Esri, as the world's sixth largest privately-owned technology company, a world leader in geographic information system software, spatial data science research and development, and a strong record of supporting thousands of environmental conservation initiatives since our founding in 1969, holds a vision to conduct business in harmony with the environment. This embodies Esri’s core values in the way we function, and spurs us on toward our mission of continually embedding sustainability in our internal operations (e.g., www.esri.com/en-us/about/sustainability-statement). As part of our values we acknowledge that everyone and everything in this world is connected. One does not have to lose profits by conserving – everyone needs clean air, clean water, everyone has a sense of place. It does not have to be good vs evil. We care about the same things no matter what business sector we represent or where we fall on the POLITICAL spectrum: we want a safe place to live, clean water, clean air for our families, safe food, gainful employment; good health for our family, friends, loved ones, our community, city, country, world. Science-based land and ocean conservation serve to ensure this.
Esri’s ongoing sustainability initiative, includes a new strategic plan, a host of performance reporting and employee community networking and sustainability improvement projects, all in keeping with the #TogetherWithNature principles. This plan includes:
Continuing to Reduce our greenhouse gas (GHG) Footprint by identifying our current scope of emissions and working to reduce our net GHG emissions.
Quantifying Sustainability in Esri Operations by following policies and practices to reduce our environmental impact, exceed environmental standards where practicable (such as Global Reporting Initiative (GRI) Standards and the GHG Corporate Standard), and measuring our success. We currently deploying our own host of company-wide sustainability performance dashboards, built with our own GIS technology, that tracks pounds of CO2 emissions avoided, pounds of e-waste and hazardous waste recycled, gallons of water saved, kilowatt hours saved, pounds of particulate emissions saved, and annual car CO2 emissions avoided.
Increasing Environmental Stewardship: as sustainability lies deep in our company’s values, we aim to foster sustainable practices within our organization and among our employees, distributors, and partners, including the conservation and protection of existing ecosystems on our campuses, within our vast communities, and at the global level through deep participation in initiatives such as the EO Wilson Biodiversity Foundation’s Half-Earth, Microsoft’s AI for Earth and Planetary Computer, National Geographic’s Earth Pulse, Nature Serve’s Map of Biodiversity Importance, and the United Nations Sustainable Development Solutions Network, and more.
Enabling Sustainability Solutions: The largest impact we can make in creating a sustainable world is through our users. We work to enable our user community of 350,000 organizations worldwide to build sustainability solutions with our location intelligence technology. To make careful and consistent decisions on issues such as fair and sustainable economic models that create new employment opportunities; where to engage underserved customers; where a company’s carbon footprint is rising or falling; where employees’ income levels fail to match the local cost of living or reveal discrepancies like a gender pay gap, companies need a way to monitor, manage, and report on their activities. One way to do this is to focus on the element that grounds nearly every social responsibility issue: its geographic dimension—the whereof what happens. For a holistic view of where an organization’s work supports its broader social duties—and where improvements are needed—companies are moving toward the idea of smart mapping to create a corporate responsibility map. In smart maps, organizations have a powerful tool to plan, track, and manage efforts toward responsible practices in every geography they touch. Many have already harnessed location intelligence generated by smart maps to grow their businesses profitably. Now, they’re finding this geographic guidance indispensable in the era of stakeholder value.
Many thanks again to those who attended the Science Symposium as part of the 2020 virtual Esri User Conference. Your participation made for the greatest event ever, with 1183 attending from at least 20 countries....
... and the amazing keynote address by Katharine Hayhoe generated 140 audience questions and comments within Slido, as well as some great audience responses in terms of polls and word clouds within Poll Everywhere!
We, of course, were not able to get to all of the questions, and Katharine and I recorded a second podcast between us (Climate Change is Here and Now) as a way to answer a few more of the remaining questions and take on related issues. This podcast was released in mid-November and was also announced via @khayhoe, @deepseadawn and @GISandScience on Twitter, as well as LinkedIn. See also our first podcast, Climate Change: Science, Solutions, Hope, released in June.
We are pleased to share with you these items by way of Science Symposium followup:
a recording of the entire session is available within the UC 2020 platform for those of you who were officially registered for the UC. If you log in again, you can re-watch the session as many times as you would like at uc2020.esri.com/live-stream/15346311/Science-Symposium.
