Ghost Hunting

Stories of heavily damaged bombers during WW2 returning safely to their home bases (like the one in this photograph) are well documented.  In these stories the plane lands with its wings riddled with bullet holes and shrapnel damage. Mechanics rushed to reinforce the metal where the damage was most visible, and aircraft engineers studied ways to reinforce these locations to avoid damage. But mathematician Abraham Wald and his colleagues at the Statistical Research Group at Columbia University famously stepped in to correct them.

According to Wald, the engineers had succumbed to a “Survivorship Bias” in their analysis.  He argued that the armor should go where the holes weren't—on the engines and the cockpit. The planes that were hit there never made it back to be studied. They were the "non-survivors," and they held the true secret to staying in the sky. Counterintuitively, reinforcing and strengthening the parts of the planes that were NOT damaged on these returning planes became the norm and survivability improved.

I read these accounts with fascination and wondered if perhaps I might be able to find examples of “Survivorship Bias” theory being applied to prevent disasters from natural hazards and events. Not surprisingly, there were many such examples and many revolved around the use of Artificial Intelligence (AI) to analyze otherwise overwhelming amounts of geographic data.  I’ve been hanging on to these links and collecting ideas for years so that I could write about it here.  But I could never find just the right way to present it. 

Like the applied AI studies I read about, I decided to ask my own questions of AI sources to collect and present some of these in outline form, along with a reader-friendly analysis. I found the returned narratives incredibly thorough and I learned a lot in the process.  So, in a departure from my usual approach to presenting my own thoughts on this page, what follows is an edited and simplified version of the information presented by the AI applications, along with an interesting technical discussion with citations and links to more information.

STUDYING GHOSTS

Disaster scientists are applying this "Survivorship Bias" theory to our cities. For decades, we have studied the buildings that survived earthquakes and the homes that stood firm against floods to create our "best practices." But this creates a dangerous blind spot. By only looking at what remains, we are ignoring the silent data of what was erased. To truly reduce the toll of natural hazards, we must learn to see the "ghosts" in our data.

The Problem of the Standing Building

When an earthquake strikes, engineers flock to the structures that are still standing to see what they did right. However, survivorship bias suggests these buildings might not be "superior,” they might just be lucky. Perhaps the seismic waves skipped that specific block, or the soil underneath was slightly denser.

If we only update building codes based on survivors, we risk reinforcing the wrong things. True resilience requires investigating the "invisible" data of total collapses. As noted by researchers in the Journal of Infrastructure Systems, focusing on surviving infrastructure can lead to an overestimation of systemic resilience, masking critical vulnerabilities that only appear at the point of total failure (Smith & Arwade, 2016).

Hunting for "Hidden" Failure Data

To find this missing data, scientists have moved beyond the clipboard and into the realm of digital forensics. They use two primary methods to capture the lessons of the "non-survivors":

1. Siamese Neural Networks and Change Detection: Using satellites, researchers compare high-resolution imagery of a town "before" and "after" a disaster. AI models—specifically Siamese networks—process both images simultaneously to identify "empty footprints." By mapping exactly where a house used to be, engineers can correlate total destruction with specific geographical features, like proximity to a fault line or a specific elevation in a flood zone.

2. Digital Twin Forensics: Before a site is cleared by bulldozers, teams use LiDAR (Light Detection and Ranging) to create a 3D "digital twin" of the rubble. This preserves the "perishable evidence" of the failure. By running these 3D models through Finite Element Analysis, they can virtually "re-collapse" the building to find the exact bolt or beam that gave way first. 

Example 1: The Lessons of the "Red Zones" in Christchurch

After the 2011 Canterbury earthquake in New Zealand, the government didn't just look at which houses stood; they looked at where the land itself "failed." They identified thousands of properties in "Red Zones" where liquefaction had turned the ground into jelly. While some houses in these zones appeared fine (the survivors), the data from the destroyed neighbors showed that the land was fundamentally unbuildable. By focusing on the "non-survivor" land, they prevented future disasters by turning these areas into permanent green spaces rather than trying to "fix" the buildings.

