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Top 10 Enhancements to Locus Environmental Software in 2019

Let’s look back on the most exciting new features and changes made in EIM, Locus’ environmental data management software, during 2019!

1. Migration to AWS Cloud

In August, Locus migrated EIM into the Amazon Web Services (AWS) cloud. EIM already had superior security, reliability, and performance in the Locus Cloud. The move to AWS improves on those metrics and allows Locus to leverage AWS specific tools that handle big data, blockchain, machine learning, and data analytics. Furthermore, AWS is scalable, which means EIM can better handle demand during peak usage periods. The move to AWS helps ensure that EIM remains the world’s leading water quality management software.

2. SSO Login

EIM now supports Single Sign-On (SSO), allowing users to access EIM using their corporate authentication provider. SSO is a popular security mechanism for many corporations. With SSO, one single login allows access to multiple applications, which simplifies username and password management and reduces the number of potential targets for malicious hacking of user credentials. Using SSO with EIM requires a one-time configuration to allow EIM to communicate with a customer’s SSO provider.

3. GIS+ Data Callouts

The Locus GIS+ solution now supports creating data callouts, which are location-specific crosstab reports listing analytical, groundwater, or field readings. A user first creates a data callout template using a drag-and-drop interface in the EIM enhanced formatted reports module. The template can include rules to control data formatting (for example, action limit exceedances can be shown in red text). When the user runs the template for a specific set of locations, EIM displays the callouts in the GIS+ as a set of draggable boxes. The user can finalize the callouts in the GIS+ print view and then send the resulting map to a printer or export the map to a PDF file.

4. EIM One

For customers who don’t require the full EIM package, Locus now offers EIM One, which gives the ability to customize EIM functionality. Every EIM One purchase comes with EIM core features: locations and samples; analytical and field results; EDD loading; basic data views; and action limit exceedance reports. The customer can then purchase add-on packages to get just the functionality desired–for example a customer with DMR requirements may purchase the Subsurface and Regulatory Reporting packages. EIM One provides customers with a range of pricing options to get the perfect fit for their data management needs.

5. IoT data support

EIM can now be configured to accept data from IoT (internet of things) streaming devices. Locus must do a one-time connection between EIM and the customer’s IoT streaming application; the customer can then use EIM to define the devices and data fields to capture. EIM can accept data from multiple devices every second. Once the data values are in EIM, they can be exported using the Expert Query tool. From there, values can be shown on the GIS+ map if desired. The GIS+ Time Slider automation feature has also been updated to handle IoT data by allowing the time slider to use hours, minutes, and seconds as the time intervals.

6. CIWQS and NCDEQ exports

EIM currently supports several dozen regulatory agency export formats. In 2019, Locus added two more exports for CIWQS (California Integrated Water Quality System Project) and the NCDEQ (North Carolina Department of Environmental Quality). Locus continues to add more formats so customers can meet their reporting requirements.

7. Improved Water Utility reporting

EIM is the world’s leading water quality management software, and has been used since 1999 by many Fortune 500 companies, water utilities, and the US Government. Locus added two key reports to EIM for Water in 2019 to further support water quality reporting. The first new report returns chlorine averages, ranges, and counts. The second new report supports the US EPA’s Lead and Copper rule and includes a charting option. Locus will continue to enhance EIM for Water by releasing the 2019 updates for the Consumer Confidence Report in January 2020.

8. Improved Non-Analytical Views

Locus continues to upgrade and improve the EIM user interface and user experience. The most noticeable change in 2019 was the overhaul of the Non-analytical Views pages in EIM, which support data exports for locations, samples, field readings, groundwater levels, and subsurface information. Roughly 25 separate pages were combined into one page that supports all these data views. Users are directed through a series of filter selections that culminate in a grid of results. The new page improves usability and provides one centralized place for these data reports. Locus plans to upgrade the Analytical Views in the same way in 2020.

9. EIM search box

To help customers find the correct EIM menu function, Locus added a search box at the top right of EIM. The search box returns any menu items that match the user’s entered search term. In 2020, Locus will expand this search box to return matching help file documents and EDD error help, as well as searches for synonyms of menu items.

10. Historical data reporting in EDD loading

The EIM EDD loader now has a new “View history” option for viewing previously loaded data for the locations and parameters in the EDD. This function lets users put data in the EDD holding table into proper historical context. Users can check for any unexpected increases in parameter concentrations as well as new maximum values for a given location and parameter.

