Lab 5: Interpolation

 The lab provided insight into interpolation methods which include:

• Thiessen polygons
• Inverse Distance Weighting (IDW)
• Spline
• Kriging

Thiessen Polygon (a.k.a. Voronoi polygon) 

Thiessen polygon is proximity based interpolation which partitions an area by corresponding point's influence. This method of interpolation provides the means to better understand the distribution or the "points" area of influence.

Inverse Distance Weighting (IDW)

The IDW interpolation operates using the principle that the closer surrounding points are to a given location the more influence (weight) they have on the estimated value. The IDW is best used when the geographic phenomenon is spatially measured uniformly (i.e. relatively uniform distribution). 

Spline

The spline method like linear interpolation pass through the know points but applies a smoothing effect. The smoothing (tension) is adjusted to best fit a natural phenomena which is expected to smoothy transition over distance. 

Kriging

Kriging interpolation is a computational intense geostatistical technique which estimates unknown values at specific location based surrounding point values. There are different types of Kriging but generally is a powerful method which provide a prediction and insight into the reliability of the prediction. 

Spline Interpolation - Water Quality Tamp Bay (instructional purpose only)

Lab 4: TINs and DEMs

This was a multipart, lab which included an overview, analysis, and evaluation of data using triangulated irregular network (TIN) and digital elevation model (DEM). As one would expect the lab with adding and symbolizing data using local scene in ArcGIS Pro.

Empowered some basic ArcGIS Pro Local Scene buttonology, the lab walk through preforming a ski run suitability analysis derived from a DEM which is converted to a TIN. This analysis not only provide the skills needed to analyze 3-dimensional data in a local scene but reinforced past skills learn in previous classes by reclassifying and performing a weighted overlay analysis to obtain the final suitability raster.

Ski Run Suitability

The final exercise in the lab leveraged the Contour tool to create isolines from a DEM and compare the results to the isolines symbolized from a TIN of the same area. As you may see from the image below the isolines created from the DEM are dependent upon the resolution (cell size) which in this case produced lines where appear smoother then the more jagged TIN isoline symbols. 

TIN isoline symbol compared to isolines generated from DEM

GIS Jobs

GIS jobs come in many different shapes and sizes, ranging from specialist, analyst, developer, and administrator roles. I tend to look for positions that focus more on the development side of things. Development might involve web GIS, front-end or back-end work, or the creation of tools.

My strengths lie in free, open-source software (FOSS) GIS technology, but many GIS developer jobs specifically require C#, particularly those familiar with the ArcGIS Standard Development Kit (SDK). This requirement is so prevalent that there are approximately 20 such job listings for every one that doesn't require C#. Other important skills often mentioned in these job advertisements include JavaScript/TypeScript and Python, again emphasizing the Esri ArcGIS SDK.

The more software developer-themed listings often resemble other tech job listings, with a long list of required or desired skills that can take years to develop, only to indicate that the position is entry-level and has a corresponding salary range. This creates a minefield of embellishment or unclear descriptions.

Given my strengths, as I begin my journey to establish myself as an ArcGIS Pro developer, I tend to seek out companies whose primary focus is Federal contracts. Such contracts align well with my strengths as a developer.