Example 2: The "Missing" Residents of Hurricane Katrina

Social survivorship bias is just as deadly as structural bias. For years, evacuation plans were based on interviews with people who stayed behind and survived. However, this ignored the data from those who didn't survive or those who were displaced and never returned. Recent sociological studies have shifted to tracking "out-migration"—using postal and cell data to find the people who disappeared from the map. This revealed that poverty and lack of transportation were the primary "failure points" for evacuation, leading to a total overhaul of how cities like New Orleans manage bus-based evacuations for the most vulnerable.

The Path Forward

To stop the next disaster, we have to stop looking at the winners. We must treat every pile of rubble as a textbook and every empty lot as a warning. By shifting our gaze from the buildings that are still here to the ones that are gone, we can finally place the "armor" where it matters most.

---

Citations

• Smith, B. G., & Arwade, S. R. (2016). "Infrastructure Reliability and Survivorship Bias." Journal of Infrastructure Systems.
• Murnane, R. J., & Elsner, J. B. (2012). "Maximum Intensity and the Survivorship Bias of Records." Nature Climate Change.
• Tanyas, H., et al. (2019). "A Survey of Landslide Inventory Inventories: The Missing Data of Failure." Geomorphology.

---

 

APPENDIX 1:

How does AI specifically identify "ghost structures" from satellite imagery to improve these failure maps?

To find "ghost structures"—the buildings and features that have been entirely erased by a disaster—AI doesn't just look for wreckage; it looks for absence. Because a building that is completely swept away or buried leaves no visual cues of "damage" (like cracked roofs or debris), traditional AI often misses it.

Here is how modern AI systems, such as those used by researchers at organizations like Microsoft AI for Good and Google, identify these invisible failures:

1. Siamese Neural Networks: The Art of "Before and After"

The most powerful tool for finding missing data is the Siamese Neural Network. Think of it as two identical AI "brains" working in tandem.

• The Workflow: One side of the network processes a "pre-disaster" image (the baseline), while the other processes a "post-disaster" image of the exact same coordinates [1, 2] .
• The Logic: Instead of just looking for rubble in the new image, the network compares the "feature maps" of both. If the pre-disaster brain sees a building footprint but the post-disaster brain sees only bare earth or water, the AI flags it as a ghost structure [3, 4] . This "change detection" allows for a 100% loss classification that manual inspectors might overlook as "empty land" [5, 6] .

2. Semantic Change Detection (SCD)

AI uses Semantic Segmentation to label every pixel in an image—for example, "this pixel is a roof," "this pixel is a road."

• By comparing these labels over time, the AI can detect when a pixel’s identity has fundamentally changed from "man-made structure" to "environmental anomaly" [7, 8] .
• Newer frameworks like Shield can even do this in near real-time directly on satellites, allowing them to beam down only the "changed" data to save precious minutes during a rescue operation [9, 10] .

3. Filling the Gaps with Sensor Fusion

Sometimes, a ghost structure is hidden by new obstacles, like deep floodwater or thick smoke. To "see" through these, AI combines different types of data:

• SAR (Synthetic Aperture Radar): Unlike standard photos, SAR uses radar waves that bounce off hard surfaces even through clouds. AI can detect the "radar signature" of a missing building even in the middle of a storm [11, 12] .
• LiDAR Integration: By stacking 3D height data (LiDAR) on top of 2D satellite photos, AI can identify where the volume of a landscape has changed [13] . If a 20-foot tall house is now a 0-foot tall flat surface, the AI marks a total structural failure.

Video:  Explainable Deep Learning on Satellite Imagery for Disaster ...,

 

---

Citations (links)

[1] https://arxiv.org

[2] https://www.mdpi.com

[3] https://engineering.fb.com

[4] https://www.spcsft.com

[5] https://isprs-archives.copernicus.org

[6] https://developer.nvidia.com

[7] https://www.researchgate.net

[8] https://github.com

[9] https://www.clarionledger.com

[10] https://www.freep.com

[11] https://www.youtube.com

[12] https://www.researchgate.net

[13] https://www.youtube.com

 

APPENDIX 2:

How AI-generated "failure maps" are used by insurance companies or urban planners to decide which areas should never be rebuilt.

AI-generated "failure maps" have become the new playbook for rebuilding smarter. By highlighting where assets were entirely erased—the "ghost structures"—insurance companies and urban planners can move from reactive repairs to proactive resilience.