Contact us to see a demo of Locus EIM

Mapping All of Space and Time

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?

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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[sc_image width=”150″ height=”150″ src=”16303″ style=”11″ position=”centered” disable_lightbox=”1″ alt=”Dr. Todd Pierce”]

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.

Predicting Water Quality with Machine Learning

At Locus Technologies, we’re always looking for innovative ways to help water users better utilize their data. One way we can do that is with powerful technologies such as machine learning. Machine learning is a powerful tool which can be very useful when analyzing environmental data, including water quality, and can form a backbone for competent AI systems which help manage and monitor water. When done correctly, it can even predict the quality of a water system going forward in time. Such a versatile method is a huge asset when analyzing data on the quality of water.

To explore machine learning in water a little bit, we are going to use some groundwater data collected from Locus EIM, which can be loaded into Locus Platform with our API. Using this data, which includes various measurements on water quality, such as turbidity, we will build a model to estimate the pH of the water source from various other parameters, to an error of about 1 pH point. For the purpose of this post, we will be building the model in Python, utilizing a Jupyter Notebook environment.

When building a machine learning model, the first thing you need to do is get to know your data a bit. In this case, our EIM water data has 16,114 separate measurements. Plus, each of these measurements has a lot of info, including the Site ID, Location ID, the Field Parameter measured, the Measurement Date and Time, the Field Measurement itself, the Measurement Units, Field Sample ID and Comments, and the Latitude and Longitude. So, we need to do some janitorial work on our data. We can get rid of some columns we don’t need and separate the field measurements based on which specific parameter they measure and the time they were taken. Now, we have a datasheet with the columns Location ID, Year, Measurement Date, Measurement Time, Casing Volume, Dissolved Oxygen, Flow, Oxidation-Reduction Potential, pH, Specific Conductance, Temperature, and Turbidity, where the last eight are the parameters which had been measured. A small section of it is below.

Alright, now our data is better organized, and we can move over to Jupyter Notebook. But we still need to do a bit more maintenance. By looking at the specifics of our data set, we can see one major problem immediately. As shown in the picture below, the Casing Volume parameter has only 6 values. Since so much is missing, this parameter is useless for prediction, and we’ll remove it from the set.

We can check the set and see that some of our measurements have missing data. In fact, 261 of them have no data for pH. To train a model, we need data which has a result for our target, so these rows must be thrown out. Then, our dataset will have a value for pH in every row, but might still have missing values in the other columns. We can deal with these missing values in a number of ways, and it might be worth it to drop columns which are missing too much, like we did with Casing Volume. Luckily, none of our other parameters are, so for this example I filled in empty spaces in the other columns with the average of the other measurements. However, if you do this, it is necessary that you eliminate any major outliers which might skew this average.

Once your data is usable, then it is time to start building a model! You can start off by creating some helpful graphs, such as a correlation matrix, which can show the relationships between parameters.

For this example, we will build our model with the library Keras. Once the features and targets have been chosen, we can construct a model with code such as this:

This code will create a sequential deep learning model with 4 layers. The first three all have 64 nodes, and of them, the initial two use a rectified linear unit activation function, while the third uses a sigmoid activation function. The fourth layer has a single node and serves as the output.

Our model must be trained on the data, which is usually split into training and test sets. In this case, we will put 80% of the data into the training set and 20% into the test set. From the training set, 20% will be used as a validation subset. Then, our model examines the datapoints and the corresponding pH values and develops a solution with a fit. With Keras, you can save a history of the reduction in error throughout the fit for plotting, which can be useful when analyzing results. We can see that for our model, the training error gradually decreases as it learns a relationship between the parameters.

The end result is a trained model which has been tested on the test set and resulted in a certain error. When we ran the code, the test set error value was 1.11. As we are predicting pH, a full point of error could be fairly large, but the precision required of any model will depend on the situation. This error could be improved through modifying the model itself, for example by adjusting the learning rate or restructuring layers.

You can also graph the true target values with the model’s predictions, which can help when analyzing where the model can be improved. In our case, pH values in the middle of the range seem fairly accurate, but towards the higher values they become more unreliable.

So what do we do now that we have this model? In a sense, what is the point of machine learning? Well, one of the major strengths of this technology is the predictive capabilities it has. Say that we later acquire some data on a water source without information on the pH value. As long as the rest of the data is intact, we can predict what that value should be. Machine learning can also be incorporated into examination of things such as time series, to forecast a trend of predictions. Overall, machine learning is a very important part of data analytics and the development of powerful AI systems, and its importance will only increase in the future.

