Date-a-Scientist

The USD/CAD conversion rate is one of those things that I’ve generally watched since a kid with varying levels of interest and intensity.  Getting immersed in the concept of seeing money disappear as we converted cash into travellers cheques to get ready for a summertime journey to ‘the states’.

Over the last couple years (well probably since 2008) it has been much more in the forefront — something that gets an appropriate amount of attention given that we operate in Canada and USA — but the last couple of years have been pretty interesting to read commentary about Canadian currency being sensitive to commodity price.

So this morning I thought it might be a good exercise to test this theory with some data.  First step was to open an IDE and start reading about API’s to gain access to exchange data and oil prices.  Second step, stop and realize I was trying to solve a problem with my favorite hammer and this was not a nail.

The modern web has most of all data we need and it has already been compiled for us — a couple of searches later, data downloaded in Excel format.  This was a great lesson that the entry friction for acting on ideas in today’s content rich web is pretty low, data is readily accessible and readily usable, so low in fact that becoming a data scientist might just become the ‘norm’ the expectation any professional.

So what did the data show us?  Without much fuss or manipulation the trend between Oil Price and CAD/USD exchange rate is fascinatingly close, looking generally like a small lag (in weeks) between the two where exchange rate declines soon after decline in commodity.

cad_usd_oil

Bridge over the River B.I.

Getting the most out of your GIS data can sometimes mean getting most of your data out of GIS.  Understanding the limits of applications and using the right tools for the job are key to maximizing efficiencies and insights.

We wanted better integration between Tableau and ArcGIS so we did something about it recently by launching Integrated Portage, an application that easily feeds spatial and attribute data into Tableau.  Super fast data conversion that allows for all the analytics of Tableau to be applied to proprietary spatial data or custom spatial analysis results output to Tableau.

Pipelines: Colored by location Quality and Sized by Diameter for Lower 48

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Soil polygons: Colored by Slope category for East Coast Canada

soil_slope.png

 

Me and Folio Down by the School Yard

Gearing up for data analytics and social media scraping and decided to hit our blog to see how things trend and popular articles hits and views etc.  Taking a quick read through it was most surprising is that our post from 2011 on our Python-based Alignment Sheet Generator remains the second most popular article (besides the landing page).

We’ve continued to use and hone the product since 2011 onwards in various projects to build nice looking alignment sheets for clients.  Ever evolving, continuing to get better, performance improvements, workflow improvements, simplification, and a new name — Integrated Folio.

One notable addition to our Python-based alignment sheet generator is that it is now fully interactive for viewing sheets within ArcMap.  Super nice to be able to jump to a page and have it automatically draw all the necessary details.

We are now at the point where the layout and data content of the alignment sheet is defined by a simple spreadsheet!  Let’s be clear here, simple does not mean dumbed-down, it just means elegance and simplicity — there is no shortage of awesome automation in the product, supporting advanced symbology and labeling and graphical bands.

PODS?  APDM?   ISAT?  We don’t really care the source, if it can be shown along a route then we can use it — we’ve tried to build out the product as being an “Infographic for your Pipeline”.  I mean we aren’t in the era of mylar and tape any longer so why do we keep trying to pave that cow-path?

 

The New Python Window in ArcGIS Pro

New UI is great

ArcPy Café

ArcGIS Pro is coming with a totally redesigned UI, and the Python window is no exception.

First off: the Python window is meant to be a utility. It’s not most flashy of designs. You type in code and get feedback, it should help you do that and that is all. Our intention in the redesign wasn’t to get people super excited about typing code into a text box, it was to pleasantly surprise you with some helpful features and get out of your way.

The way it’s set up it a little different in Pro App. Here’s a quick tour of its design and features.

Split window

There are separate places for input and output

pywin_pro_mage1

The biggest obvious change in the Python window is that it’s been divided into two pieces: the input section and the transcript section. We found from a usability point of view, mixing the input and…

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Scheduling a script to run on a regular basis

A good reminder that not all things have to be scripted

ArcPy Café

This year at the user conference we had (once again) multiple user come by the island and ask if it’s possible to run a model or script on a regular basis, or at a prescribed time… the answer is yes. Click the link to the esri blog at Scheduling a Python script or model to run at a prescribed time for great detail how to do this.

Happy coding
-the arcpy team

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