Tag Archive for: Visualization

Utilizing the Uniqueness of GIS for Better Environmental Data Analysis

November 18, 2020/by Dr. Todd Pierce

Today is GIS Day, a day started in 1999 to showcase the many uses of geographical information systems (GIS). Earlier Locus blog posts have shown how GIS supports cutting-edge visualization of objects in space and over time. This post is going to go “back to basics” and discuss what makes GIS unique and how environmental data analysis benefits from that uniqueness.

Spatial vs Non-Spatial Relationships

So, what makes GIS unique? It’s the ability of GIS to handle spatial relationships, which goes beyond just putting “dots on a map”. You are probably familiar with non-spatial relationships such as greater than, less than, or equal to, and you probably use them every day. For example, suppose you want to buy the latest gaming console (PS5, anyone?). You need to compare the price of the console to your bank account. If the console price is greater than your savings, then you cannot buy the console.

Or can you? With credit cards, you can pay later, so you go charge the console. At the time of the transaction, some software evaluates a non-spatial relationship and checks if the console price plus your current debt is less than your credit limit. If so, you can buy the console; if not, your purchase is denied.

The key point about this example is that spatial relations play no part. It doesn’t matter where you are located or where the game console is sold from. (OK, there may be things like state taxes and shipping, but that just contributes to the price.) Now, if you were trying to find all gaming consoles for sale within a certain distance of you, that is a spatial relationship. There are multiple types of spatial relationship, but the most common are inside, contains, crosses, overlaps, and within a distance of. Standard relational database software does not handle these sorts of relations, but GIS can.

As an illustration, let’s consider two current events: the 2020 US presidential election and the COVID-19 pandemic. With non-spatial relationships, you can answer various questions such as “did Biden get more votes than Clinton?” or “is the number of positive COVID tests increasing?”. But with spatial relations, you can answer more interesting questions such as “did areas with COVID hot spots vote more predominantly for Biden or Trump?”. For this question you must see if voters lie inside a COVID hot spot; a GIS can perform this analysis and then map the results. While many votes are still being counted, as of this blog post, it appears Trump performed better in COVID hot spots.

Spatial Relationships in Environmental Data

Let’s look at some example of spatial relations in environmental data. Assume you have a database of tritium sampling results in water, along with various map layers of natural and manmade features. What kind of spatial relationships can you explore with GIS?

To answer that, we’ll make some maps with the Locus GIS+ package in EIM, Locus’s cloud-based, software-as-a-service application for environmental data management. All maps shown here display wells with tritium samples, with the wells represented as colored circles. The color scale goes from blue through yellow to red, to indicate increasing tritium results.

Figure 1 shows an example of an inside spatial relationship. The map answers the question “what wells with tritium results are inside the Mortandad Canyon watershed?”. The watershed is highlighted in blue on the map, and you can easily see the wells inside the watershed.

Figure 1: Wells with tritium within a watershed

Figure 2 shows wells with tritium results that are within a distance of a river. The map answers the question “what wells with tritium results are within 500 ft of the river?”. The river, highlighted in light blue, has a 500 ft buffer shown as a dotted blue line. The wells with tritium that lie within the buffer are shown on the map, so you can check if any high tritium results are close to the waterway.

Figure 2: Wells with tritium within a specified distance of a river

Figure 3 shows another example of within a distance of. Here, the map answers the question “what wells with tritium results are within two miles of a middle school?”. The two-mile radius is shown as a shaded blue circle centered on the school. You can see the wells are confined to the area southeast of the school.

Figure 3: Wells with tritium within a specified distance of a school

These three examples are just a small subset of what can be done with GIS and environmental data. Here are some other questions illustrating the kind of spatial analysis that GIS supports.

Have any spill incidents at my site been within a specified distance of a waterway?
Do any pipelines at my site cross protected waterways?
Do any remediation areas at my site contain wells that have recorded high chemical levels in water?
Does the underground plume from a chemical release overlap any aquifers?

