Avid Geo, Boston’s #1 social spatial group, is having it’s January meet-up on the 24th. This month’s activity is a OpenStreetMap MassGIS building import. For all the meeting details check out the group’s Meet-Up site. This crew knows how to have a spatially good time!
Geo/spatial/location/GIS is everywhere now-a-days. That is awesome. No longer is what we do a specific niche that is only found in a small set of industries. Openstreetmap, location aware devices, dropping pins, Google maps – everywhere you look geo/spatial/location/GIS matters. Heck, even Gizmodo has a “Maps” tag.
This ubiquity is both a blessing and a curse. Thanks to the every growing understanding of what we collectively do there has been some abuse of a few keywords throughout the geo-world (some of which I am guilty of). For 2013 I think we should retire a few terms to keep us (me) sane. Here is my short list.
1. Heatmap -Heatmaps are one of my least favorite cartographic representations of data across space. I love density maps, choropleth maps, and interpolated surfaces, but misrepresenting data for the sake a cool map is a giant pet peeve of mine. Let’s make a conscience effort in 2013 to stop people from using the term heatmap to describe any map with bright colors on it.
2. Cloud – We get it. Enough already. To the Cloud!
3. Analytics – Overused and abused term #3. What are you actually analyzing? I love identifying and understanding patterns across space, it’s what I do everyday, but let’s lay off using the term analytics to represent any type of math or stats done on a spatial dataset. To me analytics involve higher level operations, whereas I think people often use the term to represent basic stats.
4. Big Data – You have big data, I have big data, we all have big data. Question. What is big data?
These opinions are mine and mine alone. Are there any geo related terms that you think have been overused in 2012 and need to be retired in 2013? Leave a comment. This could be fun.
The folks from the Esri Dev Meet-Up Team (@AmyNiessen and @geeknixta) will be back in town on February 7 for a winter Esri Dev Meet-Up at the Bell in Hand in Boston. I’ve been to a few of their events over the past couple years and they are always a good time. Usually there are a few good GEO and GIS presentations and Esri provides some food and drinks (nice!).
If you are in town and are available check out the event. Details are available at the Meet-Up site.
I am no fan of geographic heatmaps, especially those that are really just density maps or poorly interpolated surfaces. To me the term “heatmap” is just an overused marketing term. So, when I came across this “heatmap” today I had to laugh:
“Rental Heatmap” from Boston.Curbed.com
And here is a screen grab (in case they edit their page!).
There is no heatmap here, at all (unless I am totally using this page incorrectly, which isn’t out of the realm of possibility). No doubt, it’s a slick web-map application that really aids the story, but let’s call a spade a spade.
Side note: Is the term heat map so popular because choropleth doesn’t sound cool?
One word: GeoAwesome. That is how I would describe tonight’s (11/14/2012, GIS Day!) Ignite Spatial Boston 4, which was organized by Avid Geo, hosted by the Center for Geographic Analysis at Harvard University and sponsored by AppGeo, Axis Maps, NBT Solutions, Azavea, and CDM Smith. The twelve, five minute talks covered a variety of geo-topics from analysis to workflow, apps and technology. It was a really well-rounded night of topics. I thought all the speakers did an excellent job. Here is a real quick rundown of the talks:
Crowdsourcing Boston’s Neighborhood Boundaries – Andy Woodruff, Axis Maps. Great idea, great application, great participation, and great execution. I can’t wait to see what they do next. Check out Bostonography for more.
Save your Mouse: Automate your Spatial Workflow with PostGIS – Jesse Bishop, Research Associate, Woods Hole Research Center. You know you’re onto something when you are saving yourself literally millions of mouse clicks. Great project and great technique. Really opens up your schedule!
Visualizing the Urban Metabolism of Neighborhoods – Dr. David Quinn, urbmet. This was one of several talks that discussed a geospatial analysis that was data intensive, innovative, and had great visualization component. Dr. Quinn did a great job of breaking it all down in five minutes.
Trafficked by Sea – Stacy Bogan, Center for Geographic Analysis, Harvard University. Presented a unique approach to modeling ocean traffic networks. During her talk she proved that projections do matter!
Geeky Boating Elk, Ryan Westphal, Lead Developer, jQuery Geo Project. Great presentation, taking a totally wild and creative subject, and using it to demonstrate the power or jQuery Geo.
GeoHOLLIS: Mapping the ILibrary Catalog – Bonnie Burns, Harvard Map Collection. I used to work in a map library so I can really appreciate this project. So far they have geo-cataloged 12.5% of their 12 million records, allowing patrons to search their catalog geographically.
Fontly: Mapping the World of Vintage Typography – Brendan Ciecko, Founder, Fontly. I was really impressed with this talk. Check out Fontly and contribute to the project.
Estimating Sandy – Ben Spaulding and Boyd Zapatka, AIR Worldwide. My co-worker and I gave this talk about the work we have been doing the past couple weeks. It’s amazing how much geoscience you can jam into five minutes.
Python as an ETL – Mark Zito, GIS Specialist, CDM Smith. Mark demonstrated a few workflows using Python as a geospatial extract,transform and load tool. I need to ask him about getting one of his code samples.
