Thunderhead Explorer

Been wanting to post on this subject for quite some time (actually over a year) as associating a world coordinate with the proper nearby linear feature provides tremendous insight based on the fusion of their attributes. Moreover, doing that on a massive scale and quickly is even more imperative in today's BigData world, thus the usage of Apache Spark . I’ve posted a standalone implementation that relies on well-documented simple math and published methodology to perform searches on massive datasets in batch mode. What is exciting to me in writing this post was the viewing of the snap results in ArcGIS Pro . My lack of knowledge in extending ArcGIS Pro with downloadable Python modules contributed to the delay (and slight case of procrastination :-). However, with the help of a colleague, I was able to pip install modules that can be imported by my custom ArcPy based toolboxes without any errors. Also, since this is all based on BigData, well it has to be tested in a BigData e...

When it comes to web mapping, we are surfing on a third wave in our digital ocean. And the “collaborative processing” between the digital entities while surfing that wave is making the ride more fun, insightful and expressive. The first web wave was back in the mid 1990s, where interactive maps in the form of html image tags relied heavily on the server and requests parameters to regenerate the image when you clicked on the edge arrows to pan and zoom. Remember MapQuest and ArcIMS ? Then in the mid 2000s came the second wave or more like a tsunami, Google Maps. You hold down the right mouse button on the map and drag to pan, you use the scroll wheel to zoom in and out, and… when you click on the map, a bubble appears on the map showing the details of the clicked location. Disruptive ! And all was smooth, responsive and AJAX y. This is when I believe that this collaborative processing concept took root and materialized itself in the web mappers’ minds. Soon after, more expressive...

“But…It works on _my_ machine !!!” How many times did you hear that ? That is exactly one of the use cases of Docker for developers - Create an exact reproducible environment for each developer, even down to the hardware specification. And, that same environment can be on premise or in the cloud. With the advent of ArcGIS For Server 10.4 , I wanted to run it on my mac so I can try out some of the new features like chaining multiple SOIs. I could have started a Windows based VM and gone through the GUI based setup, which is a pretty straight forward process (My friend Georges G. calls this, a PhD process, P ush H ere D ummy). But, I wanted to automate the whole install process in a headless way (I’m sure there is a way to do that using Windows, just I do not know how, maybe a blog post for another day) Enter Docker. After downloading the ArcGIS For Linux tarball and the license file from my.esri.com , you can build a Dockerfile that automates the whole install process in a headless wa...

CSV files (though not the most efficient format and least expressive due to meager header metadata) is one of the most ubiquitous formats to place data in BigData stores like HDFS . In addition, geospatial information such as latitude and longitude is now the norm as fields in those CSV files origination from say a moving GPS based device. A constant request that I receive all the time is “How do I visualize on the web all these data points?” There is a legitimate concern in this question which is “How do I visualize on the web millions and millions on points?”. Well the short answer is “You Don’t!” (actually, you can…but that is blog post for another day). Though you can download a couple of million points to a web client, after a while the transfer time will be prohibitive. However, if you process the data on the server and send down the aggregated information to be symbolized on the client, then things become more interesting. A common aggregation processing is binning, where, im...

The applications of DBSCAN clustering straddle various domains including machine learning, anomaly detection and feature learning. But my favorite part about it, is that you do not have to specify apriori the number of clusters to classify the input data. You specify a neighborhood distance and the minimum numbers of points to form a cluster and it will return back a set of clusters with the associated points in the cluster that meet the input parameters. However, DBSCAN can consume a lot of memory when the input is very large. And since I do BigData, my data inputs will overwhelm my MacBook Pro very quickly. Since I know Hadoop MapReduce fairly well, and MR has been around for quite some time, I decided to see how other folks implemented such a solution in a distributed share nothing environment. I came across this paper, which was very inspiring and found out that IrvingC used it too as a reference implementation. So I decided to implement my own DBSCAN on Spark as a way to fu...

Happy 2016 all. Yes it has been a while and thanks for your patience. Like usual, at the beginning of every year, there are the promises to eat less, exercise more, climb Ventoux and blog more. Was listening to Feakonomic (When Willpower Isn’t Enough) , and this initial post of the year is to harness the power of a fresh start. Esri has been advocating for a while to use FileGeodatabase , and actually released a C++ based API to perform read-only operations on it. However, the read has to be performed off a local file system and the read is single threaded (you could write an abstract layer on top of the API to perform a parallel partitioned read if you have the time). In my BigData uses cases, I need to place the GDB files in HDFS so I can perform Spark based GeoAnalytics. Well, that made the usage of the C++ API difficult (as it is not using the Hadoop File System API) and will have to map the Spark API to a native API and will have to publish the DLL and…(well you can imagine ...