How Insurance Companies Use Failure Data

Insurance companies like Travelers and Munich Re use these maps to stabilize their markets and accelerate recovery:

• Rapid Claims without Site Access: In the 2018 Camp Fire, Travelers used aerial imagery and geospatial AI to identify homes that were completely destroyed before residents could even return. This "view of the unseen" allowed them to initiate claims immediately [1] .
• Granular Risk Pricing: Companies like ZestyAI use AI to analyze historical failure data (e.g., 3D roof analysis and storm history) to assign "hail scores" [2] . This helps insurers identify low-risk homes even in high-risk areas, allowing them to offer competitive premiums where others might withdraw [2] .
• Fraud Detection: By comparing "before and after" satellite images, fraud examiners can verify if a property was actually destroyed by a weather event, preventing false claims from driving up premiums for everyone [1] .

How Urban Planners Use Failure Data

City planners use AI failure maps to determine if a location is too dangerous to inhabit, often leading to "managed retreat"—moving entire communities out of harm's way:

• Zoning and Building Moratoriums: Planners use AI to combine past disaster data with topography and geology to create predictive risk maps [3] . If AI shows that a specific "ghost structure" footprint was hit repeatedly by floods or is on soil prone to liquefaction, planners can restrict rebuilding in that zone [3, 4] .
• Resilient Reconstruction: Tools like Urban Footprint use AI to select safe, equitable locations for new settlements by analyzing climate and demographic data [5] . This ensures that new construction avoids the "failure traps" identified by the AI.
• Managed Retreat Databases: Planners are increasingly using global databases of managed retreat cases to understand the long-term success of moving people away from high-risk floodplains [6] .

Video: Kansas City using AI to better prepare for natural disasters

 

---

Citations (links)

[1] https://www.esri.com

[2] https://www.insurancejournal.com

[3] https://www.orbit-o-r.com

[4] https://www.preventionweb.net

[5] https://urbandesignlab.in

[6] https://agupubs.onlinelibrary.wiley.com

 

APPENDIX III:

How different countries handle these "no-build" decisions based on AI data.

Different countries approach "no-build" decisions with varying levels of central authority and technological integration. While some use AI to strictly enforce land-use bans, others use it as a tool for "living with" the hazard.

1. Japan: Institutionalized "Forever" Bans

Japan has the world's most aggressive stance on managed retreat after a disaster [1] .

• The Policy: Following the 2011 tsunami, the central government designated certain hazardous coastal areas as off-limits to residential use in perpetuity [2] .
• AI Integration: Japan uses high-resolution seismic and tsunami simulations to identify "Red Zones" [1] . Instead of just building higher walls, they use AI to design "layered defense systems" [3] . In some cases, they build 1.2-kilometer-long "firewall" apartment complexes designed to protect older wooden neighborhoods behind them, essentially using infrastructure as a physical shield [1] .
• Outcome: These zones are often converted into pocket parks with emergency water stores or coastal forests [1, 4, 5] .

2. The Netherlands: "Living with Water" through Digital Twins

The Dutch approach is less about banning construction and more about using AI to make high-risk living possible [6] .

• The Policy: Known for the philosophy of "living with water," the Netherlands uses AI to manage water levels in real-time rather than just retreating [6] .
• AI Integration: They utilize a system called "Digital Delta," which creates virtual replicas (Digital Twins) of entire cities and waterways [6, 7] .
• Automated Action: When AI sensors in rivers and dikes detect thresholds are met, the system automatically closes storm-surge barriers and activates pumps [7, 8] . Their AI prediction accuracy for dike failure reaches roughly 94%, giving them the confidence to maintain low-lying communities [9] .

3. United States: Fragmented Local Control

In the U.S., "no-build" decisions are rarely made at a federal level, leading to a patchwork of risk [10] .

• The Policy: Land-use decisions are primarily local, driven by zoning and permitting laws [10] . This often leads to "survivorship bias" in development, where 844,000 new homes were built in FEMA-designated 100-year flood plains between 2001 and 2019 [10] .
• AI Integration: Federal agencies like FEMA and the DIU use AI for damage assessment and risk modeling rather than direct zoning enforcement [11, 12] .
• Emerging Shift: Planners are starting to use AI-driven "Billion-Dollar Disaster Maps" to visualize the rising cost of rebuilding in the same spots, pushing for tougher local zoning [13] .

