Methods and Methodology | Locus Technologies

Technology Outlook for the Environmental Industry

Neno Duplan is founder and CEO of Locus Technologies, a Silicon Valley-based environmental software company founded in 1997. Locus evolved from his work as a research associate at Carnegie Mellon in the 1980s, where he developed the first prototype system for environmental information management. This early work led to the development of numerous databases at some of the nation’s largest environmental sites, and ultimately, to the formation of Locus in 1997.

Mr. Duplan recently sat down with Environmental Business Journal to discuss a myriad of topics relating to technology in the environmental industry such as Artificial Intelligence, Blockchain, Multi-tenancy, IoT, and much more.

Download a preview

Click here to learn more and purchase the full EBJ Vol XXXIII No 5&6: Environmental Industry Outlook 2020-2021

5 Keys to Simpler Air Quality Monitoring

Time management is an ever-present struggle. With expanding air quality monitoring and regulatory programs, more is expected from air quality professionals without compromising work quality. Locus Technologies offers the tools to ease your workload. Here’s how Locus transforms your air quality data and reporting management:

Integration

Integration can save you a great deal of time and stress with the most cumbersome air quality data management duties. Our air quality software has a unique point and click integration application enabling connection with major databases and third-party systems that have open API (access privileges). Some integration, database, and communication standards and methods that are supported include OLE compliance, SOAP, COM, Java, XML, web services, DBC/ODMA/SQL/Oracle, AWS, VIM, and MAPI.

Locus also provides a powerful two-way synchronization with MS Excel, allowing users to download to Excel, then work, edit, verify, or append data on their local copy of Excel. Any revisions they perform to the downloaded data can be automatically synchronized back to the Locus Platform application. During the process, a complete audit trail will be preserved. This is a great time saver, especially if you are sending large volumes of valid values in a database or if you are migrating any historical data.

Dashboards Tailored to Your Needs

Your air quality data management software should have built-in dashboards to meet your needs. With other software providers, when you need a new report, chart, or other visualization of your air quality data, it usually incurs a custom software development charge. Locus allows you to assemble the information you want in your chosen format (bar or line charts, maps, tables, treemaps, diagrams, etc.) and share your custom dashboards and real-time information/data with your team or regulators without the fees. In addition, the views and dashboards export to Excel, so you can easily integrate with commonly used tools and further mine the data.

With Locus, powerful dashboards will help you understand the status of single of multiple facilities in an air quality program based on a matrix you design. With the the flexibility of Locus, facility information can be automatically populated based on the user credentials, saving you and your team time and frustration.

Simplified Reporting

Locus Platform’s air quality application and calculation engine supports simultaneous calculations using multiple methods for various reporting programs including EPA, State, or Local, CDP, TCR, DJSI, Title V, e, and others. Our software also assists in streamlining your emissions tracking and reporting requirements for programs such as GHG, Fenceline, Title V, and LCFS. Locus air quality software is fully integrated with our compliance/asset management and remote sensing systems, making digital transformation more efficient. In addition, Locus’ vapor intrusion and indoor air management application will easily organize, manage, and report indoor air and vapor intrusion data.

This allows users to input data only once and utilize it to report to multiple federal, state, and voluntary reporting programs, according to your required format. The application will also support direct electronic reporting formats for many reporting programs, so that additional manual transcription and submittal of data are no longer necessary. This is a very powerful tool and a huge advantage to customers in terms of improving efficiency, while reducing costs.

Mobile

Locus’ Mobile application allows you to sync with your server to create in-field data collection profiles on a mobile device, whether it’s your phone or a tablet. It will allow you to click through and enter field inspection data on the device even when you are offline. Air quality field operations data validation is performed in real-time and is stored locally on the device when you are out of service range, with data will automatically being updated in Locus’ cloud when you have connection.

Locus gives the benefits of data entry directly on the mobile device, with immediate data availability on the cloud when you reach an internet signal. Other advantages of using Locus Mobile includes location metadata and mapping integration, bar-code/OR code scanning, voice recognition, and form customization.

Easy to Use Calculation Library

To alleviate the effort in researching complex air emissions calculations ranging from GHG to Tank emissions, Locus has designed a Java Library, Curta, for complicated scientific computing on our software. Curta contains a collection of built-in functionality, unit conversions, periodic and hierarchical calculations that can be used to solve mathematical models of problems in Science and Engineering.

Curta can be used directly as API (Application Program Interface) in the UI (User Interface) design, or implicitly combined with the Locus Platform Sustainability application with clear break down into calculation indicators and sources. It offers an integrated solution to work with different data types, continuously changing inputs and large set of unknown variables.