What’s next?

As the technology around machine learning and artificial intelligence evolves, Locus will be working to integrate these tools into our EHS software. More accurate predictions will lead to more insightful data, empowering our customers to make better business decisions.

Contact us today to learn how machine learning and AI can help your EHS program thrive

Stories and conversations from this summer’s radiological workshop

Locus recently joined the nuclear power plant community in Orlando, FL for this year’s Radiological Effluents and Environmental Workshop. It’s always a pleasure to join other professionals in a space that encourages discussion, education, and awareness of industry processes and compliance.

Locus Technologies at the NEI Radiological Conference, Orlando, 2019

Bill Donaldson and Danny Moore of Locus Technologies.

Each conference we attend is an opportunity to learn. Whether talking with current or potential customers, it’s always fascinating to hear some of the success and horror stories experienced in their daily operations. We’ve summarized a few of those conversations below. And since REEW takes place in the summer, there’s a theme.

Version Island

Imagine you’ve spent years utilizing a certain feature of your radiological software. You’ve gone through the training process, the growing pains, and you are finally enjoying the fruits of that labor. Now imagine learning that the latest and greatest version being released has removed the feature that you’ve grown to rely on. You are now stuck on a version island. At this point, a costly and time consuming upgrade will cause more problems. Locus SaaS has no version numbers, meaning you will never need to upgrade.

Off the Grid

At one time, the simple columns and rows in Excel seemed to provide a sufficient solution to prepare your REMP sampling data for reports. However, when you need to transfer data between systems, or create more sophisticated reports, those grids begin to feel like prison bars. Maybe it’s time to go off the grid and deploy a more modern solution that can connect and work side by side with your existing tools.

Sunsetting (Decommissioning)

Closing a power plant is a long and involved process that many attendees were in the process of dealing with or will be in the near future. This operational change can be the motivation to rethink the way radiological data is sampled and managed. Some software packages can be too big for the job. Locus offers a modular approach where you only pay for what you need. Choosing which system tools are relevant to the type of data sampling and resources available can minimize implementation cost and increase productivity.

Serene Security

Many people we spoke with at REEW had serious security concerns. Locus takes those concerns seriously. We are SOC 1 and SOC 2 certified and have migrated our software to Amazon Web Services. All customer data is stored with AWS, one of the most advanced and secure cloud-hosting providers on the planet. Locus provides the ability to control user permissions, customizing access based on job duties. This provides a more granular approach to data security.

Making Plans

Using a sample planning application organizes sample events and allows for scheduling weeks, months, or years in advance. Many were interested in this powerful tool that is flexible enough to adapt when a reactor changes modes, allowing for one-time, ad-hoc samples. Mobile applications that integrate with planned samples and events minimize setup, ease data collection, speed up loading field data, and can expedite samples to the lab more efficiently.

Your feedback has helped Locus build a solution that makes it easy to manage all your facility’s data for RETS/REMP, helping you meet your NRC reporting requirements. We enjoyed speaking with everyone at REEW and we look forward to seeing you again next year!

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About the author—Danny Moore, Locus Technologies

Mr. Moore has spent the last decade designing and marketing for enterprise SaaS systems. In his career at Locus, he leads a team of marketing professionals in branding, content creation, social media engagement, and email outreach. Mr. Moore enjoys attending conferences as a Locus brand ambassador and sharing any feedback gained to improve product development.

About the author—Bill Donaldson, Locus Technologies

Mr. Donaldson has 5 years experience in SaaS systems, performing Product Management and QA/QC of Locus Mobile iOS application and Locus’ Environmental Information Management system (EIM). While completing his B.S., Mr. Donaldson held several paid internships, where he configured a Relational GeoDatabase and a Database Management System (DBMS), for biological data entry.

EHS Digital Transformation: Managing Drinking Water Quality Data and Compliance: CCR in the Cloud

In most industrialized cities around the world, drinking water is readily available and safe. Safeguarding groundwater (aquifers), streams, rivers, reservoirs, and lakes is crucial to continue delivering clean water on the tap. So is testing and validated water quality data. There are several aspects of drinking water quality that is of concern in the United States, including Cryptosporidium, disinfection by-products, lead, perchlorates, and pharmaceutical substances.