4. Global South (e.g., Mozambique and Nigeria)

In developing nations, AI is used to identify "invisible" populations that official maps miss [14] .

• The Policy: International initiatives like the UN's "Early Warnings for All" use AI to protect vulnerable areas by 2027 [15] .
• AI Integration: AI agents ingest satellite and social media data to create predictive models in flood-prone areas like Mozambique [16] . These systems identify where aid logistics will fail, helping planners decide where temporary or permanent relocation is the only safe option [16] .

Summary Table

Country/Region	Primary Policy Goal	Key AI Technology
Japan	Permanent hazard avoidance	Tsunami/Seismic simulation and 3D modeling [1]
Netherlands	Automated resilience	Digital Twins and automated barrier control [6, 7]
USA	Information-based local zoning	FEMA risk modeling and satellite change detection [11, 12]
Global South	Humanitarian risk triage	Satellite-driven population and connectivity mapping [14]

Video:  The Netherlands is deploying AI-powered flood defense ...

 

---

Citations (links)

[1] https://www.businessinsider.com

[2] https://managedretreat.ucdavis.edu

[3] https://www.facebook.com

[4] https://www.facebook.com

[5] https://www.facebook.com

[6] https://www.facebook.com

[7] https://www.facebook.com

[8] https://www.instagram.com

[9] https://www.facebook.com

[10] https://www.youtube.com

[11] https://fedscoop.com

[12] https://www.sciencedirect.com

[13] https://www.ncei.noaa.gov

[14] https://sdg-action.org

[15] https://www.itu.int

[16] https://www.youtube.com

 

 

A Visit to Welch

Check out the new post at MarkBeck.net that's related to the two below (flooding in Welch WV). I made a trip to see the flood damage in the region first-hand. But it wasn't just a business trip. Here's a hint.

Welch, WV

Photo by TripAdvisor

In a post last week, I highlighted the plight of the town of Welch, WV and its precarious location beside the confluence of the Tug and Elkhorn Rivers, both with a history of flooding. Sandwiched on tiny slivers of land between the rivers and tall wooded mountains that snake through southwestern West Virginia, there isn’t much room for water and people to coexist when the rains fall.

Welch was, for the first half of the 20^th Century, a prosperous coal town. Mining meant jobs, and local coal fed the appetites of nearby steel mills and beyond. Welch claimed itself “The Heart of the Nation’s Coal Bin.”  After the end of WWII, much changed.  By the 1960s, automation reduced the demand for mining labor, smaller steel mills closed, and the population of Welch (and McDowell County) dwindled.  One history adds this observation:

When presidential candidate John F. Kennedy visited Welch by automobile caravan in 1960, he saw a city whose businesses were struggling due to a growing poverty rate throughout the county. What Kennedy learned here during his campaign for the 1960 West Virginia primary was believed to be the basis of the aid brought to the Appalachian region by the Kennedy and Lyndon Johnson administrations. During a speech in Canton, Ohio on September 27, 1960, he stated "McDowell County mines more coal than it ever has in its history, probably more coal than any county in the United States and yet there are more people getting surplus food packages in McDowell County than any county in the United States. The reason is that machines are doing the jobs of men, and we have not been able to find jobs for those men."

The first recipients of modern era food stamps were the Chloe and Alderson Muncy family of Paynesville, McDowell County. Their household included fifteen persons. On May 29, 1961, in the City of Welch, as a crowd of reporters witnessed the proceedings, Secretary of Agriculture Orville Freeman delivered $95 of federal food stamps to Mr. and Mrs. Muncy. This was the first issuance of federal food stamps under the Kennedy Administration, and it was the beginning of a rapidly expanding program of federal assistance that would be legislated in the "War on Poverty".

