<<<This picture sums up how I felt through most of the project.

I encountered a significant number of errors in the Idrisi GIS. Cause and effect issues were difficult to resolve. It seemed like everytime I did something there would be an error that I would have no idea how to fix and I would have to wait for a T.A. to fix the problem. A lot of the errors were caused by my my errors. Selecting the wrong file, or the wrong setting would cause problems as well as problems with the projection or coordinate system. After using Concat many of the problems were resolved. Using Concat would have been a timely resolution that would have saved me alot of time.

I did a lot of georeferencing to gather my data which causes problems. Google earth sometimes doesn't find a "hit" for the address. Therefore the data is incomplete because I am missing some points that I know should be there. This was a problem with my nursing homes, elementary schools, and daycares. I encountered another problem with georeferencing addresses which was the points wouldn't match up with my basemap due to projection errors or human error. I therefore had to clip the points that did not match up with the base map which lead to further data loss.

The target age population for this project included elementary aged children, and senior citizens. However when calculating the population density I included all ages. If I was to redo this project I would separate the age catigories to my target ages which would effect the results because the data would be more specified. Originally my population density was in meters. In order to convert area to square hectar I multiplied by 0.0001. I did this wrong about three times, because I did not know what the area was measured in. My conversions were wrong because I used the wrong amount of decimal places. This leads me to believe that there may still be error in my population density data.

My landuse data was reclassified based on what I thought would be suitable and unsuitable. The problem with doing this is that some areas could be suitable even though I classified them as unsuitable. Certain areas could be suitbale in some circumstances where as it could be considered unsuitable in other circumstances. For example I classified high-rise residential areas as suitbale based on population density when in some cases there may not be an area to put a new community centre. If I was to redo this I would have given the landuse weighted values instead of making it a constraint.

I would have liked to use other modules in my analysis but I felt that distance would give me the best output for my factors.

In addition to the factors I used there are other factors that could have inhanced the results to this analysis, for example: slope data, ethnicity, and income data. I would have liked to use slope data for Burnaby because you can only build on areas that are less then a certian degree. If I were to use other demographic data such as income or ethnicity, with further research I would have ideally noticed a trend in attendance at current community centres in Burnaby.

There are always problems when using contraints because there are no fuzzy boundaries. With my bus stop constraint I used a 500 meter buffer and anything outside the buffer is considered unsuitable. What makes 501 meters less suitbale then 500 meters? This can be a concern when you could be potentially eliminating a very suitable area right outside the constraint boundary.

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