Recent headlines about water quality issues in cities like Flint, Pittsburgh, Asheville, or Rome and Capetown are motivating consumers to ask more questions about their water quality. Albuquerque’s groundwater is becoming seriously depleted; Fresno’s groundwater is highly susceptible to contamination; In Atlanta, Chicago, Detroit, Houston, Los Angeles, New Orleans, Newark, Philadelphia, Phoenix, San Diego and Washington, D.C., source water is threatened by runoff and industrial or sewage contamination; Water supplies in Baltimore, Fresno, Los Angeles, New Orleans, San Diego, and several other cities are vulnerable to agricultural pollution containing nitrogen, pesticides or sediment.

Locus Technologies IoT Monitoring. Connected at all times.

In most cities in the US, drinking water quality is in conformity with the norms of the Safe Drinking Water Act, which requires EPA to set Maximum Contaminant Levels (MCL) for potential pollutants. In addition, the EPA’s Consumer Confidence Report (CCR) Rule of 1998 requires most public water suppliers to provide consumer confidence reports, also known as annual water quality reports, to their customers. Each year by July 1 anyone connected to a public water system should receive in the mail an annual water quality report that tells where water in a specific locality comes from and what’s in it. Locus EIM automates this reporting and allows utilities to be transparent by publishing CCR online in real time so that consumers have access to their CCR at all times. Consumers can also find out about these local reports on a map provided by EPA.

Utilities must maintain good water quality records and manage them in a secure database with built-in alerts for any outliers so that responsible water quality managers can react quickly when there is exceedance of MCL or another regulatory limit.

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

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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Celebrating 55 years of improving spatial thinking with GIS technology

Today, November 15, is GIS Day—an annual celebration established in 1999 to showcase the power and flexibility of geographical information systems (GIS).

Not only is GIS more powerful than ever before—it is also vastly more accessible. Anyone with Internet access can create custom maps based on publicly available data, from real-time traffic conditions to environmental risk factors, to local shark sightings. Software developers, even those at small companies or startups, now have access to APIs for integrating advanced GIS tools and functionality into their programs.

As the Director of EIM and GIS Development at Locus, I lead efforts to integrate GIS with our software applications to deliver our customers’ spatial data using the latest GIS technology. Let us take a look at how far GIS has come since I started working with it and at some of the new and exciting possibilities on the horizon.

Origins of GIS

Before you can understand where GIS is today, it helps to know how it started out. This year is the 55^th anniversary of the work done by Roger Tomlinson in 1962 with the Canada Land Inventory. We consider this the birth of GIS, and Mr. Tomlinson has been called the “father of GIS”.

The original GIS used computers and digitalization to “unlock” the data in paper maps, making it possible to combine data from multiple maps and perform spatial analyses. For example, in the image shown here from the Canada Land Inventory GIS, farms in Ontario are classified by revenue to map farm performance.

An early GIS system from the Canada Land Inventory, in Data for Decisions, 1967
Photo: Mbfleming. “Data for Decisions (1967).” YouTube, 12 Aug. 2007, https://youtu.be/ryWcq7Dv4jE.
Part 1, Part 2, Part 3

In 1969, Jack Dangermond founded Esri, which became the maker of, arguably, the world’s most popular commercial GIS software. Esri’s first commercial GIS, ARC/INFO, was released in 1982, and the simpler ArcView program followed in 1991. That year, 1991, is also the year I started working with GIS, although I used the TransCAD system from Caliper before starting with Esri software a few years later.

Back then, GIS work required expensive software packages installed on personal computers or large mainframe systems. There was no Google Maps; all map data had to be manually loaded into your software. Getting useful data into a GIS usually required extensive file manipulation and expertise in coordinate systems, projections, and geodesy.

While the government, utility, and resource management sectors used GIS heavily, there was not much consumer or personal use of GIS. As for me, I spent a lot of time in my first job digitizing paper maps by hand or trying to figure out why the map data I had loaded into a GIS was not lining up properly with an aerial photo.

Esri’s ArcView 3.2 for desktop computers (from the 1990s)
https://map.sdsu.edu/geog583/lecture/Unit-3.htm

The Google Revolution

How much has changed since those early days! After the release of OpenStreetMap in 2004, Google Maps and Google Earth in 2005, and Google Street View in 2007, GIS has been on an unstoppable journey—from only being used by dedicated GIS professionals on large computers in specific workplaces, to be accessible to anyone with an internet browser or a smartphone. High-quality map data and images—often the most expensive item in a GIS project in the 1990’s — are now practically free.