In the 1960s and 1970s, McDowell County coal continued to be a major source of fuel for the steel and electric power generation industries. As United States steel production declined, however, McDowell County suffered further losses. In 1986, the closure of the US Steel mines in nearby Gary led to an immediate loss of more than 1,200 jobs. In the following year alone, personal income in McDowell County decreased dramatically by two-thirds. Real estate values also plummeted. Miners were forced to abandon their homes in search for new beginnings in other regions of the country.

The Welch of the 21^st Century is but a remnant of its former self, yet those rugged, hard-working citizens who remain in the town hold tenaciously to their homes and community with pride. They watch out for each other too—just like the statement in my prior post by McDowell County Commissioner Michael Brooks who said the people of his county will step into harm’s way for their neighbors.

So it must be difficult for some to accept that, to avoid the incessant danger and threat of rising flood waters, they’ll have to leave.  Agencies like the Natural Resources Conservation Service offer buyout opportunities with incentives to encourage those in the most flood-prone areas to seek a new life elsewhere. Land purchased through this program is restored to flood plain conditions and development is banned.

But not everyone can simply start over elsewhere. Something must be done to help those who remain. There are solutions. And the solutions offer hope, but not without controversy.  Just four days ago a local news outlet shared the following:

McDowell County Commissioner Michael Brooks is frustrated. He believes one of the biggest issues is a lack of proper stream maintenance in the county.

“There needs to be a massive effort throughout McDowell County and southern West Virginia to dredge and clean our streams. They’re completely full and many of our streams are nearly at road level, those that weren’t after this are now. That is a major, major issue,” said Brooks in an appearance on MetroNews “Talkline” which was live in Welch Thursday.

Brooks isn’t alone. Many have talked about the need to scour out the bottom of waterways like Elkhorn Creek and the upper stretches of the Tug Fork River along with tributaries which overflowed and caused major damage and destruction. Brooks said it doesn’t even take a major flood to push water into a roadway in many cases.

But there are always debates and roadblocks to dredging.

Money is always a factor when considering the cost of the work. Some often argue the cost of rebuilding is far less than the cost of mitigation.

But the bigger obstacle to dredging is environmental protection. The U.S. Fish and Wildlife Service and other federal agencies typically throw up roadblocks to dredging to protect endangered species and the destruction of their habitat. The area’s watershed is home to several rare types of crayfish and the Candy Darter, all of which are considered species on the brink.

Brooks said his concern is McDowell County’s human population is on the brink.

“We can restock crawdads and we can restock fish, but when people leave these communities they never come back. That is unfortunately what we have faced here in McDowell County. When the population goes from 30 to 40 thousand people down to 18 thousand or so, people are tired,” he explained.

Brooks added after every flood the county has endured the population took a hit. People continue to leave his county in droves, many of them run off by an inability to protect their homes from rising water and an unwillingness to risk having to endure it again.

“In my opinion, respectfully, there’s a lot of people making these decisions that affect the lives of people like our folks here in southern West Virginia who live in a subdivision somewhere and never have those issues. We need some kind of a panel of folks who have lived this continually throughout their lives to demonstrate the significance,” said Brooks.

There are always trade-offs when it comes to nature and development, and it may be possible to create pathways for floodwaters and protect vulnerable structures. In fact there must be a way. But that’s a topic for another post.

In the meantime, please enjoy this video introduction to the people of Welch and McDowell County. 

 

A Bridge to... SOMEWHERE!

 

McDowell County, West Virginia has consistently been among the poorest counties in the state and the country. In 2024, the county’s median household income was $27,682, which is more than 40% below the state median, according to the US Census Bureau.  And yet the county suffers from a long history of devastating floods—three in the last 25 years that caused death and destruction.  The winter storm that wreaked havoc over the central and eastern part of the country earlier this month was the third. The local news reported that:

At least one fatality has been confirmed, and there are still people unaccounted for. The Tug Fork River reached historic levels, and rainfall compounded on the flooding with over a month’s worth of rain in just two days. Lives, homes, businesses, and critical infrastructure have been severely impacted.

Over the weekend, relentless rainfall turned roads into rivers, trapping residents and destroying property. In Welch, floodwaters surged through the town much of Saturday and into the evening. Vehicles were overturned, roads and sidewalks were caked in thick mud, and countless businesses and homes fell victim to the floodwaters.