October 1st update! The full recording is now available on YouTube !
the introductory slide deck of Esri Chief Scientist Dawn Wright is still available at this link;
the slide deck of keynote speaker Prof. Katharine Hayhoe is now available at this link;
Best Practices for Geospatial Validation of Flood Forecasts via Citizen Science
'Catch the King' Tide is a successful citizen-science GPS data collection effort that maps king tides in Hampton Roads, VA. Hundreds of volunteers map maximum flood extents using their phone's GPS to validate and improve high-res. inundation model predictions. 150,000+ geotagged high water marks and photos have been captured using the Sea Level Rise and ArcGIS Collector Apps to trace floodwaters in VA since 2014: https://www.esri.com/about/newsroom/blog/mapping-virginias-highest-tide/
1:Many | Leveraging the Gamut of Esri Products to Restore a Species
With over 600 Eastern Bluebird boxes spread over 27,000 acres of land, Three Rivers Park District innovated a way for citizen scientists to collect critical habitat information through an easy to use application while managing the restoration of a species. Learn about how GIS staff capture real time information leveraging related record collection in Survey 123, arcade visualizations, and distributed collaboration to power their solution.
Esri ArcGIS-based Submarine Cables of the World Interactive Map
98% of the world’s internet runs on subsea cables, but as an industry, we have not had a suitable, free mapping tool of systems. STF used a managed crowdsourcing effort for data collection – all system information is derived from the public domain by an analyst as well as update recommendations. STF’s online map was built with the industry-standard Esri ArcGIS platform and linked to the STF Submarine Cable Database. Systems are linked to STF’s News Feed for current news about the system.
Live Fire Map: Crowdsourcing Early Detection of Wildfires for Public Awareness
The Live Fire project uses crowdsourced monitoring by trained volunteers to identify new wildfires and quickly map them on a public information map. Fire reports are identified from sources including social media, camera networks, radar tracking of fire aircraft, radio scanners, and official sources. After validation for accuracy and geolocation, each report is added to the Live Fire map, including a link to the official responding agency to enable public users to obtain additional information.
Leveraging ArcGIS & StoryMaps to Communicate Shelter Locations During Disasters
For all major US hurricanes since 2018, CEDR has gathered shelter locations from county emergency management & other local sources via crowdsourcing and automated techniques. Our shelter feature layer is relied upon by FEMA and included in public maps from organizations such as NAPSG. Additionally, in 2019, CEDR documented location issues with Puerto Rico shelters via a Story Map, which provided a rich way to communicate a detailed data analysis effort clearly in a compelling visual format.
Crowdsourcing Real-Time Real Estate Data Through GIS
Commercial real estate markets are large and complicated, making it difficult to engage and educate audiences. Linking 3D buildings within Esri Portal to a CRM system creates a platform where users can easily explore markets and instantly access crowdsourced data in a real-time, two-way communication exchange. We will explore everything from linking extruded buildings with dynamic backend datasets across geographies to ensuring users have access to the best crowdsourced content.
Are you working with complex scientific multidimensional datasets? Would you like to explore and learn how to use powerful tools and capabilities to help solve your problems? Many workflows can help with your analytical needs, but you may be wondering where to start. In this post, we will walk you through how to incorporate a multidimensional scientific data workflow (ingest, visualize, analyze, and share) within ArcGIS and which of Esri’s latest multidimensional geoprocessing tools you can use. You will also learn a simple way to build and share your analytical science products using NetCDF, HDF, and GRIB (curated by NOAA and NASA).
One effect of climate change is changes in precipitation patterns. As sea surface temperatures increase, these patterns change and areas around the globe experience either anincrease or decrease in their annual precipitation. Current studies indicate that theSahara Desert is expandingdue to decreased precipitation over the region. At the same time, South America is experiencing a slight increase in precipitation inpast and severe storms. Much of the world’s freshwater supply is replenished through precipitation, so it is vital that we understand the changes already occurring. In this brief investigation, we will usemultidimensional NOAA datashowing monthly global precipitation from 1900 to 2017 to analyze and predict precipitation trends around the globe. We will also take a closer look at the Sahara desert and Amazon rainforest regions. If you wish to follow along, you can download the datahere. We will use theArcPyto ingest the data and analyze it inArcGIS NotebooksandArcGIS Pro.
The first step is to ingest the data so you can visualize it in ArcGIS Pro. We will do this using theArcPy GP tools. The ArcGIS Notebook code shown here creates a raster object from a multidimensional raster dataset and applies the stretch function for better visualization.
Once you ingest the data, now you can explore the data structure and its variables as shown below.
Working with ArcGIS Notebooks allows you to access external python libraries to extend your analysis, but it also allows you to visualize and manipulate your data using ArcGIS Pro. Data may be displayed in the ArcGIS Notebooks window, but it can also be added to a map in your ArcGIS Pro project where you can work with it as you would in any other project.
Once the precipitation data has been loaded into the new multidimensional raster, you can begin to explore the data and look for trends. This data includes monthly precipitation totals from 1900 to 2017. This means there are 1,404 slices of precipitation data represented here (117 years of monthly data), which gives you enough data points for your trend analysis. To begin, you will need to aggregate your monthly precipitation data into yearly precipitation.
Now that the data has been aggregated to mean annual precipitation, let’s take a look at the Sahara desert and Amazon rainforest regions using theTemporal Profile Charting tool.