Just think how revolutionary it is that anyone can have instant access to detailed satellite images and road maps of almost anywhere on Earth! Not only can you perform such mundane tasks as finding the fastest route between two cities or locating your favorite coffee shop while on vacation—you can also see live traffic conditions for cities across the globe; view aerial images of countries you have never visited, and get street level views of exotic places. Back in 1991, such widespread access to free map data would have seemed like something straight out of science fiction.

Traffic conditions in London, 3:30 pm 10/16/2017, from Google Maps

South Base Camp, Mount Everest, Google StreetView

Mashups in the cloud

Obviously, the amount of spatial data needed to provide detailed coverage of the entire globe is far too large to be stored on one laptop or phone. Instead, the data is distributed across many servers “in the cloud.” Back in the 1990s, everything for one GIS system (data, processing engine, user interface) needed to be in the same physical place—usually one hard drive or server. Now, thanks to the internet and cloud computing, the data can be separate from the software, creating “distributed” GIS.

The combination of freely available data with distributed GIS and the power of smart phones has led us to the age of “neogeography”—in which anyone (with some technical knowledge) can contribute to online maps, or host their maps with data relevant to their personal or professional needs. GIS no longer requires expensive software or cartographical expertise; now, even casual users can create maps linking multiple data sources, all in the cloud.

Google’s MyMaps is an example of a tool for easily making your maps. Maps can range from the playful, such as locations of “Pokemon nests,” to the serious, such as wildfire conditions.

Google MyMaps of Pokemon Nest Locations
https://www.google.com/maps/d/viewer?mid=1anyDs-H7hOtGQaxaTwuLjulUrVM&hl=en&usp=sharing

These online maps can be updated in real time (unlike paper maps) and therefore kept current with actual conditions. Such immediate response is instrumental in emergency management, where conditions can change rapidly, and both first responders and the public need access to the latest data.

Map showing wildfire and traffic conditions in northern California, 10/16/2017
https://google.org/crisismap/us-wildfires

Furthermore, software programmers have created online GIS tools that let non-coders create their maps. These tools push the boundaries of distributed GIS even further by putting the processing engine in the cloud with the data. Only the user interface runs locally for a given user. During this period of GIS history, I created several mashups, including one for viewing natural hazard risks for my hometown. For this application, I combined several data types, including property lines, flood plains, landslide vulnerability, and wildfire risk.

Floodplain data for Buncombe County, NC
https://buncombe-risk-tool.nemac.org

Programming GIS with APIs

Another significant advance in GIS technology is the ability to integrate or include advanced GIS tools and features in other computer programs. Companies such as Google and Esri have provided toolkits (called APIs, or application programming interfaces) that let coders access GIS data and functions inside their programs. While neogeography shows the power of personal maps created by the untrained public, computer programmers can use APIs to create some very sophisticated online GIS tools aimed at specific professionals or the public.

During my 10 years at Locus, I have helped create several such advanced GIS tools for environmental monitoring and data management. One example is the publicly-available Intellus application that Locus Technologies developed and hosts for the US Department of Energy’s Los Alamos National Laboratory. It uses an Esri API and distributed GIS to provide access to aerial images and many decades of environmental monitoring data for the Los Alamos, NM area. Users can make maps showing chemical concentrations near their home or workplace, and they can perform powerful spatial searches (e.g., “find all samples taken within one mile of my house in the last year”). The results can be color-coded based on concentration values to identify “hot spots”.

Map from Intellus showing Tritium concentrations near a specified location
https://www.intellusnmdata.com

Locus Technologies also provides more sophisticated forms of analysis in its EIM cloud-based environmental management system. For example, contour lines can be generated on a map showing constant values of groundwater elevation, which is useful for determining water flow below ground. With such powerful spatial tools in the cloud, anyone at the organization, from facility managers to scientists, can easily create and share maps that provide insight into data trends and patterns at their site.

Groundwater contour map where each line is a 10 ft. interval, from the Locus EIM system

There’s a (map) app for that

One particularly exciting aspect of GIS today is the ability to use GIS on a smartphone or tablet. The GIS APIs mentioned above usually have versions for mobile devices, as well as for browsers. Programmers have taken advantage of these mobile APIs, along with freely available map data from the cloud, to create apps that seamlessly embed maps into the user experience. By using a smartphone’s ability to pinpoint your current latitude and longitude, these apps can create personalized maps based on your actual location.