Partly Cloudy: Real World Tales of Geo Migration to AWS – Michael Terner, EVP, AppGeo. Michael shared AppGeo’s experiences about moving to the cloud. Really focused on the benefits, which I thought were right on the money (he talked about cost savings, if you didn’t get my pun).
Making Sense of 500 Million Location Requests Per Day – Richard Sutton, Geospatial Lead, Skyhook. I think everyone who was in the room (100+ people) wanted his data and databases after he finished his talk. I can’t describe how incredible the data and analysis he described are. The potential for this data is unbelievable.
Cartographic Ingredients from the Eye Candy Kitchen – Jeff Blossom, Center for Geographic Analysis, Harvard University. We often forget the basics that make a map a pleasure to gaze upon. Jeff did a great job of breaking down the basic ingredients all cartographers need to remember when creating a map.
A big thanks to Guido Stein and the Avid Geo crew for bringing us this event. There is a strong geo-community in Boston and events like this really bring it to the forefront!
Recently I posted some tips and example script to measure distance between points using SQL Server. The obvious extension of this script would be to generate the lines between two points. I wanted to create a script that took two points from a table and using STLineFromText to create the line. The vast majority of the script is the same as the previous post, with the exception of the line to create the lines. So, here it is:
select t1.Id as ID, t1.NAMEASCII as t1_Name, t1.SOV0NAME as t1_SOV0NAME, t1.Latitude as t1_Latitude, t1.Longitude as t1_Longitude, t2.ID as t2_ID, t2.NAMEASCII as t2_Name, t2.SOV0NAME as t2_SOV0NAME, t2.LATITUDE as t2_Latitide, t2.LONGITUDE as t2_Longitude, Geography::Point(t1.Latitude, t1.Longitude, 4326).STDistance(Geography::Point(t2.LATITUDE, t2.LONGITUDE, 4326)) as Distance_Meters, Geography::STLineFromText('LINESTRING('+cast(cast(t1.longitude as float)as varchar)+' '+cast(cast(t1.latitude as float)as varchar)+','+cast(cast(t2.longitude as float)as varchar)+' '+cast(cast(t2.latitude as float)as varchar)+')', 4326) as line from dbo.Populated_Places t1 cross join dbo.Populated_Places t2 where t1.SOV0NAME = 'United States' and t2.SOV0NAME = 'United States' and t1.ID != t2.ID and t1.NAMEASCII = 'Boston'
A few notes about this script:
Ok, that’s it. Pretty simple and pretty fast (the example query takes two seconds to run). Perhaps I’ll put together a simple script to string together multiple points into a line or multi-line object next!
I noticed earlier that my post about last year’s major New England storm was getting a ton of action today. It’s probably time to get some info up about Hurricane Sandy.
I’ll keep updating this page when I come across data sets or mapping sites with pertinent information about the event. If you have a good link post it in the comments section!
Last updated 11/4/2012
Storm Mapping Sites
Hurricane Sandy Data
Twitter/Social Media Mapping
Power Outage Sites
I’m in Somerville, Massachusetts and the wind has been whipping all day. If the power stays on I’ll try to update this page if I come across some more data and maps.
When I put together the Spatial SQL for the Geographer posts I never wrote anything about measuring distance. Geographers often need to know the distance between locations, as distance decay is one of those things we find important. Fortunately for us, measuring distance between points is pretty easy using Spatial SQL (and pretty fast). The following query demonstrates an example of measuring distance between a set of defined points from the populated places data available in the sample data.
select t1.Id as ID, t1.NAMEASCII as t1_Name, t1.SOV0NAME as t1_SOV0NAME, t1.Latitude as t1_Latitude, t1.Longitude as t1_Longitude, t2.ID as t2_ID, t2.NAMEASCII as t2_Name, t2.SOV0NAME as t2_SOV0NAME, t2.LATITUDE as t2_Latitide, t2.LONGITUDE as t2_Longitude, Geography::Point(t1.Latitude, t1.Longitude, 4326).STDistance(Geography::Point(t2.LATITUDE, t2.LONGITUDE, 4326)) as Distance_Meters from dbo.Populated_Places t1 cross join dbo.Populated_Places t2 where t1.SOV0NAME = 'United States' and t2.SOV0NAME = 'United States'
You will notice that in the query a few different things are happening:
The results of this query will return a set of columns with the measurement in meters from every record to every other record for US cities. With some additional coding you can generate a distance matrix, find the nearest locations, or create line features between points pretty quickly. This query can also go pretty fast, if the proper indexes are generated and called in the query it should take less than a minute. Not to bad when returning over 600k records.
When I wrote the Spatial SQL (or SQL Spatial, whatever you want to call it, I use both interchangeably for better or worse) blog posts I never mentioned a set of tools that are available which greatly expand the capabilities of the spatial analysis within SQL.
http://sqlspatialtools.codeplex.com/
If you are working with spatial data within SQL Server I encourage you to check this out. I know this works in SQL Server 2008 R2, but I cannot vouch for its usefulness in SQL Server 2012, as I have not used it yet. For a complete list of the functionality available on Codeplex click here.
Until next time, keep on geo-ing!