All these examples illustrate the power of GIS for analyzing spatial relationships, and these examples are just the beginning. GIS can also perform more sophisticated analyses that look at spatial relationships in different ways to answer questions such as:

How confident can we be in the results of the spatial relationship analysis?
Do all data records follow the spatial relationship, or are any outliers that fall outside the norms?
Has this spatial relationship changed over time? Has the relation grown stronger or weaker?
Can we predict the future of the spatial relationships?

Locus continues to bring new analysis tools to our Locus GIS+ system for environmental applications. These applications let you take advantage of the unique ability of GIS to analyze spatial relationships in your environmental data.

Acknowledgments: All the data in EIM used in the examples was obtained from the publicly available chemical datasets online at Intellus New Mexico.

Interested in Locus’ GIS solutions?

Locus GIS+ features all of the functionality you love in EIM’s classic Google Maps GIS for environmental management—integrated with the powerful cartography, interoperability, & smart-mapping features of Esri’s ArcGIS platform!

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About the Author—Dr. Todd Pierce, Locus Technologies

Dr. Pierce manages a team of programmers tasked with development and implementation of Locus’ EIM application, which lets users manage their environmental data in the cloud using Software-as-a-Service technology. Dr. Pierce is also directly responsible for research and development of Locus’ GIS (geographic information systems) and visualization tools for mapping analytical and subsurface data. Dr. Pierce earned his GIS Professional (GISP) certification in 2010.

A Visualization is Worth a Thousand Data Points

March 4, 2020/by Dr. Todd Pierce

Visualize environmental data with Locus EIM.

You’ve probably heard the saying “A picture is worth a thousand words”. While the advice seems timeless, it actually is fairly modern and started with newspaper advertisements from the 1910s. Furthermore, it’s only since the 1970s that cognitive science has caught up and determined the truth in the saying. Basically, humans have very limited working memory, which is the “storage space” for processing data while making decisions and reasoning through problems. A good picture, though, works as “offline storage” that lets you push information out of your limited working memory and into another format for use as needed. This advantage is especially true when the picture is a useful data visualization such as a chart or map. In this case, you could say “A visualization is worth a thousand data points”.

How limited is working memory? There is a rough consensus, known as Miller’s law, that you can only have “seven, plus or minus two” items in memory at one time. Think of a typical 10-digit phone number that you may need to memorize for a short period. It can be hard to remember all ten individual digits as one large number, as that exceeds working memory. However, you can employ a technique called “chunking” to group items together, reducing the number of items to remember. If you group the phone numbers into the typical ###-###-#### pattern, you only have to remember 3 chunks of 3 to 4 items. A good visualization not only stores information offline, reducing pressure on your brain; it also groups many data items into a much smaller number of chunks so you can process the data more efficiently.

Let’s look at some real examples of how visualizations help by working through a typical scenario using EIM, Locus Technologies’ cloud-based application for environmental data management. Assume you manage a site where you are tracking tritium (H-3) levels in groundwater using a set of monitoring wells. You want to know where tritium has been high over the past ten years. EIM provides different visualizations for exploring your data and finding the answers you need.

First let’s just look at an export of all the data. Using the analysis functions in EIM, you search for all tritium concentrations from monitoring wells for the past ten years. EIM sends the results to a table as shown in Figure 1.

Tabular view of Tritium query results in Locus EIM

Figure 1 Tabular view of Tritium query results

The table has 717 results for multiple wells. It is very difficult to see overall patterns here, either spatially or temporally. Each of the 717 results is one item, and if you try to scroll and sort the table to see if tritium is increasing or decreasing over time, your working memory is quickly overwhelmed. This is where a good data visualization can help.

To start, you decide to send the data to the Locus GIS+ application, using the graduated color and size options. The GIS+ takes the concentrations from the results table and plots them on a site map using the stored coordinates for each well, as shown in Figure 2. The map represents each location with a symbol that is colored and sized to reflect the actual maximum value at that location. The map legend shows you how this was done. Large red circles, for example, represent results from 4,500 to 7,000 pCi/L. As the sizes get smaller, and the colors go from red to blue, the actual result gets smaller.

