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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!



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 >

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

Blazing a trail: SaraniaSat-led Team Shapes the Future of Space-Based Analytics

10/13/2025

On July 24, 2025, a unique international partnership of SaraniaSat, NV5 Geospatial Software, BruhnBruhn Innovation (BBI), Netnod, and Hewlett Packard Enterprise (HPE) achieved something unprecedented: a true demonstration of cloud-native computing onboard the International Space Station (ISS) (Fig. 1). Figure 1. Hewlett... Read More >

NV5 at ESA’s Living Planet Symposium 2025

NV5 at ESA’s Living Planet Symposium 2025

9/16/2025

We recently presented three cutting-edge research posters at the ESA Living Planet Symposium 2025 in Vienna, showcasing how NV5 technology and the ENVI® Ecosystem support innovation across ocean monitoring, mineral exploration, and disaster management. Explore each topic below and access the full posters to learn... Read More >

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

Monitor, Measure & Mitigate: Integrated Solutions for Geohazard Risk

9/8/2025

Geohazards such as slope instability, erosion, settlement, or seepage pose ongoing risks to critical infrastructure. Roads, railways, pipelines, and utility corridors are especially vulnerable to these natural and human-influenced processes, which can evolve silently until sudden failure occurs. Traditional ground surveys provide only periodic... Read More >

Geo Sessions 2025: Geospatial Vision Beyond the Map

Geo Sessions 2025: Geospatial Vision Beyond the Map

8/5/2025

Lidar, SAR, and Spectral: Geospatial Innovation on the Horizon Last year, Geo Sessions brought together over 5,300 registrants from 159 countries, with attendees representing education, government agencies, consulting, and top geospatial companies like Esri, NOAA, Airbus, Planet, and USGS. At this year's Geo Sessions, NV5 is... Read More >

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Positioning plots

Anonym

Last week, I gave an example of creating a multi-panel plot (or multiplot) using the LAYOUT keyword. Today (with a nod to a comment from Paul Young), I'll show an alternate technique using the POSITION keyword. The Solar Physics Group at NASA's Marshall Space Flight Center provides information on the sunspot cycle. You can download the group's monthly sunspot number data—dating back to 1749!—here (TXT). I'd like to display, side-by-side, a plot of the sunspot series since 1970 and a histogram of the series. Start by reading the data from the file, assuming it's included in your IDL path. I prefer the astrolib READCOL procedure for reading text files.

 file = file_which('spot_num.txt') readcol, file, year, month, sunspots

Next, create a time vector and use it to restrict our analysis to sunspot activity since 1970:

 time = year + (month-1.0)/12.0 i_ge1970 = where(time ge 1970.0, /null) time_recent = time[i_ge1970] sunspots_recent = sunspots[i_ge1970]

Now use HISTOGRAM to calculate a discrete frequency distribution of the sunspot numbers since 1970:

 sunspot_histogram = histogram(sunspots_recent, $ binsize=10, $ locations=sunspot_bins)

I've empirically chosen a bin size of 10, and returned the locations of the bins into the variable sunspot_bins. The first plot, positioned on the left, is of the sunspot series:

 xr = minmax(time_recent) yr = minmax(sunspots_recent) series = plot(time_recent, sunspots_recent, $ dimensions=[800,600], $ position=[0.10, 0.15, 0.75, 0.90], $ xrange=xr, yrange=yr, $ xtitle='Year', ytitle='Sunspots', $ title='Sunspot Activity (1970-present)')

The key in this call to PLOT is the POSITION keyword: this four-element array describes the lower left [0.10, 0.15] and upper right [0.75,0.90] corners of the bounding box of the plot, in normalized coordinates. The plot fits within this box. I've also used the convenient astrolib MINMAX function to set up axis ranges for the plot, and the DIMENSIONS property to set the size of the plot window, in pixels. The second plot, positioned to the right of the first, displays the histogram of the sunspot series:

 histoplot = plot(sunspot_histogram, sunspot_bins, $ position=[0.80, 0.15, 0.95, 0.90], /current, $ /histogram, $              ; IDL 8.2.1 yrange=series.yrange, $    ; match yrange of first plot ymajor=0, $                ; no axis text /fill_background, fill_color='light gray', $ xtitle='Frequency', title='Histogram')

Swapping the order of the parameters to PLOT transposes the histogram. POSITION is used again to position the plot. CURRENT ensures this plot appears in the same window as series. The HISTOGRAM property tells IDL to draw discrete blocks instead of point-to-point lines (hi, Haje!). Here's my result: Sunspot series and histogram

I like this pair of plots because scanning horizontally across the time series gives a visual estimate of the histogram displayed on the right. Next week, I'll perform some simple time series analyses on these data.

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