Figure 3: Region of Interest used for charting change precipitation change
Figure 4: Precipitation change in the Sahara desert region
Figure 5: Precipitation changes in the Amazon rainforest region
The Temporal Profile Charting tool plots the mean annual precipitation on the vertical axis and time on the horizontal axis. It can also be used to generate trend lines, shown here in red. From these trend lines, we can see that the average annual precipitation in the Sahara desert region has decreased over time. Trends in the Amazon rainforest region are less clear, but precipitation appears to have slightly increased in this region over time.
Detecting Precipitation Anomaly
Another way to look at changes in precipitation is by detecting anomalies. The term “anomaly” means a departure from a reference value or long-term average. A positive anomaly value indicates that the observed precipitation was greater than the long-term average precipitation, while a negative anomaly indicates that the observed precipitation was less than the long-term average precipitation. To do this, we will use theGenerate Multidimensional Anomalytool. We will use this tool to compute the anomaly for each time slice in the multidimensional precipitation raster. The anomaly data will let us see how the precipitation deviates from the average at each location over time.
We can use the charting tool to look at the anomaly data through time. Figures 6 and 7 shows how the average annual precipitation compares to the long-term average (the average over all 117 years). Years with positive anomaly values had more precipitation than the long-term average, and years with negative anomaly values had less precipitation than the long-term average.
Figure 6: Precipitation anomaly in Sahara desert region
Figure 7: Precipitation anomaly in Amazon rainforest region
Using these graphs, we can see the overall trends in the Amazon rainforest region and the Sahara desert region. Over this time period, annual precipitation has steadily declined in the Sahara desert region to the point where it is now consistently below the long-term average. Annual precipitation trends in the Amazon rainforest region are again harder to identify, but it appears they have slowly but steadily increased. Additional data points in upcoming years will help to separate the trend from the noise in this region.
Precipitation Patterns and Trends
Another way to look at this precipitation data is to use a simple regression model to look at the trends and predict future precipitation. To begin, we will use theGenerate Trend Rastertool. This tool helps estimate the overall trend for each pixel along a dimension (time in this case). You can calculate the trends for one or more variables in a multidimensional raster. For this analysis, we will use the original monthly precipitation dataset and use a harmonic regression to account for seasonal fluctuations in precipitation. Here is the code block.
The result is a 3-band dataset, with the slope of the trend as band 1. Positive slope values (purple areas in figure 😎 indicate the average precipitation is in an increasing trend over time and negative values (green areas) indicate that precipitation is in a decreasing trend.
Figure 8: Precipitation trend, with the Amazon rainforest region outlined in blue and the Sahara desert region outlined in red
As we inspect this trend map, we can see that the Amazon rainforest region is mostly dark purple and is experiencing an overall increase in precipitation. The Sahara desert region is primarily a light green and is experiencing an overall decrease in precipitation.
Predicting Future Precipitation
We can also use this trend raster to do predictive modeling with thePredict Using Trend Rastertool to explore what the precipitation trend might look like in the future. In this case, we will use the trend raster as an input with harmonic regression to predict the precipitation from 2018 to 2027. We will use a harmonic regression to remove the underlying effects of seasonal variation. This will minimize the effect of seasonality in our predictions. The code block to predict precipitation is as shown below.
The output from the Predict Using Trend Raster tool is a new multidimensional map showing predicted annual precipitation (figure 9).
Figure 9: Predicted annual precipitation for the period of 2018 to 2027
This new multidimensional raster contains predicted annual precipitation values for 2018 to 2027, which can be viewed using the chart tool. Our predicted results show a continued increase in precipitation in the Amazon rainforest region (figure 10), and a continued decrease in precipitation in the Sahara desert region (figure 11). The predicted precipitation increase in the Amazon rainforest region is relatively small compared to the current annual precipitation (an increase of 0.2 percent in a region that is receiving almost 18 cm/year of precipitation). However, predictions show an expected 3.9 percent decrease in annual precipitation in the Sahara desert region by 2027. This 3.9 percent decrease in an area which already receives less than 1 cm of precipitation per year will result in continued expansion of the Sahara desert region.
Figure 10: Predicted annual precipitation in Amazon rainforest region, 2018 to 2027
Figure 11: Predicted annual precipitation in the Sahara desert region, 2018 to 2027
To test the accuracy of our precipitation predictions, we will subset the original dataset to just 1900 to 1950. We will use this subset of the data and the same steps we completed previously to build a model and predict annual precipitation for the period 1951 to 2017. Once our prediction is complete, we can compare the predictions to the observed data for 27 random locations around the world.
Figure 12: Random locations used for the accuracy assessment
Figure 13: Comparing the accuracy of predicted precipitation to observed precipitation
This accuracy assessment in figure 13 shows an overall agreement between our predicted precipitation and the observed precipitation for 1951 to 2017. This accuracy assessment indicates our original 2018 to 2027 predictions are within a reasonable level of error and accurately represent future precipitation values.