Graduated symbol and color map in Locus EIM

Figure 2 Graduated symbol and color map of tritium concentrations

This map is great for showing spatial patterns in the data. You can easily pick out a couple of “areas of concern” near the center of the map – one with orange and yellow circles, and another with red circles. To revisit our discussion on working memory and chunks, the map takes the 717 results and summarizes them so your brain can quickly pick out the two areas of concern.

Let’s look more closely at the area of concern with higher results. If we zoom in on the map, we see the two red locations are wells MCOI-5 and MCOI-6 as shown in Figure 2.

Zoomed map for one area of concern in Locus GIS+

Figure 3 Zoomed map for one area of concern

The map shows you where these two high concentrations of tritium are located. But what if you want to see how the concentrations vary over time? You can make a time series chart in EIM for these wells and include a desired regulatory limit, as shown in Figure 4. The green and blue lines represent the tritium concentrations over time for the two wells. The red line at top shows a regulatory action limit.

Figure 4 Line chart showing time series for tritium for two wells, with action limit

The chart shows you two important things. First, and most importantly, all the tritium concentrations for both wells lie well below the regulatory action limit! Second, the concentrations have very different trends for the two wells: MCOI-6 started higher but has trended lower, while MCOI-5 started below MCOI-6 but has now surpassed it. You can confirm these general impressions by running concentration regression charts in EIM for the two locations, as shown in Figure 5. The charts show the best fit regression line and the strength of the relation.

Figure 5 Concentration regression charts in EIM

You can grasp these facts quickly because the of how the chart works. Each series of concentrations for a well consists of multiple data items that are ‘chunked’ into one line on the chart. There are two many individual data points on this chart for your working memory, but only three lines, which can easily be manipulated in your brain. For comparison, Figure 6 shows the actual data values for the chart. The time trends shown above in the charts are not as obvious from the table.

Figure 6 Actual data values for the chart in Figure 4

Now, this might be counter-intuitive, but what if you wanted to put some of these values on the map? While visualizations do help understand data, sometimes it can be useful to have the data shown as well so viewers can see where the visualizations came from. The EIM Data Callouts function can do this. Figure 7 shows data callouts for the two wells. Each callout shows the maximum annual tritium result for 2010-2020. Now you have the actual tritium concentrations located spatially next to the matching wells!

Figure 7 Data Callouts in EIM GIS+

Now that you know where your tritium might be a concern, suppose you want to see what’s going on with groundwater at your site. The EIM contouring module does that for you. There are multiple contouring options, but for this example let’s use the default options for kriging. We know from Figure 2 that the wells MCOI-5 and MCOI-6 are located in the Mortandad Canyon. Figure 8 shows the contouring map generated from EIM for the groundwater wells in that canyon, using the most recent groundwater levels. Higher groundwater values are lighter in color than lower values.

The area of concern is marked with an arrow at upper left. The contour lines and values can help you determine how the tritium might migrate in your site. Imagine trying to picture this just using tables of groundwater readings! With the contour map, the readings turn into lines that can be chunked together for analysis: the higher levels at the upper left forming a “plateau”, the closely packed lines moving across the map to the east, and then the “saddle” area at lower right. These different line patterns carry particular meanings to engineers and scientists who interpret contour maps.

Contour map for groundwater in Locus GIS+

Figure 8 Contour map for groundwater levels

The contour map completes our tour of some of the visualization tools in EIM. Because visualizations let you chunk items together, you can look at the ‘big picture” and not get lost in tables of data results. Your working memory stays within its capacity, your analysis of the information becomes more efficient, and you can gain new insights into your data.

Acknowledgments: All the data in EIM used in the examples was obtained from the publicly available chemical datasets online at Intellus New Mexico.

