How to Grayscale ArcGIS Pro Vector Symbology

Most of the time, ESRI software is great, it does [mostly] what you ask it and as long as you aren’t doing anything too crazy it behaves. We all know that it has it’s ‘unique-ness’ about it, after using it for a few years you start to ask “why don’t they do this….” or “How comes I can’t do that…..”. Well, a lot of this is being addressed in ArcGIS Pro, already it has answered the question as to why we needed 3 different GIS software (ArcGIS Desktop, ArcScene & ArcGlobe) by bundling it all up into one package. Now (with 1.3) we are starting to see other features which we always wanted in ArcGIS Desktop coming into ArcGIS Pro, case in point, converting symbology to grayscale.

Today, I discovered while creating a basemap, that ESRI have implemented a couple of neat little touches, firstly RGB VALUES ON HOVER.

Hovering the mouse provides RGB values
Hovering the mouse provides RGB values

Although this isn’t ground breaking, it is a nice little touch which, for us cartophiles and OCD cartographers, provides a quick and easy bit of feedback.

The other discovery was having the option to grayscale the symbology. The new ArcGIS Pro can be a little tricky to get your head around, so it is understandibly not obvious but I went to change the RGB values on a piece of road and found another option: GRAYSCALE

Grayscale dropdown
Grayscale dropdown

Selecting “Grayscale” takes you to this menu:

Grayscale removes colour while retaining it's presence.
Grayscale removes colour while retaining it’s presence.

 

Okay, so this isn’t groundbreaking BUT having played with photoshop a little, I’ve found that the RGB value which is automatically given is almost a perfect match for what you get if you desaturate the colour.

What does all this mean? It means that you can easily and confidently convert your vector symbology to grayscale without guesswork! Creating alternative grayscale maps should now be a lot easier! Now, the question is, will this ever make it to ArcGIS Desktop?!

Dragons8mycat

Do your work right and you can be smart too

Originally published in xyHt

Ever since I saw the word phrase “smart city”, I have cringed. Not because of the term but what it alludes to. To me it says that we (geospatial experts) haven’t done our work right….let me explain

From Wikipedia:

“A smart city is an urban development vision to integrate multiple information and communication technology (ICT) solutions in a secure fashion to manage a city’s assets”

Now, my understanding, as a person who uses a GIS on a daily basis, was that a GIS was used to overlay and integrate multiple layers of information to gain insight and manage a project more efficiently….so, in reality, these two aren’t too dissimilar. In fact, when you look into it further, the [smart] platform is pretty much a GIS which links to live data and data which is structured to be interlinked [each data is linked to all the other data]…oh, of course, there is some form of asset management, usually in the form of a CMS [Content Management System].

Smart_City_GraphI guess my point is that I’m frustrated that many of us, geospatial experts, aren’t being “smart” with our data and hands up, at times I can be one of you. I download a load of data, put it in my geodatabase and don’t think twice about it until someone asks for it.

Here is a great example – I was working on site analysis of wind farms and pretty much the job involved loading in all the environmental constraints, physical and topographical constraints, overlaying them and finding gaps. The way it has been done for generations. Except I woke up one day and thought, “why am I doing this?”….and I looked at the data I was using and started to build a model (in ESRI modelbuilder) and what the model did was take all the files, spatial joined them (merging them with their attributes in tact) and then doing a few tasks to turn the gaps in the data into polygons. I then made a centroid from the polygons and THEN did another spatial join on the data using the nearby setting.

What I ended up with was a fully automated way to find the best sites for a wind farm and also report back (in spreadsheet) what the nearest constraints were. Over time I found there were other data I could build into it, like land use, Land Registry land type (freehold/leasehold) and even some analysis to provide slope, average sun, aspect. Yes, 3 years ago I was working “smart”….unfortunately too smart for the company as this new-fangled technology wasn’t as good as having somebody rummage through by hand to find the best locations (even though the best sites were the ones the computer picked!).

Let’s have a look at the principle behind this :

Knowing that we were trying to find areas suitable for wind farms, the area needs to be unbuilt land, have not within 250m of  a building, it shouldn’t be closer than 40km from an airport (though it could be), it shouldn’t be on anywhere too steep or next to an existing wind farm. Obviously it shouldn’t be in any of the environmentally sensitive areas.

Most of the data is open data –

Environmental constraints: Natural England

Wind farms: The Crown Estate and Restats

Land Registry land type: Land Registry

Land Use (rough):

Topography (buildings, terrain): Ordnance Survey vectormap, Strategi & Open Map

And (curiously enough) farms, restaurants, business parks and other points of interest were taken from my SatNav (extracted as a csv)

The model would then look a little like this:

WindFarm_Flowchart

But there are other ways to be smart

The former method uses a spatial join technique whereby features which lie in the same location are combined into a large dataset which can be interrogated. Another technique is to join tables of related information to enhance the data about location, this is quite commonly used in demographics but can be used anywhere.

A great example of this would be the neighbourhood statistics websites whereby they provide information about your locality…let’s have a look at how this can be done with openly available data:

If we download the Super Output Areas (average population approx 1000) from National Statistics, we can then join most of their data based on Super Output Area [SOA] ID

SOA_types
Output area types
ONS_Join
By joining the area code to the area code in the table we can extract informative data

As you can see, this can be used to create much more informative data, some software vendors might even call it “enriched” data and it is extremely easy to do.

….and then you realise that you can THEN spatially join this data to buildings, political boundaries, offices and all other types of data to extract SMART data about the locations.

My challenge to you today is to “enrich” the next data you use, if only for your own satisfaction, add some demographic data to it, add some wikipedia data to it, spatially join it with the INSPIRE Land Registry polygons (while you can)….go on, do it……that sense of satisfaction, THAT is why you do GIS.

 

Nick D