With a simple historical precipitation data set and Esri’s multidimensional tools, we were able to visualize and interpret the history of precipitation across the world. We were also able to predict future precipitation and investigate environmentally sensitive regions like the Amazon rainforest and the Sahara desert. These multidimensional tools allow you to work with rich datasets such as the precipitation records used here and easily produce meaningful results to help answer complex questions.
Magrin, G.O., J.A. Marengo, J.-P. Boulanger, M.S. Buckeridge, E. Castellanos, G. Poveda, F.R. Scarano, and S. Vicuña, 2014: Central and South America. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1499-1566
Natalie Thomas, Sumant Nigam. Twentieth-Century Climate Change over Africa: Seasonal Hydroclimate Trends and Sahara Desert Expansion. Journal of Climate, 2018; 31 (9): 3349 DOI: 10.1175/JCLI-D-17-0187.1
Wu, Yutian & Polvani, Lorenzo. (2017). Recent Trends in Extreme Precipitation and Temperature over Southeastern South America: The Dominant Role of Stratospheric Ozone Depletion in CESM Large Ensemble. Journal of Climate. 30. 10.1175/JCLI-D-17-0124.1.
#University of Delaware Air Temperature & Precipitation
Often similar netCDF data comes in separate files with each file having a single time stamp, or a height stamp, or any dimension stamp, and for many it becomes a daunting task to simply combine them into a single file.
Here's a tutorial that shows a quick and easy way to do so.
We are pleased to share the lineup of oral talks, panels, and posters that will be presented December 9-13 at the 2019 American Geophysical Union (AGU) Fall Meeting in San Francisco's newly renovated Moscone Center, as well as the Esri Booth Demo Schedule, the most comprehensive and exciting ever. Many know ofAGUas among the world’s most well-respected Earth science scholarly organizations, and itsannual fall meetingdwarfs our UC by over 10,000 attendees. AGU 2019 expects 28,000 attendees from 113 countries, making it the largest Earth and space science meeting in the world.
In addition, AGU's Centennial Celebration will culiminate at this year's Fall Meeting. As part of the celebration, AGU will be featuring Story Maps at their AGU Central booth, which will be at the base of the escalators in Moscone North, and then online afterward. The story maps will showcase AGU's public engagement projects for the Centennial and to map their Thriving Earth Exchange work.
You’ll see in the list below ofscientific papers, posters, and entire sessionsthat Esri is leading or contributing on a wide variety of interesting and important projects. Many of these are in collaboration with our federal partners at NASA, NOAA, US Forest Service, Department of Energy, EPA, and the USGS, as well as several universities and national laboratories. This showcases how Esri not only enables great understanding of the world with our products and services, but also performs good science, and contributes well as a member of the scientific community, sharing and inspiring others as to The Science of Where.
In addition, we will have a large theater-styleexhibit booth (coordinated by Research & Sciences Industry Manager Drew Stephens) with messaging and demos (as organized by Dan Pisut) on multidimensional scientific data and analysis, imagery, big data geoanalytics, The Living Atlas, ArcGIS Pro, Imagery, Ecological Land Units, Ecological Marine Units, GeoPlanner, Insights, story maps, the web GIS pattern, our commitment to open/interoperable, and more. See the Esri Booth Demo Schedule and exhibit floor plan (Esri is booth 739 near the central walkway to the right of NASA).
Overall,we are sending 17 Esri staffto participate at AGU.
AGU PRESENTATIONS and SESSIONS with Esri Co-Authors
(where ED = Education, IN = Earth and Space Science Informatics, OS = Ocean Sciences; 5-character session numbers are entire sessions)
(where ED = Education, EP = Earth and Planetary Surface Processes, GH = Geohealth, H = Hydrology, IN = Earth and Space Science Informatics, NH = Natural Hazards, NS = Near Surface Geophysics, OS = Ocean Sciences)
Welcome to our new Esri science portfolio site! Science underpins everything that Esri produces. We develop for and support a number of sciences. We're very excited about all the things that we're doing across all the environmental sciences, and into the social sciences and digital humanities. This is embedded in what we do—to strengthen the foundation of our software and services; to research, publish, communicate, and serve alongside the scientific community; and to inspire positive change in society.
Rather than a site focusing exclusively on what we SELL, this site focuses on what we BELIEVE as we conduct the broad and important MISSION of science (THE SCIENCE OF WHERE ®). In turn, it shares what we are specifically ACCOMPLISHING in collaboration with our users, in the areas of Open Science, Weather & Climate, Solid Earth Science (e.g., hydrology, ecology, geology, agriculture, etc.), Ocean Science, Geographic Information Science (including data science), and Social Science.