as points, lines, and polygons – Uses less memory than raster format – Does not loose positional accuracy How is all this done?  GIS stores data in a relational database structure (‘3-D spreadsheets’) – e.g. employee names linked to store number, store number linked to shipment arrival – any data can be linked by a common attribute to any other data  Example shown here is a list of counties (geographic data) by income code (demographic data) High End 3-D Representation   Surfaces are made from Triangular Irregular Networks (TIN) that interpolate 3-D surfaces from 2-D contour values. Uses: – Hydrology: surface and underground flows – Line-of-Sight analysis – Pollution Plume tracking – Customer analysis – Soil erosion potential 3-D Rendering Example Elevation measurements can be easily converted into 3-D. Such elevational data are collected regularly by federal and state agencies.  These data can be downloaded/ ordered at little or no cost. USGS 7.5 Minute quad in 3-D Beaty, NV A 3-D rendering of the terrain  How many data points are contained in this image? Thousands? More? – Even without statistical measurement (which can be done) the pattern of pollution can be seen. Location and density of wells is also clear. – Line of sight analysis allows us to determine where to put a house or power plant where it could or could not be seen from major roads. Notice the roads actually track up the hills on the right side of the image. Part II. What can GIS do?  Some general types of GIS operations are listed on the next few slides – Many more are possible than are shown here and more are being created every day – Extensions and scripts created by users (http://www.esri.com/arcscripts) – Third-party and government developers make software plug-ins for specific uses (eg. EPA’s BASINS software) 1. Proximity Analysis    Two or more data layers are overlaid GIS creates buffers around features on a particular layer This allows analyses such as flood zone delineation and features near a route such as hotels along a bike route. 2. Query and Overlay Analyses  Query building is a data exploration operation – Example statement: ‘([acres] > 500 AND [age] > 55)’ – This would highlight all land parcels of greater than 500 acres owned by people older than 55 years old in a data set loaded into the GIS.  Map algebra with raster data, in this type of operation mathematical operations are done on each pixel of multiple data layers. This results in a new data layer that is calculated from all the input layers. 3. Spatial Analysis   Raster data can also be used to create surfaces Other raster data uses: – Density analysis – Proximity analysis – Least-cost paths – Line-of-sight – Hydrology analysis Part II: Data Examples  Here is Atlanta – Highways – Roads – Census Tracts  Close up of downtown – Map contains data for each street – Each address in the city can be geocoded – that is its location estimated in a systematic way – Length of each street segment - block – Streets can be sorted by length, name, income, home value, race, age - all provided by the Census Bureau (TIGER) Atlanta Example  Hypothetical population of opossums. – Data can be sorted by attribute, such as sex, females are yellow in this example – Hmmm, why are males found closer to populated areas? – We do not know – but how else would we discover the pattern? Atlanta  Same population now reclassified by some other attribute. – a genetic marker? – age?, size?  Other operations: – I can make a chart of any of the attributes. – I can compute density of points to see where the animals are most clustered – Measure distances between individual locations     Thanks for viewing this presentation. Please email me with any comments or questions you have. If you want to learn more about doing your own GIS projects, and more capabilities of GIS, please view the presentation “GIS Resources and Projects” in this folder. A number of the images and figures in this presentation are reproduced with permission from the ESRI website. Please visit there to learn more! Other images used with the permission as stated of Peter H. Dana, The Geographer's Craft Project, Department of Geography, The University of Texas at Austin. All commercial rights reserved. Copyright 1995 Peter H. Dana.