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About the Author—Dr. Todd Pierce, Locus Technologies

Mapping All of Space and Time

November 14, 2019/by Dr. Todd Pierce

Today is GIS Day, a day started in 1999 to showcase the many uses of geographical information systems (GIS). To celebrate the passage of another year, this blog post examines how maps and GIS show time, and how Locus GIS+ supports temporal analysis for use with EIM, Locus’s cloud-based, software-as-a-service application for environmental data management.

Space and Time

Since GIS was first imagined in 1962 by Roger Tomlinson at the Canada Land Inventory, GIS has been used to display and analyze spatial relationships. Every discrete object (such as a car), feature (such as an acre of land), or phenomenon (such as a temperature reading) has a three-dimensional location that can be mapped in a GIS as a point, line, or polygon. The location consists of a latitude, longitude, and elevation. Continuous phenomenon or processes can also be located on a map. For example, the flow of trade between two nations can be shown by an arrow connecting the two countries with the arrow width indicating the value of the traded goods.

However, everything also has a fourth dimension, time, as locations and attributes can change over time. Consider the examples listed above. A car’s location changes as it is driven, and its condition and value change as the car gets older. An acre of land might start covered in forest, but the land use changes over time if the land is cleared for farming, and then later if the land is paved over for a shopping area. The observed temperature at a given position changes with time due to weather and climate changes spanning multiple time scales from daily to epochal. Finally, the flow of trade between two countries changes as exports, imports, and prices alter over time.

Maps and Time

Traditional flat maps already collapse three dimensions into two, so it’s not surprising that such maps do not handle the extra time dimension very well. Cartographers have always been interested in showing temporal data on maps, though, and different methods can be employed to do so. Charles Minard’s famous 1861 visualization of Napoleon’s Russian campaign in 1812-1813 is an early example of “spatial temporal” visualization. It combines two visuals – a map of troop movements with a time series graph of temperature – to show the brutal losses suffered by the French army. The map shows the army movement into Russia and back, with the line width indicating the troop count. Each point on the chart is tied to a specific point on the map. The viewer can see how troop losses increased as the temperature went from zero degrees Celsius to -30 degrees. The original thick tan line has decreased to a black sliver at the end of the campaign.

Charles Minard’s map of Napoleon’s Russian campaign in 1812-1813.

The Minard visual handles time well because the temperature chart matches single points on the map; each temperature value was taken at a specific location. Showing time changes in line or area features, such as roads or counties, is harder and is usually handled through symbology. In 1944, the US Army Corps of Engineers created a map showing historical meanders in the Mississippi River. The meanders are not discrete points but cover wide areas. Thus, past river channels are shown in different colors and hatch patterns. While the overlapping meanders are visually complex, the user can easily see the different river channels. Furthermore, the meanders are ‘stacked’ chronologically, so the older meanders seem to recede into the map’s background, similar to how they occur further back in time.

Inset from Geological Investigation of the Alluvial Valley of the Lower Mississippi River.

Another way to handle time is to simply make several maps of the same features, but showing data from different times. In other words, a temporal data set is “sliced” into data sets for a specific time period. The viewer can scan the multiple maps and make visual comparisons. For example, the Southern Research Station of the US Forest Service published a “report card” in 2011 for Forest Sustainability in western North Carolina. To show different land users over time, small maps were generated by county for three years. Undeveloped land is colored green and developed land is tan. Putting these small maps side by side shows the viewer a powerful story of increasing development as the tan expands dramatically. The only drawback is that the viewer must mentally manipulate the maps to track a specific location.

Land Use change over time for Buncombe County, NC

GIS and Time

The previous map examples prove that techniques exist to successfully show time on maps. However, such techniques are not widespread. Furthermore, in the era of “big data” and the “Internet of Things”, showing time is even more important. Consider two examples. First, imagine a shipment of 100 hazardous waste containers being delivered on a truck from a manufacturing facility to a disposal site. The truck has a GPS unit which transmits its location during the drive. Once at the disposal site, each container’s active RFID tag with a GPS receiver tracks the container’s location as it proceeds through any decontamination, disposal, and decommission activities. The locations of the truck and all containers have both a spatial and a temporal component. How can you map the location of all containers over time?

