Introduction When I was at university studying GIS these three terms used to trip me up regularly when describing data. While all are similar, they have some distinct differences that you have to know when analysing data. Interpretation The first definition is interpretation. This is the first and most simple of the concepts being shown here. It is as simple and seeing what is happening in the data as it is. What are the trends or patterns being seen within the scope of the data available.
Introduction Having a large repository of spatial data is great but limited if you don’t have an effective way to display it. While tables and spreadsheets of data certainly have their place, especially around data people and engineers like ourselves, most people’s eyes begin to glaze over and any impact that your data could have is lost. That is why GIS professionals make use of apps such as dashboards and storyboards which can effectively convey not only data but knowledge or a story with it. Dashboards As mentioned above spreadsheets are great, to a degree. They contain a lot of raw data in a logical format but that’s about it. It is difficult to draw any meaningful conclusions from a spreadsheet as when it comes down to it, they’re just numbers on a page. This is where a dashboard comes into its own, the analysis of patterns and trends from your data. It does this through the use of graphs and indicators being presented in line with your spatial data. The only limit as to what kind of analysis you do with a dashboard is the extent of the data you are presenting. The applications of a dashboard are essentially limitless, if you have data, you can create a dashboard (in some sense) from it and draw more meaningful conclusions from it. Storyboards While dashboards are great for doing critical analysis of data storyboards fulfill a different niche whilst also presenting your data. Storyboards are used to tell a story with your data. Think of PowerPoint but instead of different slides you just scroll through the story. They can integrate maps, dashboards, images, videos and text all to enhance the data you are trying to convey whilst also being visually appealing to engage audiences both data driven and not. Proterra Group Examples Unfortunately, I haven’t had the opportunity to create a storyboard here at Proterra yet but there are some great examples available on Esri’s website. Dashboards, however, are Proterra Group’s specialty. See below for examples of some that we have created. Maps are a great way to visualise and disseminate your data to a wide audience of people in a short period of time.
Introduction One of my biggest pet peeves as a GIS Officer is when I have received data as part of a task only to discover it’s in a completely different format to what is required or not all the relevant associated files have been attached leading to a corrupted piece of data. GIS data can be a little tricky to wrap ones head around with all the different tech jargon that gets thrown around so here is a breakdown of the main data types and file formats they are found in. What types of GIS data are there? There are two types of GIS data that you will come across: vector and raster. These two data types represent different phenomena in GIS and are very different in terms of structure and operation. Vector data refers to more traditional geometric geographic data in the forms of points (data with an x and y coordinate), lines (data with coordinates and lengths) and polygons/areas (data with coordinates and areas. Vector data can be used to visualize almost anything from pipes to buildings to wildlife habitat areas or even customer/ratepayer data. Vector data also have attributes attached to it which can be used to further visualize different phenomena within the data. The other GIS data type which you will come across is raster data. Raster data is made up of pixels with different numeric values assigned to each pixel. The most common form of raster data is satellite/aerial imagery with the numeric value of each pixel of imagery giving it an RGB colour which builds the image. Other forms of raster data include slope/elevation datasets, groundwater/catchment modelling and lidar data. In general vector data is used to represent discreet data (data with a distinct start and end) and raster data is used for continuous data (data with in between values such as surfaces). What are GIS file formats? There are literally hundreds of file formats that GIS data can take the form of, so I’ll list a couple of the major ones here; Shapefile (.shp and associated files) – The original and one of the most popular vector data file types used in Esri software. Feature Class (.gdb) – A relatively newer form of vector Esri file format, must be in a geodatabase and has a better functionality/connectivity than shapefiles. MapInfo TAB (.tab and associated files) – The native vector file format for MapInfo, must the converted before being used in Esri software. Keyhole Markup Language (.kml/.kmz) – Commonly called a Google Earth file as it is used to display GIS data in Earth browsers such as Google Earth. GeoTIFF (.tiff) – One of the most common raster data file formats although any picture file format (.jpg, .png, etc.) are also able to be read if they are georeferenced. Why is data so important? As I have previously mentioned data is one of the main pillars of what makes GIS so making sure you are using the required data for the task you intend to do is important for maintaining accuracy and legibility. For example, some tasks just physically wont work with raster data as they do with vector data and vice versa whereas some will give you wildly unexpected/erroneous results (think tools crashing and data not working).
