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NV5 Geospatial Blog

Each month, NV5 Geospatial posts new blog content across a variety of categories. Browse our latest posts below to learn about important geospatial information or use the search bar to find a specific topic or author. Stay informed of the latest blog posts, events, and technologies by joining our email list!



Mapping Earthquake Deformation in Taiwan With ENVI

Mapping Earthquake Deformation in Taiwan With ENVI

12/15/2025

Unlocking Critical Insights With ENVI® Tools Taiwan sits at the junction of major tectonic plates and regularly experiences powerful earthquakes. Understanding how the ground moves during these events is essential for disaster preparedness, public safety, and building community resilience. But traditional approaches like field... Read More >

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

Comparing Amplitude and Coherence Time Series With ICEYE US GTR Data and ENVI SARscape

12/3/2025

Large commercial SAR satellite constellations have opened a new era for persistent Earth monitoring, giving analysts the ability to move beyond simple two-image comparisons into robust time series analysis. By acquiring SAR data with near-identical geometry every 24 hours, Ground Track Repeat (GTR) missions minimize geometric decorrelation,... Read More >

Empowering D&I Analysts to Maximize the Value of SAR

Empowering D&I Analysts to Maximize the Value of SAR

12/1/2025

Defense and intelligence (D&I) analysts rely on high-resolution imagery with frequent revisit times to effectively monitor operational areas. While optical imagery is valuable, it faces limitations from cloud cover, smoke, and in some cases, infrequent revisit times. These challenges can hinder timely and accurate data collection and... Read More >

Easily Share Workflows With the Analytics Repository

Easily Share Workflows With the Analytics Repository

10/27/2025

With the recent release of ENVI® 6.2 and the Analytics Repository, it’s now easier than ever to create and share image processing workflows across your organization. With that in mind, we wrote this blog to: Introduce the Analytics Repository Describe how you can use ENVI’s interactive workflows to... Read More >

Deploy, Share, Repeat: AI Meets the Analytics Repository

Deploy, Share, Repeat: AI Meets the Analytics Repository

10/13/2025

The upcoming release of ENVI® Deep Learning 4.0 makes it easier than ever to import, deploy, and share AI models, including industry-standard ONNX models, using the integrated Analytics Repository. Whether you're building deep learning models in PyTorch, TensorFlow, or using ENVI’s native model creation tools, ENVI... Read More >

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Data Structure Analysis

Anonym

One of the main questions anybody using a programming language has to ask themselves is "what data structures should I be using?" This can be a complicated and difficult question as there are many trade-offs to consider. If it is desired to have a dynamic data type, IDL provides multiple options. In this analysis we will consider: dynamic arrays, lists, hashes, and ordered hashes and their ability to insert and delete elements. To start, let's review our data structures. Dynamic arrays are based on the ability for IDL arrays to resize themselves. For example:

array = [1,2,3,4]               ; Declaration

array = [array, 5]              ; Insert

array = [array[0:1],array[3:*]] ; Remove

 

Lists use the IDL object LIST:

list = LIST(1,2,3,4)    ; Declaration

list.add,5              ; Insert

list.remove, 2          ; Remove

 

Hashes use the IDL object HASH:

hash = HASH([1,2,3,4],[1,2,3,4]) ; Declaration

hash[5] = 5                      ; Insert

hash.remove, 2                   ; Remove

 

Ordered hashes use the IDL object ORDEREDHASH:

ohash = ORDEREDHASH([1,2,3,4],[1,2,3,4]) ; Declaration

ohash[5] = 5                             ; Insert

ohash.remove, 2                          ; Remove

 

For each data structure we will time how long it takes to insert and remove n elements (Note: the inserts/removals are done inside of a FOR loop, one insert/removal per iteration. This is done to simulate an application which expects a high degree of volatility in the use of their data structures. However, since IDL is a vectorized language, it is always best to try to group multiple operations into a single call). Please see the attached plots for the results of the runs. The results are what we would expect from a simple big-O analysis. Dynamic arrays are comparable for small input sizes, however, as soon as the size of the input grows, it becomes much faster to use a hash (either type) or a list. In terms of pure speed for any arbitrary input size, list  is the fastest. However, if you know your input bounded to a few elements, all of the proposed data structures can offer a similar performance.

Note: For this analysis, all the data structures had similar performance up to 10,000 elements. This is in no way a comprehensive test and the results may differ on your system. However, the rule of thumb I follow is, if your input is less than 10,000 elements choose the data structure which is the easiest for you to work with.

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