Curta features include:

Calculation engine suite Independent code base for Curta only, safe and stable for any applications and platform.
Sequential calculation steps Curta can construct multi-step calculation structure where formulas can build on each other without knowing the exact values at the initiation of the calculation.
Conditional calculation logics Calculation steps can be set with conditions and logic for example effective date, input units, tank type etc.
Hierarchical calculation results Calculations can be designated to sources with hierarchy with Curta able to acknowledge the parent-child relations of the sources and present it as a calculation tree.
Execute parallel calculations for periodic data Curta can repeatedly conduct complicated calculation structure on a periodic base.
Execute parallel calculations for multiple sources Curta can repeatedly conduct complicated calculation structure for multiple linked sources for example facilities, tanks etc.

Contact us to see Locus’ Air Quality App in action

Stay in Compliance With Smart Sample Planning and Management Tools

Imagine the time savings and the simplicity of having your regulatory requirements all lined out for the year without having to worry about missing required samples. For water utilities, this is especially valuable given the strict schedules and public health implications of missing sampling events. Locus sample planning streamlines repetitive sampling, such as required samples for drinking water or monitoring wells. Any sampling events can be planned and reused repeatedly, even with tweaks to the schedule for the samples to be collected. We’ve outlined some key features of Locus sample planning in this infographic.

Contact us to see Sample Planning in action

Evergreen Natural Resources Selects Locus Technologies for Environmental Software

Locus will provide environmental field and analytical data management software for Evergreen Natural Resources.

MOUNTAIN VIEW, Calif., 17 March 2020 — Locus Technologies, industry leader in environmental software, today announced that Evergreen Natural Resources, a privately-held energy company based in Denver, Colorado, has chosen Locus environmental software for their data collection and management.

Evergreen Natural Resources has selected Locus’ environmental software, EIM, after proof of concept and usability testing. They will seek to utilize Locus EIM as a laboratory database management system, and for regulatory report generation, while also taking advantage of Locus’ premium GIS tool, GIS+, as well as Locus Mobile.

“With over 2,600 unique locations that require routine sampling, Locus’ environmental and GIS software allows us to collect, manage, visualize, and analyze data. Locus EIM aligns with our strategy to increase availability and reduce our internal application infrastructure footprint,” said Cesar Zayas, IT Director of Evergreen.

“Evergreen Natural Resources is a rapidly emerging company in the energy sector, and their decision to utilize Locus’ powerful environmental software shows their objective to manage their data quality at the highest level. Our scalable software will match their continued growth,” said Wes Hawthorne, President of Locus.

A Visualization is Worth a Thousand Data Points

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.

[sc_button link=”https://www.locustec.com/applications/environmental-information-management/” text=”Learn more about Locus EIM” link_target=”_self” color=”#ffffff” background_color=”#52a6ea” centered=”1″]

[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.

An EHS&S Look Into the Tech Used in the Iowa Caucuses

The Importance of User Implementation and Quality Assurance from an EHS&S Software Provider

After reading about the IowaReporterApp used during the 2020 Iowa caucuses, it struck me how remarkably similar it is in intended function to the EHS&S software developed by my employer, Locus Technologies. Both their application and Locus’ mobile technology collect large quantities of sensitive data from several remote users at multiple facilities, allowing for instant calculation and reporting. What surprised me though, is just how vastly different their user implementation and data management methodology was from what is standard operating procedure at Locus.

In this blog, I will highlight some of the pitfalls of the IowaReporterApp, and compare it to Locus’ EHS&S software. Note, this article is not a political critique, but is an examination of data collection and data quality methods used during the caucuses.

Implementation

Users were introduced to the IowaReporterApp just days before the caucuses and received no app-specific training. Many users were downloading the application on the night of the caucuses.

The Iowa caucuses have been held biennially for almost a half-century as the first major contest of the primaries. The date of the caucuses was a surprise to no one. As a result, app development deadlines should have given enough time for user implementation, through one-on-one training or presentations with appropriate support staff. If app deadlines were not met, there should have been a fallback to redundant reporting systems, like in the case of Nevada, who were also planning to use the app but have opted out after the debacle in Iowa.

When Locus introduces new users to our software, we take implementation seriously. Our customer support team is composed of domain experts who have actively built and used Locus software. We know the deadlines and the problems users typically face during the implementation process. From one-on-one and on-site training to quick turnaround, our support team does everything they can to ensure that users are comfortable with our product as soon as possible.