A search in the Apple AppStore for “map” returns thousands of apps with map components. Some of these apps put maps front-and-center for traditional navigation, whether by car (Waze, MapQuest, Google), public transit (New York Subway MTA Map, London Tube Map), or on foot (Runkeeper, Map My Run, AllTrails). Other apps use maps in a supporting role to allow users to find nearby places; for example, banking apps usually have a map to show branches near your current location.

What’s really exciting are the apps that allow users to enter data themselves via a map interface. For example, HealthMap’s Outbreaks Near Me not only shows reports of disease outbreaks near your location, but it also lets you enter unreported incidents. The GasBuddy app shows the latest gasoline prices and lets you enter in current prices. This “crowdsourcing” feature keeps an app up-to-date by letting its users update the map with the latest conditions as they are happening.

The Outbreaks Near Me app for phones (left) and the GasBuddy app for tablets (right)

Here at Locus Technologies, we use the power of GIS in our Locus Mobile app for field data collection. Users can enter environmental data, such as temperature or pH measurements from a monitoring well, and upload the data back to the EIM cloud for later review and analysis. The Locus Mobile app includes a map interface for navigating to data collection points and tracking visited locations. The app also lets users create new data collection points “on the fly” simply by clicking on the map.

The map interface in the Locus Mobile app; blue dotted circles indicate locations that are not yet started.

Looking to the future

Where will GIS go from here? It’s possible that augmented reality, virtual reality, and 3D visualization will continue to expand and become as ubiquitous as the current “2D” maps on browsers and phones. Also, the “internet of things” will surely have a GIS component because every physical “thing” can be tied to a geographical location. Similarly, GIS can play an important role in “big data” by providing the spatial framework for analysis. It will be interesting to see where GIS is when we celebrate the 20^th GIS Day in 2019!

Thanks to the GIS Timeline for providing some of the history for this article.

About guest blogger— Dr. Todd Pierce, Locus Technologies

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California to Regulate Groundwater in 2015

California’s drought prompted the Legislature into action in 2014, leading lawmakers to regulate groundwater for the first time. The state will begin the long process of regulating groundwater for the first time in the state’s history under three new laws that require local agencies to create sustainable groundwater management plans to ensure priority basins are sustainable by 2040.

Since the state’s founding, water has been considered a property right; landowners have been able to pump as much water from the ground as they want. But increasing reliance on underground water, particularly during droughts, has led to more pumping from some basins than what is naturally being replaced.

On 16 September 2014, California Governor Jerry Brown signed three companion bills, The three bills: SB 1168, AB 1739 and SB 1319, which compose the Sustainable Groundwater Management Act (the Act) create the first comprehensive framework for regulating groundwater in California, placing managerial and monitoring responsibilities in the hands of local agencies while also creating mechanisms under which state agencies may oversee and potentially even intervene in groundwater management. With the Act to go into effect on 1 January 2015, and numerous implementation deadlines, stakeholders throughout the state should prepare for increased regulation, management, and oversight.

The Act requires the establishment of groundwater sustainability agencies (GSA) for groundwater basins in the state. By 31 January 2015, the Department of Water Resources (DWR) will classify each groundwater basin (as identified by DWR Bulletin 118) as high, medium, low or very low priority. GSAs responsible for high- and medium-priority groundwater basins must create and implement a groundwater sustainability plan (GSP) for their basins. Groundwater basins, or portions of groundwater basins, which are subject to a previous groundwater adjudication are exempt from the GSP requirement.

Once formed, GSAs will have broad groundwater management and investigatory powers to prepare and execute the GSP. GSAs may inspect property or facilities to ensure the requirements of the GSP are being met, including use of surface waters. Further, the GSA will have the authority to regulate and limit groundwater extractions, require the submission of annual extraction reports or impose well spacing requirements, among other substantial powers.

The Act requires that GSPs be designed to achieve “sustainable groundwater management” for the basin within 20 years of implementation. “Sustainable groundwater management” is defined as the maintenance of groundwater use in a manner that does not cause “undesirable results.” An undesirable result is the occurrence of at least one of the following:

Chronic lowering of groundwater levels, indicating a significant and unreasonable depletion of supply.
Significant and unreasonable reduction of groundwater storage.
Significant and unreasonable seawater intrusion.
Significant and unreasonable degradation in water quality.
Significant and unreasonable land subsidence that substantially interferes with surface land uses.
Surface water depletions that have significant and unreasonable adverse impacts on beneficial uses of the surface water.