“[It was] carnage to be honest with you. Some people lost everything. Some people lost their lives. It’s horrible, and I don’t know what to say. It actually tugs my heart talking about it.” Sheriff James Muncy Jr. said on Sunday.

Waterlogged streets, collapsed embankments, and desperate rescue efforts paint a forbidding picture of the situation and show the scale of the devastation…

The Tug Fork River crested at 22.1 feet on Saturday around 10 PM, tying 2002 for the highest levels recorded in history. The rush of water left the streets of Welch caked in mud, flooded houses and businesses, and made countless roads impassable. The infrastructure damage has only compounded the issue of safe roadways.

The following statement, however, says it all about the people of McDowell County.

Despite the damage, the resilience of McDowell County residents is undeniable.

“You know, we have some of the best people in the country here in McDowell County. And we’ve got folks that have basically—they’ve really placed themselves in harm’s way to try to look out for their neighbors and try to do all that we can,” Commissioner Brooks said.

The County suffers when the rains hit and the rivers swell, and there may not seem like much can be done; but there are infrastructure improvements that, if funded, would make a world of difference.  Organizations like the NRCS (Natural Resources Conservation Service) have purchased properties in flood-prone areas, such as the Elkhorn Creek/Tug Fork River Watershed. Residents in those areas are being offered an opportunity to relocate. And of course, walls, raising foundations, and other diversion techniques would help.

The town of Welch has a particular problem with an underpass beneath the railroad on the main road leading to town. It’s too short for some traffic and a lower section that provides access to higher vehicles floods during storms. Even worse, the hospital and the town are on opposite sides of this dangerous underpass.  In a local TV newscast, Welch Mayor McBride said he believes the citizens of Welch deserve a new bridge that is not such a dangerous hazard. 

“It’s a way of life, a quality of life that just has to be done. It’s time. It should have been done a long time ago, but I don’t want somebody twenty years from now saying it should’ve been done. We campaign hard for it. The governor has been very accessible to it… We’re not gonna give up no matter. As long as I’m here, they’re gonna hear from me screaming about a bridge.”

One simple remedy would make a huge difference to this town and others who must pass through it.

To help the people of McDowell County please consider donating to the charities listed here.  And here.

And for a heartwarming story of one hero of McDowell County, please watch this brief video. And consider helping Sharon's cause here.

 

Wildfire Infographic

Just a quick note to post this link to a helpful infographic created by FEMA in response to the tragic wildfire that claimed lives and property on Maui last year. The illustrated guide helps property owners arrange their buildings and yards to better protect from the spread of wildfires. The full-sized document can be found here.

Unnatural Disasters Are The Most Dangerous

When it comes to establishing policies and practices that promote resilient communities, there is much attention given to climate-related events.  In fact, the United Nations' Sendai Framework for Disaster Risk Reduction 2015-2030, doesn't even mention earthquakes (or tsunami or volcanic eruption) within its pages.  As this blog so frequently points out, however, geologic hazards (and related events) take as many lives as climate disasters do. Adapting to both must be our focus, regardless of what actions we may take on the climate side to adopt mitigating practices like reducing energy and carbon emissions.

Curious, I began investigating historical statistics regarding the disasters that have killed the most human beings, thinking that we should always prioritize measures to thwart those events at the top of our adaptation policy list.  It didn't take long to discover something quite horrific.  The most loss of life for any known historic (non-war, non-pandemic-related) disaster occurred in the very recent (relatively speaking) past--partially within my own lifetime, in fact.

First, I ran across this fascinating graphic.  It's a very large image (in its original form, here), but a piece of it is depicted below:

See the largest "bubble?"  It turns out that the largest loss of life in a known disaster occurred during the Great Chinese Famine of 1957-62. Many tend to remove "famine" and "pandemic" from the list of disaster mortality statistics, as they would war deaths. But I appreciated this article addressing it as they did--particularly given that the famine has, at least in part, "natural" origins.  Like so many natural events that turn into "disasters" due to human activity, however, this one swelled out of control and destroyed (estimated) 23 million to as many as 55 million lives.  And the reasons for the exacerbation, though complicated and highly convoluted, stem primarily from the policies and actions of the country's Government at the time. Accounts of what happened and why, range from the scientific to the political to the emotional.  The accounts (particularly the one linked as "emotional") are sobering. 