Untested Software/Quality Assurance

User implementation deadlines are all the more important given that the software had no real-world use to this point. While it is not advisable to go live with untested software, at the very least, having users stress test a product before field-use could have staved off a few issues.

This is something we see frequently with newer products and newer companies. Locus has over 20 years of experience creating EHS&S software used by U.S. government organizations and Fortune 500 companies. Our quality assurance team rigorously tests any update we bring to customers and doesn’t rush changes to sell a platform update, since every user is always on the same version of Locus software.

Data Redundancy

No one can question that Excel or Google Spreadsheets can perform math correctly, but what is frequently overlooked or not even considered, are the macros, custom functions, and calculations that are often added to spreadsheets when deployed for managing data and other tasks. If one fails, there need to be backups for reporting and storing data.

When the untested application predictably failed, users flocked to the phone lines as a redundancy. Manual data collection on such a scale created confusion and could not carry the load, and had no way of accounting for errors in data entry. At Locus, we understand the importance of EHS&S data, and maintain backups and full audit trails for all critical data, with quick restoration available so you can keep going if anything should happen.

Security

The IowaReporterApp was not released in time to get approval in Apple’s app store, and it was sent out through beta testing platforms which required suspension of smartphone security settings.

ProPublica, a nonprofit organization who produces public interest investigative journalism, did a report on the security of the IowaReporterApp after the Iowa Caucuses. Shockingly, they found security problems to be “elementary” and that the app was so insecure that vote totals, passwords, and other sensitive information could have been intercepted or changed. Luckily, there seems to be no evidence of hacking or tampering with results.

Locus understands the need for security with sensitive data, and hosts our entire infrastructure in the most secure and reliable cloud, Amazon Web Services. AWS has an unmatched portfolio of cloud services that Locus fully utilizes to the benefit of their customers.

Summary

Overall, I think that the mishaps related to the IowaReporterApp show just how easy it is for a data collection and management application to fail if not properly implemented and ran by those with years of practical expertise. Subverted data quality will always be extremely costly to your organization, both financially and otherwise, and should be avoided unequivocally.

Locus Technologies was founded in 1997. Locus’ environmental data management software currently handles over a half billion sensitive records taken from over one million unique locations and is used hundreds of organizations including the government agencies and Fortune 500 companies. Aaron Edwards received his bachelor’s degree in Political Science from UNC Asheville and is Marketing Associate for Locus Technologies. He is an active voter, and is unaffiliated with any political party.

[sc_button link=”https://www.locustec.com/why-locus/” text=”Why Locus?” link_target=”_self” color=”#ffffff” background_color=”#52a6ea” centered=”1″]

How to extend your EHS software with integrated systems

Integration with other systems, whether on-premises or in the cloud, has become a key wishlist item for many EHS software buyers. It allows you to take advantage of other tools used by your organization (or available from third parties) to simplify processes, access information, and enhance communication, both internally and externally.

“49% of EHS software buyers are unhappy with their current solution’s poor integration with internal IT systems.”

NAEM 2017 EHS & Sustainability Software Buyer’s Guide

This blog will take a look at some common types of integrations we most hear about when talking with EHS professionals.

Integrate on-premises systems with cloud EHS software to provide a seamless business process
Integrate with identity providers to enable single sign-on
Integrate with public API (like EPA or regulatory information providers) for data submittal or private commercial APIs for proprietary content
Integrate with multiple sources for consolidation and review of disparate data sources (portal integrations)

Integration with on-premises systems and cloud EHS software

Many potential software buyers want to integrate an existing on-premises system with a Software as a Service (SaaS) system. The SaaS integration advantage is accessing information in existing systems without the additional user burden of using multiple software systems, making it easier to perform parts of a unified business process.

A good example is creating a cloud system that integrates with an onsite Enterprise Resource Planning (ERP) system such as SAP. Many business processes must connect external vendors with internal resources to track work, scope, invoices, and payment. Cloud systems are ideal when external vendors need internal interactions.

In this example, previously, vendors sent invoices as email attachments, then had to be manually entered into the owner’s ERP system. The vendors had zero visibility into the processing status of the invoice, thus slowing down the flow of information between the owner and vendors. The owner wanted to create a single view of the contracts, vendors, the approved budgets, and the payment approval status to streamline the process and enhance transparency.

To meet the customer’s enhancement request of a “Single View” data had to be integrated securely between the on-premises secure ERP system and the cloud system. First, the owner established a secure web service API. The cloud system authenticates and consumes the API over an encrypted connection to integrate the process. Maintaining security for all parties, and application users have full access to the specific data they needed to complete their business flow.

