Geographic Information Systems (GIS) evolved as a game changer in spatial planning. In a sense, it is the advent of this technology that changed the tables in the way development projects are designed. This was done by allowing new capabilities regarding the application of advanced mathematical and statistical tools, that, up to a few years earlier, would demand a full team of highly qualified statisticians, IT engineers and/or geographers to be reached. By all means, several of today’s mega-companies (mainly the ones that could afford the maintenance of such a high caliber personnel) did indeed care to invest in such a venture, in order to accomplish the following goals:
- A complete understanding of the human geography prior to the investment
- The comparison of several different locales that could host the new venture
- The visualization of statistical and anecdotal projections and their impact on the venture
- The a posteriori surveyance and the verification of prior hypotheses
- The refinement of statistical methods regarding future applications in different projects
As informational capabilities have grown exponentially and most of the mathematical and IT requirements posed by such undertakings, not only have the final costs been slashed, but also, the informational results have grown greater capabilities for accuracy and constant updating. Of course, a big part of the science’s evolution is owed to the philosophy of open data that abound in our day and age. For example, having official government data on population and median income and the arithmetics influx of foreign workers can help real estate agencies discover the next gold mine of properties that are, statistically, bound to rise in value.
Of course, having this as my first article in the Mapping Company website, I wish to set off with the description of a simple, yet powerful tool that can be applied without specialist knowledge. It is this timeless method of analysis that was used over several historical projects, up to the invention of modern Math and Computer Science.
The method in question is called Strengths Weaknesses Opportunities Threats Analysis. For shorter, SWOT.
With this method, the beauty lies in the simplicity. It’s pros and cons are apparent.
+Easy to apply
+Requires minimum expertise
+Relies on Intuitive interpretation of data
+Requires experience to be done properly
–Results are gross
–They rely mostly on the applicant’s view of the data
–Expertise is not applied on the data
–Fails to offer definite answers
As is obvious, SWOT analysis is the equivalent of a sharp stone when compared to the steam engine that are the more advanced methods of spatial analysis. However, one should not rush discard the method this easily.
For any project with spatial implications, a SWOT analysis is a necessary first step. Given time and budget, an engineer can produce a myriad radically different solutions about the development of a plot of land. If allowed to brainstorm, he can conjure solutions that will cost twice or thrice the budget, solutions that are too taxing on the environment or incompatible with the local customs and necessities. Since brainstorming paves the way for actual project development, the team needs an efficient, rational way to cut the bulk and allow them to focus on the fewer solutions that are for example, within budget, eco-friendly, and most profitable.
As mentioned earlier, with SWOT analysis, simplicity is key. The steps are as following:
- Place all alternatives side by side
- Decide (grossly) on the criteria that matter most for the venture
- Grade the alternatives in a small scale (1-3 or 1-5 is perfect)
- Find the sum of each alternative
- Cut from your analysis those that fail to pass the test*
*Hint: Figure the median value (x) and the standard deviation (s) of the scores and cut alternatives scoring less than x, x-s, or x-2*s.
So, as is apparent from the allocation of the criteria in each of the three general categories (financial, ecological, social), this organization’s main interest lies mainly in the financial criteria that are as many as the sum criteria of the other two criteria.
So, what do these results mean, and how are they to be interpreted?
The analysis can only by a longshot imply that the highest scoring alternative is the best one. As mentioned earlier, the results are highly inaccurate and do not, in any occasion, point to some definite result in the comparison. What can be told though, is that the two alternatives that scored the least rank lower than the median value (16) so they seem worse than the rest and, should resources be pressing, be crossed out (especially if further examination involves the costly process of collecting data).
So, this is SWOT analysis. Use it with caution and save time and effort.
Until next time,