California’s Water Shortage

A new paper published in Nature Climate Change, by NASA water scientist James Famiglietti, presents the chilling reality of California’s ongoing drought crisis. “The Global Groundwater Crisis,” uses satellite data to measure the depletion of the world’s aquifers, and summarizes the effects this has on the environment.

These aquifers contain groundwater that more than 2 billion individuals rely on as their primary source of water. Groundwater is also essential, as it is one of the main sources we rely on to irrigate food crops. In times of drought, the lack of rain and snow results in less surface water (the water that settles in lakes, streams, and rivers). Thus, farmers must rely on available groundwater to irrigate their crops, leading to rapid depletion in areas of high farming concentration.

California’s Central Valley has been one of the most effected regions in the state. The map below depicts groundwater withdrawals in California during the first three years of the state’s ongoing drought.

According to James Famiglietti, “California’s Sacramento and San Joaquin river basins have lost roughly 15 cubic kilometers of total water per year since 2011.” That means “more water than all 38 million Californians use for domestic and municipal supplies annually—over half of which is due to groundwater pumping in the Central Valley.”

As more water is pumped from the aquifers, things can only get worse. As this trend continues, wells will have to be dug deeper, resulting in increased pumping costs. This, in turn, will lead to a higher salt contents, which inhibits crop yields and can eventually cause soil to lose productivity altogether. Over time, Famiglietti writes, “inequity issues arise because only the relatively wealthy can bear the expense of digging deeper wells, paying greater energy costs to pump groundwater from increased depths and treating the lower-quality water that is often found deeper within aquifers.” This problem is already apparent in California’s Central Valley. Some low-income residents are forced to let their wells go dry, while many other farmers are forced to irrigate with salty water pumped from deep in the aquifer.

The lesson we can learn from Famiglietti’s research is that “Groundwater is being pumped at far greater rates than it can be naturally replenished, so that many of the largest aquifers on most continents are being mined, their precious contents never to be returned.” This problem of diminishing groundwater is perpetuated, due the lack of forethought, regulation, or research concerning this water source. Famiglietti contends that if current trends hold, “groundwater supplies in some major aquifers will be depleted in a matter of decades.”

Without any change of practices, we can expect steeper droughts and more demand for water. Famiglietti suggests that if we ever plan on getting the situation under control, we must start carefully measuring groundwater and treat it like the precious resource that it is. However, if the globe continues on this path without any adjustment, it will most likely result in civil uprising and international violent conflict in the water-stressed regions of the world.

Water Scarcity Shines Spotlight on the Fracking Industry

The World Resources Institute (WRI) has released a report that highlights the potential for water scarcity to put a halt on fracking among the world’s top 20 shale countries.

In one of these countries—the United States—fracking has been used for years. However, new technology has enabled companies to drill deeper and horizontally, allowing fracking in more populated areas than ever before. These modern fracking techniques require millions more gallons per well of water, resulting in millions more gallons of contaminated wastewater. This increased amount of water usage results in two major causes for concern: water scarcity, and groundwater contamination.

Adding to this concern, the WRI report states that 38 percent of the world’s shale resources are found in areas that are water barren or “under high to extremely high levels of water stress”, and 40 percent of countries with the largest shale reserves have severely limited freshwater sources. With the spotlight being shined brighter than ever on fracking’s relationship with water, the WRI has compiled a list of actions for these operations to take in order to help preserve the integrity of water supplies. The list is made up of four recommendations.

First, the WRI suggests conducting water risk assessments to understand local water availability and reduce business risk. Next, increase transparency and engage with local regulators, communities, and industry to minimize uncertainty and ensure adequate water governance to guarantee the security of the water and reduce risks. The last action the WRI recommends is minimizing freshwater use and engaging in corporate water stewardship to reduce impacts on water availability.

Current findings and water shortages suggest an urgent need for improved monitoring and transparency for operations within the fracking industry. Using a centralized system for managing crucial fracking information can increase transparency, improve compliance with current regulations, and better protect the quality and quantity of the world’s water supplies.

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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.

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Origins of GIS

The Google Revolution

Mashups in the cloud

Programming GIS with APIs

There’s a (map) app for that

Looking to the future

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