As a second example, consider mobile data collection instruments deployed near a facility to check for possible contamination in the air. Each instrument has a GPS so it can record its location when the instrument is periodically relocated. Each instrument also has various sensors that check every minute for chemical levels in the air plus wind speed and temperature. All these data points are sent back to a central data repository. How would you map chemical levels over time when both the chemical levels and the instrument locations are changing?

In both cases, traditional flat maps would not be very useful given the large amounts of data that are involved. With the advent of GIS, though, all the power of modern computers can be leveraged. GIS has a powerful tool for showing time: animation. Animation is similar to the small “time slice” maps mentioned above, but more powerful because the slices can be shown consecutively like a movie, and many more time slices can be created. Furthermore, the viewer no longer has to mentally stack maps, and it is easier to see changes over time at specific locations.

Locus has adopted animation in its GIS+ solution, which lets a user use a “time slider” to animate chemical concentrations over time. When a user displays EIM data on the GIS+ map, the user can decide to create “time slices” based on a selected date field. The slices can be by century, decade, year, month, week or day, and show the maximum concentration over that time period. Once the slices are created, the user can step through them manually or run them in movie mode.

To use the time slider, the user must first construct a query using the Locus EIM application. The user can then export the query results to the GIS+ using the time slider option. As an example, consider an EIM query for all benzene concentrations sampled in a facility’s monitoring wells since 2004. Once the results are sent to the GIS+, the time slider control might look like what is shown here. The time slices are by year with the displayed slice for 3/30/2004 to 3/30/2005. The user can hit play to display the time slices one year at a time, or can manually move the slider markers to display any desired time period.

Locus GIS+ time slider

Here is an example of a time slice displayed in the GIS+. The benzene results are mapped at each location with a circle symbol. The benzene concentrations are grouped into six numerical ranges that map to different circle sizes and colors; for example, the highest range is from 6,400 to 8,620 µg/L. The size and color of each circle reflect the concentration value, with higher values corresponding to larger circles and yellow, orange or red colors. Lower values are shown with smaller circles and green, blue, or purple colors. Black squares indicate locations where benzene results were below the chemical detection limit for the laboratory. Each mapped concentration is assigned to the appropriate numerical range, which in turn determines the circle size and color. This first time slice for 2004-2005 shows one very large red “hot spot” indicating the highest concentration class, two yellow spots, and several blue spots, plus a few non-detects.

Time slice for a year for a Locus GIS+ query

Starting the time slider runs through the yearly time slices. As time passes in this example, hot spots come and go, with a general downward trend towards no benzene detections. In the last year, 2018-2019, there is a slight increase in concentrations. Watching the changing concentrations over time presents a clear picture of how benzene is manifesting in the groundwater wells at the site.

GIS+ time slider in action

While displaying time in maps has always been a challenge, the use of automation in GIS lets users get a better understanding of temporal trends in their spatial data. Locus continues to bring new analysis tools to their GIS+ system to support time data in their environmental applications.

Time slice for a Locus GIS+ query

Interested in Locus’ GIS solutions?

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About the Author—Dr. Todd Pierce, Locus Technologies

Taking your environmental data to the next level with advanced integrated GIS features

August 15, 2018/by Locus Product Team

In our last GIS blog, we covered some tips for choosing an integrated GIS/environmental data management system. Now let’s look at some more advanced features that may be appealing to a wide range of data managers and facility owners.

1) Look for ways to integrate GIS base maps from other sources—so you can easily add piping diagrams, facility building layouts, or watersheds and drainage.

A map is much more meaningful with your facility information. Google maps are great, but they won’t show your current building layout and your pipe and sewer diagrams. So look for the capability to display maps created by other internal departments, like facilities or operations, so you can gain more insights from your data and have information readily available to share with other parts of the company who may disturb the area with digging or construction activity.

