GIS (geographic information systems) is a way of presenting and analyzing the physical world around us. It gives us practical insight into complex spatial relationships; how the state of vulnerability can be tied to income or education and how disaster risk may be amplified based on these factors. At its core, GIS is a way of understanding the world around us through the attenuation of multiple data sources expressed in terms of their cause/effect/indicative spatial relationship.
GIS and the understanding thereof has implications for all decision-makers, from town planners and logisticians to someone planning a holiday road trip or dropping kids at school. GIS has (rightly or wrongly) gained a stigma of being elitist and the domain of only the professionally trained practitioners. In a recent essay arguing for maintaining professional ethics in GIS, I state that professionalization of the profession "is imperative to ensure high standards of accurate and objective analysis and data depiction". Yet while this may be seemingly contradictory and while I must acknowledge that there are significant pitfalls of analysis being conducted by individuals that are not technically competent, the outputs should become more mainstream in their utilization by the non-GIS professionals and move away from flat images/maps, or black box data portals.
Outputs should be interactive and act as part of the suite of decision-making tools by allowing for spatial analysis and engagement with the data while still maintaining spatial and relationship integrity. I believe this is the next evolution of GIS as a profession. No more black boxes, no more basic data overlays, but rather a simple structured framework for data engagement.
To prove that this is indeed possible for those that lack the programming skill that this evolution seems to imply, I've designed a few simple examples of this being done in good old MS Excel. Each of these examples is varied in terms of underlying data complexity but will act to fulfil a different but specific function.
Disclaimer: all the data values and status depicted below has been randomized to protect personal information and project rollout. The information should be considered as theoretical only and acting only as a proof of concept.
Example 1: Design Status of Water collection Points under the "Day Zero" scenario.
The first step is to have a normal status in a table. In this example the data consists of Latitude and Longitude, population served, design priority and design status for each of the anticipated water points.
This is not particularly extraordinary on its own, but when power view is enabled it enables the spatial component of this data using the power of the bing mapping platform. Power view is simply the third component of power pivot and power query.
These data are then displayed spatially that'll highlight the distribution of water points (is there sufficient coverage?), the estimated population served at each water point (will there be sufficient taps and space to effectively and efficiently serve these people?), the priority in establishing these water points (is there an equitable rollout of water points?), and the water point design status as depicted by the colour of the dots on the map (are we achieving our objectives in the design stage? what is the progress?). This information can be filtered down in much the same way as excel auto filters do to provide additional insight into project status.
This very simple example has some basic functions like zooming, panning, filtering and displaying attributes and yet it is designed without any GIS application. It should be used to show and query "spatially" the project status and where intervention may be needed and makes reporting more interactive. Software like Power Bi also has functionality like this.
Example 2: Risk data fly through.
Being able to display spatial data tangibly is the key to get decision-maker buy-in. There are few examples where this could be more pertinent than in the field of disaster risk assessments. Risk assessments are meant to provide large overviews of a district, municipality, or province/state. They take various datasets such as demographic data, known capacities such as hospitals or schools and analyse these against previously occurring hazards in the area. Each of these relationships is difficult to comprehend but will have large ramifications to the areas based on those decisions made. It is therefore imperative that those making the decision can engage with the data in a way that truly breeds understanding.
The following example is a fly-through of theoretical risk data, highlighting different spatial scales of analysis, different forcings to the risk equation and the components of which these comprise. All analysis was done in MS Excel, the visualisation was done in the 3D mapping native to Excel from 2016 onwards. The only GIS work undertaken was gathering and checking the boundary polygons. These polygons are then imported into Excel for linking to the analysis data and for display in the 3D mapping.
While not possible in the youtube video. The fly-through in the excel file can be paused, panned and zoom to the area of interest and the data is engageable to see the attributes.
Example 3: Hybrid GIS and Excel mapping.
The last example is if there were indecision on the locations of water distribution point interventions. Waterpoint planning was very much required, and the ramifications for those points were very much dependant on the locations in which they were situated. The locations determine the characteristics of the population served, the amount of water needed, the opening hours needed, the ability to get water to the locations. Decisions of this nature would require consideration of many factors when deciding on the locations of water points and would result in the significant back and forth between stakeholders.
GIS, as amazing as it is, functions best when data is static, or where outputs are given as defined scenarios and analysis. Outside of model builder or scripting, GIS does struggle on dynamic analysis. So in the case of stakeholders asking to "see how X changes things", the dynamic nature of excel has an advantage.
This work uses GIS to bring in land use over a small domain, demographic data and create a 100m x 100m mesh grid. Attributes of the land-use and demographics are interpolated to the individual grid cells. The locations of the water points will then dynamical associate themselves with these cells. This in term determines the requirements for each water point.
Like the previous video, the fly through in the excel file can be paused, panned and zoom to the area of interest and the data is engageable to see the attributes, but more importantly, changing the locations of water points will update all the water point population characteristics allowing for optimized decision making.
While these options may not have all the tools normally associated with traditional GIS packages, the ubiquitousness of and the familiarity with which people engage with Excel lends it to be an option that could seriously be considered, if not for the spatial analysis potential, then at least for display and interrogation purposes.
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