One caveat to this type of solution is that the owner’s IT personnel are often involved in establishing a pathway to the internal data and gaining internal approvals for this to happen. Therefore, if you are considering integration with internal systems, check with your internal resources to make sure they can accomplish your goals while adhering to your corporate IT security policies.

Integration with identity providers

Many companies request single sign-on (SSO) for their users when accessing a third-party cloud software. This functionality can be a key purchase criterion for selecting a software vendor. SSO integration termed “integration with identity providers,” is especially important for large enterprises and many geographically distributed users.

Using SSO, company employees authenticate in their own employee portal. When an employee clicks on a link to the software provider, they are taken to the provider’s website and presented with the correct information based on her authorization, without having to log in again.

One approach to providing this functionality is to use Security Assertion Markup Language (SAML) assertion through integration with the company’s identity provider (IdP). In a typical use case, the vendor software maintains the user identities and permissions for every employee at the company needing access to the EHS software. When an employee of the company accesses the vendor’s SaaS applications, the SaaS sends an authentication request to the company’s IdP at this point he IdP authenticates the user and sends a SAML response. Allowing the user access the relevant parts of the vendor’s SaaS software.

The user experience is a seamless workflow and one less set of credentials to manage. It also provides a method to simply and quickly remove users when they leave the company or no longer need access to the software. Once removed from the company authorization, access to the external software is also removed.

Integration with public/private APIs

Government agencies and other public/private entities are increasingly delivering services or requiring data submissions via publicly available APIs (Application Programming Interfaces). API’s help both parties by significantly streamlining data submission (such as submitting hazardous waste manifests to EPA). More importantly, electronic data submissions all but eliminate the tedium and the likelihood of errors in manual and repetitive data entry. However, the benefits of API data exchange impact users only if their software is capable of making the connection and sending the information safely and accurately to the intended recipient.

EPA’s public REST API for submitting GHG emissions reports

One example where APIs are extremely useful is in the submitting annual GHG (greenhouse gas) emissions to EPA. Previously, GHG regulatory report submission was a manual process on the EPA website. Data was typically calculated in spreadsheets for multiple sites and manually entered into the EPA website. This manual process was both slow and error prone.

To streamline the regulatory reporting process, EPA introduced an option to upload files that are formatted per EPA specifications. The specification allows combined data from each reporting site (for multi-site facilities) to be submitted as a single consolidated XML file.

The software can provide reporting tools that make it easy to run multiple reports and combine results in a single XML, which is designed to be suitable for submission to EPA. In the future, when EPA provides an API, the process will become even more streamlined for data owners, and the upload portion of the process can be eliminated.

EPAs e-Manifest system has recently gone live and is another great example of integration with a public API. Automating submission of hazardous waste manifests will be a huge time saver for all entities that are required to ship waste. The new system will also finally put to rest the clunky dot matrix printers that are kept around only to print out these manifests. For this type of integration, look for built-in tools to configure automatic submission of manifests with EPA’s public REST API web service. Because this is new (July 2018), expect some trial and error on the first submissions, but after the integration is worked out, it will be a game-changer for companies required to ship and transport waste.

GIS integrations for visualizing site and facility data

Mapping and geographic information systems are another popular public API integration option for EHS software. Using Esri (a leading geographic information system) public APIs, EHS software can validate location data for address formatting and accuracy across a range of applications. This type of integration is typically easy to configure and you just need to create the business rules that integrate with Esri APIs to check, format, and store the correct address and geo-coordinates for locations.

Google Maps APIs can be used to show relevant maps of sites or facilities and overlay useful information like terrain, demographics, or traffic to make the EHS data more meaningful and understandable. Similarly, devices with GPS tracking can be visualized in dashboards to see current sampling locations or inspection locations in a map view. These types of integrations are very familiar to most software users and are easy to configure as most sources of information are readily available publicly and come with well documented API information.

Content services integrations provide up-to-date regulatory notifications

From a private API perspective, consider content management services like RegScan and Specialty Technical Publications (STP). Using services like this, companies can connect with third-party content providers to get information about the latest important environmental regulations delivered within their software application. This is a great concept as no software vendor can excel at all dynamic regulatory programs, so it makes sense to purchase the information from providers who specifically focus on certain types of content. Another example is product regulatory compliance or online Material Safety Data Sheets (MSDS).

