1. MANAGING A GIS PROJECT

2. Starting Points for GIS: Do your homework: GIS, RS, GPS Get familiar with the terminology Gain general knowledge of spatial analysis: Georeferencing, Topology, and Map Projections and Coordinate Systems Talk to people who have implemented GIS View / Interact with working GIS projects

3. Getting Started with a GIS Project Begin at the End –what kind of outputs will be needed from the GIS –what kind of questions will be asked of the GIS Do you really need GIS or is DBMS enough Identify the needed resources –data, technology, people –do the resources exist for your project

4. Getting Started with a GIS Project Define level of accuracy needed –how to track: QA/QC/ metadata schema –costs grow exponentially with accuracy Prototype your project! –Take 5-10% of the total budget and prototype, from beginning to end, the project with a limited but varied subset of the data. –The problems that you head-off should more than compensate for the cost of the prototype. Complex system needs careful management –Mistakes cost money and time

5. COST Building a GIS from scratch is very expensive. The 90/10 Rule: 90% of the work gets you 10% of the solution. But it is that last 10% of the project that gets 90% of the reward. Unfortunately (or perhaps due to bad management), most projects run out of money just as they are about to move into the last 10% zone. Quality control is usually where people shave the costs. But at what expense?

6. Identify Needed Resources: Data Define data parameters –geographic extent –currency / time frame –geographic coordinate system and projection (one or more?) –which data model: raster or vector or both? –scale of the source data –grid data spatial resolution will you be mixing scales and resolution

7. Identify Needed Resources: Data Identify Desired Data Themes –if you want to implement specific models, you must collect data needed for input: USLE = A = R*K*L*S*C*P For each data theme, determine: –Entity model or continuous data theme –how will attribute information be encoded: ordinal (ranked), nominal (text), interval (numeric), ratio, unique –how will the data be used in the GIS? If data is used in a model, then it must be input in form that is supported by the model. Sometimes this can be recalculated, sometimes not. (High, low cannot be converted to exact values).

8. How to Obtain Desired GIS Data? Get it from another source Create it yourself / in house

9. How to Obtain Desired GIS Data? Get it from another source –How to find out who has wha t? Browse / Search online Contact likely producers –Free data vs. buying data –Hiring a consultant or firm to produce custom data

10. How to Obtain Desired GIS Data? Create it yourself / in house –point data with GPS –digitizing maps –scanning maps and photos Hybrid methods –post-process pre-existing data Interpolation: DEMS -> contour lines Geocoding vs. address matching Processing satellite imagery Cons: additional hardware / software / expertise needed Pros: ability to continue to create, edit your data

11. Identifying Needed Resources: technical Software Hardware –These were reviewed earlier in the lecture. The key is to evaluate these factors once you have a better sense of your project and your needed data resources. Network –large files + low bandwidth = poor GIS performance –particularly important for large-scale projects or distributed, online GIS systems

12. Identifying Needed Resources: People Working with GIS requires a combination of GIS-related technical skills and project subject skills. Need to strike a balance between the types of skills needed as they can provide checks/balances on each other. Very slow learning curve, even for technical people due to need to work spatially. Without an understanding of basic geographic principals, may not notice or understand logical errors or inconsistencies. Making use, not taking advantage of local GIS community

13. Identifying Needed Resources: People Possible roles for Consultants? –Prototyping the GIS project –One-time / specific tasks, like data creation, especially tasks that require expensive equipment and software or very specific expertise. –Reviewing the GIS project

14. Identifying Needed Resources: People What to Expect from Consultants? –Help clarifying the project goals, inputs, analysis –Info on data: availability, appropriate scales and formats –Ability to explain data models (vector, raster) and which data themes and attributes most suitable and how this relates to the queries and models that will be run in the GIS –Familiarity with one or more GIS software packages –Experience working on GIS projects from concept to completion

15. Evaluating the Quality of Spatial Data in GIS: Currency / Timeliness: is the data up-to-date? Completeness: any holes in the data? Consistency: map scale, metadata Accessibility: format, copyright, cost Accuracy & Precision: Sources of error in the data Sources of error in derived data and in the results of modelling and analysis * From: Burrough, P.A. and R.A. McDonnell, Principles of Geographic Information Systems, 1998

16. Evaluating Quality: Accuracy and Precision density of observations positional accuracy attribute accuracy - qualitative & quantitative topological accuracy

17. Evaluating Quality: Sources of Error Data entry or output mistakes Choice of the original data model Natural variation and uncertainty in boundary location and topology Observer bias

18. Pitfalls: what can undermine a GIS Project Inability to think spatially Lack of understanding the nature of error in spatial data and GIS. Hiring the wrong people or consultants. or Not training the staff. Lack of understanding of the nature of GIS: overestimating it, underutilizing it, not getting it. Failure to understand that GIS is a model of the real world not the real world. Underestimating the costs and needed resources, particularly data and staff.

19. Pitfalls: what can undermine a GIS Project: Failure to plan before the plunge. Not knowing when to cut your loses! More money won't make a bad design / bad software / bad data better. Failure to set boundaries / limits to extent of project Failure to set and adhere to timelines Failure to document data and processing procedures Failure to train users on how to use the final GIS product. Failure to share results of the GIS with entire org.