GIS+ - Intellus - historical buildings and watersheds

In this example from the Intellus website, environmental data can be visualized in relation to historical buildings and watersheds, both elements created by internal mapping departments. Internal base maps can also replace default maps from Esri or Google.

2) Load in other data from the Esri cloud to leverage a wide range of available data for your facility and use it with your GIS+ layers.

With the right GIS solution, it’s easy to bring in data from any public source, including government agencies, such as EPA. Combining your map with the world of online data can bring fresh insights to your environmental compliance challenges.

In this example, GIS is used to merge Audubon bird points with Los Alamos National Laboratory (using the Intellus website).

3) Add reference information, such as photos and reports, to locations, and access them from the map.

Using a freeform polygon search (another must-have in a GIS tool), users can highlight an area and—with a single click—see all the data, field photos, and reports associated with that area. This is especially useful for active facilities where activities are planned in areas with legacy contamination (“know before you dig!”). This type of functionality makes it simple for less savvy map users to easily get the information they need.

In this example, a polygon tool was used to highlight an area, and all data, documents, reports associated with ALL locations within the selected area are available from the map. These functions let facility staff review key environmental information before conducting activities at a facility location.

4) Better understand complex and dense maps with clustered locations.

Some facilities or sites have very dense sampling locations that can be a challenge to view on maps due to overlapping data points. Using the concept of clustering, one can more easily view the dense data, with results color-coded to help focus the review. Clicking on the cluster reveals the details underneath for more close review.

In this example, tritium in monitoring wells at the Los Alamos site in New Mexico is being reviewed on the map. Without clustering, the map is impossible to read or use effectively. With clustering, the orange circles (“clusters”) indicate higher concentrations of the contaminant, and clicking on the cluster reveals the individual data points it contains.

Before clustering is applied, we have a very difficult-to-read map.

After clustering is applied, the map is much more useful—colors focus the user on the higher concentration areas.

5) Watch trends or changes over time with time layers.

Imagine being able to watch changes in data over time with a simple slider control. An integrated GIS can provide that clarity over all the data in your database, so you can watch the progress of a cleanup, track chemicals in your water distribution system, or watch a groundwater plume move over time.

6) Search for sampling results near a given address or within a given distance from selected map features.

For sites with concerned neighbors, it’s key to know what chemicals or other environmental conditions may be affecting them. With GIS tools, it’s easy to put in an address and see what is within a radius, or to look within a distance from a specific location. In this example, you can see that there are no sampling locations within a 2000-ft radius from the center point. You can also type in an address and see what is nearby.

Looking at a 2000-ft radius from a location to see what is nearby.

7) Turn data into insights with data callouts.

The more information you can provide to users in a format that highlights results in a meaningful way, the more you can help streamline review and analysis for any data review effort. GIS tools that support data callouts (with logic to highlight actionable results) can quickly convert a mass of data into a clear picture of the issues at a facility or site.

In the map below, data summaries are presented on a facility map to show areas with results above an action limit and associated with other detected parameters. Reviewers can easily see the exceedances (in red) and pinpoint where the issues lie. Although these maps may look complicated to produce, they can be integrated with standard reporting tools that generate maps at the click of a button.

Intrigued by the possibilities?

When you’re evaluating an integrated GIS solution, make sure to dig deeper than the obvious necessary features to learn about all the advanced functionality that is available or on the product roadmap. The best solutions will already have some truly powerful capabilities available, with an even longer list of upcoming features.

Your environmental information management will evolve to the next level when you have the flexibility of visualizing your data in so many ways. Happy mapping!