Any modern online specialty knowledge vendor will be able to supply an API for accessing and integrating their service information into an existing modern EHS software solution. This means EHS software users can access articles that provide clear analysis on evolving environmental regulations or other topics of targeted interest with the content managed by the content provider. Additional features may also be available from the content vendor such as alerts or notifications so content consumers can stay up to date with changes. Through robust integration via public or private API, software vendors can provide enhanced content to users well beyond what the software vendor natively supplies.

Two major advantages of public and private APIs is that no permissions are needed to access the information, and that users have immediate access to current and reliable content at all times. Effective integrations can reduce the time needed to research related information from other sources and eliminate many manual errors by having applications connect directly to each other.

Portal integrations

Another common integration request is “portal integration”, or the melding of various streaming data sources (such as “big data” or IoT data) into a single system to enable better data analysis and insight. For example, many companies have multiple continuous monitoring systems (CEMS) that generate huge amounts of data at frequent intervals. With such huge volumes of data, it is hard to review and take action without condensing the information into an understandable format.

A modern SaaS platform with built-in integration tools is essential to bring various data sources together and display the information in a meaningful way. Look for dashboards designed to handle this type of data that provide a way to integrate data from different data sources into a single unified view that is easier to interpret. Look for tools that make it easy to combine and present data using different types of graphical charts and as GIS maps.

Like integrations with on-premises systems, system owners will need to be involved in setting up the integration, and software collecting the streaming data will need to be sophisticated enough to be readable by modern systems. If you’re using legacy data collection systems such as SCADA in your organization, you can integrate with those systems as well, avoiding the need for costly hardware upgrades. However, its best to check with the system owners to ensure their systems are able to integrate before you start your EHS system planning.

Careful planning to ensure integration success

With all the advances in software platforms and commercial data sources providing enhanced linkage to data that was previously unavailable or behind firewalls, EHS software customers have a lot to consider when evaluating options. In the last several years, software integration has become a hot topic and something most EHS departments are at least talking about. If you’re evaluating EHS software solutions, you would be wise to add one or more of these integration capabilities to your “wish list” for any potential vendor solution.

Consider the exact information you want to bring into your EHS software, the quality of the information you want to consume, and the reliability of the source.

Also, remember that internal and external data providers may upgrade or change over time. For that reason, the ease and reliability of integration is an important parameter to consider.

There are many clear benefits to taking advantage of modern integrated software tools wherever you can in your EHS processes. Even if some integrations are only optional for your needs, consider the benefit to your organization in simplifying your EHS software implementation, maximizing other available resources, and improving the reliability and accuracy of data sources driving your EHS decisions.

Integrations are sometimes initially perceived as an optional feature, but you should consider making it a requirement for your EHS software based on these benefits. Moreover, as new integration tools increasingly become available, you’ll find more value out of having a system that can use them to their full advantage.

NAEM recently published this blog as a part of their Green Tie series. Read it here.

Interested in learning more about integration? We recently published a detailed white paper on integrated systems for EHS software. It is available as a free download.

[sc_button link=”https://www.locustec.com/white-paper/extend-ehs-software-integrated-systems/” text=”Download free white paper” link_target=”_self” background_color=”52a6ea” centered=”1″]

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!

[sc_button link=”https://www.locustec.com/applications/gis-mapping/” text=”Learn more about Locus’ GIS solutions” link_target=”_self” color=”#ffffff” background_color=”#52a6ea” centered=”1″]

[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

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.

Integration

Dashboards Tailored to Your Needs

Simplified Reporting

Mobile

Easy to Use Calculation Library

Contact us to see Locus’ Air Quality App in action

Contact us to see Sample Planning in action

Visualize environmental data with Locus EIM.

The Importance of User Implementation and Quality Assurance from an EHS&S Software Provider

Implementation

Untested Software/Quality Assurance

Data Redundancy

Security

Summary

“49% of EHS software buyers are unhappy with their current solution’s poor integration with internal IT systems.”

Integration with on-premises systems and cloud EHS software

Integration with identity providers

Integration with public/private APIs

Portal integrations

Careful planning to ensure integration success

1. Migration to AWS Cloud

2. SSO Login

3. GIS+ Data Callouts

4. EIM One

5. IoT data support

6. CIWQS and NCDEQ exports

7. Improved Water Utility reporting

8. Improved Non-Analytical Views

9. EIM search box

10. Historical data reporting in EDD loading

Contact us to see a demo of Locus EIM

Space and Time

Maps and Time

GIS and Time

Interested in Locus’ GIS solutions?

What’s next?

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

Locus Technologies

Quick Links

Site Search