A recurring theme among these historical accounts is a lack of transparency and a seemingly deliberate ignorance of reality that prevents leaders from seeing, let alone understanding, the problems and potential solutions.  I won't dwell on the details, but I will share the following from the article from AsianStudies.org linked above:

"According to one study, China experienced some 1,828 major famines in its long history, but what distinguishes the Great Leap Forward from its predecessors are its cause, massive scope, and ongoing concealment. In his recent study of famine, Cormac Ó Gráda suggests that, historically, famines emerged from natural phenomena, sometimes exacerbated by human activity. Modern famines, on the other hand, stem from human factors such as war or ideology exacerbated by natural conditions."

Such was the case in the 1950s-60s China. Like I wrote here many times during the recent COVID-19 Pandemic, building disaster-resistant communities is so much more than just moving (or raising) infrastructure.  It's as much about people.  How we interact, how we treat each other, and how we plan ahead for potential problems is critical to helping preserve our lives and livelihoods.  We need to demand a responsive government with an open mind to keep the best interests of their citizens in mind.

-----------

To assist the hungry today, please consider donating to a charity like this one. 

Telling It Like It Is

Please allow me a brief personal diversion into a topic that I feel quite strongly about.  I don't mean to disparage any particular academic or researcher, so I won't divulge specifically the source of these sentences, but please read the following that crossed my desk in a scientific article recently:

Unsafe conditions (exposure to hazards) are shaped through a series of disaster risk drivers generated from processes, priorities, resource allocation and production–consumption patterns that result from different socio-economic development models. In essence, disaster risk drivers emanate from the ways the basic goals and parameters for growth and societal definitions of development are established and implemented.

I believe what the authors were saying could have been more simply stated, perhaps like this:

Because social and economic conditions vary by community, we expect to see each have their own unique priorities, different patterns of production and consumption, goals for resource allocation, and patterns of physical growth and development. These characteristics, in turn, contribute to unique exposures to hazards or risks that must be addressed.

Without having completed the original research, I'm only interpreting the writers' intent.  And as you'll see in reading entries in this blog I'm as guilty as anyone for resorting to imprecise, jargon-clouded language.  But my point is that we should all try to find ways to say what's needed in a manner that is more easily understood by all.

"The Plain Writing Act of 2010 was signed on October 13, 2010. The law requires that federal agencies use clear government communication that the public can understand and use."  The Federal government's web site addressing this law includes a number of guides, helpful hints, and even some humorous examples to illustrate the need for the use of plain language in all government documents and records.

When it comes to the topic of hazard mitigation and resiliency in communities, where citizens and government officials need to be "on the same page" with the scientists and engineers, it goes well beyond simple "best practice."  When addressing natural hazards and preparing for potential impact on human activities, clear communication is absolutely critical and can save lives.

It's not just external (community) communication either.  There are real benefits for scientists and researchers themselves to adopt a more "plain language" approach for all technical writing--even among peers.  Lily Whiteman, a contributor to the Washington Post and a senior writer for the National Science Foundation, shares the following:

Plain language is one of our best tools for improving scientific literacy and encouraging wise decision-making by the public on science-based issues. It is important for scientists to use plain language not only to reach the public; but also to reach one another. Indeed, scientific information conveyed in plain language invariable reaches bigger scientific audiences than information conveyed in technical language. Evidence of this includes the following:

A recent study showed that medical articles reported in The New England Journal of Medicine and then reported in The New York Times receive about 73 percent more citations in medical reports than do articles not reported in The New York Times.

The Cleveland Clinic Journal of Medicine is a nationally successful journal with the best readership growth trend and advertising growth trend in its market. But The Cleveland Clinic Journal wasn’t always so successful. Until the mid-1990s, it was a forgettable, low circulation journal. How did the editors of The Cleveland Clinic Journal of Medicine dramatically increase their readership? By replacing their journal’s dense, long-winded, jargon-filled style with an alternative style that incorporates the principles of plain language.

The quote, “Simplicity is the ultimate sophistication,” is attributed to Leonardo da Vinci.

I couldn't have said it better. Or more clearly.