Screenshot of Locus GIS location clustering functionality

See your data in new ways with Locus GIS for environmental management.
Locus offers integrated GIS/environmental data management solutions for organizations in many industries.
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WM Symposia 2018 provided an excellent showcase for Locus GIS+ in LANL’s Intellus website

March 28, 2018/by Locus Product Team

At the annual WM Symposia, representatives from many different DOE sites and contractors gather once a year and discuss cross-cutting technologies and approaches for managing the legacy waste from the DOE complex. This year, Locus’ customer Los Alamos National Laboratory (LANL) was the featured laboratory. During their presentation, they discussed Locus GIS+, which powers Intellus, their public-facing environmental monitoring database website.

If you haven’t been to LANL’s Intellus website recently, you are in for a surprise! It was recently updated to better support casual users, and it features some of the best new tools Locus has to offer. Locus reimagined the basic query engine and created a new “Quick search” to streamline data retrieval for casual users. The guided “Quick search” simplifies data queries by stepping you through the filter selections for data sources, locations, dates, and parameters, providing context support at each step along the way.

While a knowledgeable environmental scientist may be able to easily navigate a highly technical system, that same operation is bound to be far more difficult for a layperson interested in what chemicals are in their water. Constructing the right query is not as simple as looking for a chemical in water—it really matters what type of water you want to look within. On the Intellus website (showing the environmental data from the LANL site), there are 16 different types of water (not including “water levels”). Using the latest web technologies and our domain expertise, Locus created a much easier way to get to the data of interest.

Just querying data is not necessarily the most intuitive activity to gain insights. Locus integrated our new GIS+ visualization engine to allow users to instantly see all the data they just queried in detailed, context-rich maps.

Intellus GIS+ map showing “Quick search” query results for chromium levels in the LANL area

Instead of a dense data grid, GIS+ gives users an instant visual representation of the issue, enabling them to quickly spot the source of the chemicals and review the data in the context of the environmental locations and site activities. Most importantly for Intellus users, this type of detailed map requires no GIS expertise and is automatically created based on your query. This directly supports Intellus’ mission to provide transparency into LANL’s environmental monitoring and sampling activities.

GIS+ also allows users (albeit with a bit more experience in GIS mapping) to integrate maps from a wide range of online sources to provide even more insight to the available data. In the example below, we overlaid the publicly-available US Fish and Wildlife critical habitat maps with data from the LANL site to show the relationship of the site to critical habitats. This type of sophisticated analysis is the future of online GIS. Locus takes full advantage of these opportunities to visualize and integrate data from varying sources with our GIS+ tools, made simple for users and integrated with ArcGIS Online by Esri.

Intellus GIS+ map showing imported layers of US Fish and Wildlife critical habitats in relation to LANL environmental sampling data

Overall, Locus is very proud of our cross-cutting environmental information management tools. We were one of many WM18 attendees enjoying LANL’s presentation and getting even more ideas from the audience on the next steps for better environmental visualization.

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Tag Archive for: Visualization

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Tag Archive for: Visualization

Utilizing the Uniqueness of GIS for Better Environmental Data Analysis

Spatial vs Non-Spatial Relationships

Spatial Relationships in Environmental Data

Interested in Locus’ GIS solutions?

A Visualization is Worth a Thousand Data Points

Visualize environmental data with Locus EIM.

Mapping All of Space and Time

Space and Time

Maps and Time

GIS and Time

Interested in Locus’ GIS solutions?

Taking your environmental data to the next level with advanced integrated GIS features

1) Look for ways to integrate GIS base maps from other sources—so you can easily add piping diagrams, facility building layouts, or watersheds and drainage.

2) Load in other data from the Esri cloud to leverage a wide range of available data for your facility and use it with your GIS+ layers.

3) Add reference information, such as photos and reports, to locations, and access them from the map.

4) Better understand complex and dense maps with clustered locations.

5) Watch trends or changes over time with time layers.

6) Search for sampling results near a given address or within a given distance from selected map features.

7) Turn data into insights with data callouts.

Intrigued by the possibilities?

Get a demo of Locus GIS

WM Symposia 2018 provided an excellent showcase for Locus GIS+ in LANL’s Intellus website

Tag Archive for: Visualization

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