Category: Analysis

Source: The Conversation – USA (2) – By Stephanie Insalaco-Wyner, Assistant Professor of Geographic Information Sciences, Southwestern University

Florida’s Indian River Lagoon has been an ecosystem in decline going back to 2011, when harmful algal blooms led to a severe decline in seagrass, the foundational component of shallow coastal ecosystems.

Seagrass meadows stabilize sediments, improve water clarity and provide critical habitat and forage for species ranging from invertebrates to sea turtles and manatees. Seagrass also generates a significant amount of economic activity in the state of Florida.

The loss of seagrass in the Indian River Lagoon System undermined fisheries, tourism and wildlife, ultimately leading to the starvation of more than 1,200 manatees from 2020-25, peaking in 2021-22.

Mosquito Lagoon is part of the Indian River Lagoon system that spans 28 miles (45 kilometers), running from Cape Canaveral in the south up to Ponce Inlet in the north. As in the rest of the lagoon system, years of nutrient pollution and recurring algal blooms had diminished seagrass cover to nearly zero by the early 2020s. By most accounts, Mosquito Lagoon had crossed a critical ecological tipping point.

In the fall of 2022, hurricanes Ian and Nicole struck Florida’s east coast within six weeks of one another, bringing intense rainfall, storm surges and coastal erosion. In the immediate aftermath, seagrass declined even further.

But a few months later, in the spring of 2023, seagrass began to return. Satellite imagery revealed rapid and widespread regrowth.

We are geographers who study environmental change. Our research documents this unexpected recovery and examines what it may reveal about ecosystem resilience in heavily degraded coastal systems.

One of us, Hannah Herrero, is a Volusia County native who grew up around the lagoon. She returned to her hometown at the outset of the COVID-19 pandemic. It was there that some local guides and fishermen she’d known for years suggested that our team should use satellite imagery to look at the state of collapse in the lagoon.

The study we designed as a result used satellite imagery and machine learning, a type of artificial intelligence that uses advanced algorithms to learn and predict patterns, to track seagrass dynamics in Mosquito Lagoon before, during and after the storms. This approach allowed us to observe change at a scale and frequency that is difficult to achieve using only traditional field survey methods.

Tracking seagrass from space

Monitoring seagrass coverage “the old-fashioned way” involves going into the lagoon and laying out transects, straight lines that cut through a landscape, so standard observations could be recorded. We would then have to boat or wade all along those lines to measure seagrass extent and locations and create digital maps manually to show where it is present.

As you can imagine, this is a time-intensive process that’s limited by how far you can boat or swim in a day, and by financial resources.

So we decided to use satellite imagery instead. This method is not without its own challenges – water turbidity, or cloudiness, seasonal variability and the patchy nature of vegetation that grows on the bottom of the lagoon all make it difficult to observe seagrass growth directly on the imagery.

To address this challenge, our study used imagery from NASA’s Harmonized Landsat–Sentinel program, which combines data from multiple satellites into a consistent record of photos of the same areas taken frequently over time. We analyzed imagery collected between September 2022 and January 2024, focusing on periods before and immediately after the hurricanes and throughout the subsequent recovery.

We applied a type of machine learning model called Random Forest to classify each image into seagrass and nonseagrass categories.

The machine learning algorithm is informed by training samples collected in the field, but once the model has learned the signature of seagrass, it is able to then apply the classification model to the rest of the lagoon and across time with limited human input. We can then validate this classification.

Heading into the field

First, we had to train the model using hundreds of GPS points collected in the field over multiple seasons. This step helps to ensure that satellite classifications align with on‑the‑ground conditions and are accurately interpreting the images.

Over several weeks during the summers of 2020 through 2023, our team spent many hours navigating Mosquito Lagoon in a small skiff designed for shallow depths, recording seagrass presence.

It wasn’t always easy – Florida summers are intensely hot and humid, and Mosquito Lagoon definitely lived up to its name. But we got to see a wide variety of wildlife, including manatees, dolphins, sea turtles and alligators. And occasionally, on lucky days, we even spotted a roseate spoonbill or reddish egret.

Our experience in the field highlighted why this system matters: Mosquito Lagoon is a remarkably vibrant place, teeming with wildlife. These long days on the lagoon, surrounded by its biodiversity and immersed in its unique sense of place, are what anchor the remote sensing data to on-the-ground ecological conditions and make the resulting models credible.

What we found

Our analysis reveals three distinct phases of seagrass coverage.

First, seagrass declined sharply following hurricanes Ian and Nicole. By December 2022 and early 2023, satellite imagery showed virtually no detectable seagrass across the lagoon.

Then, in March 2023, we identified a statistically significant shift. Seagrass began to reappear, initially in small, scattered patches.

Finally, during late spring and summer 2023, seagrass expanded rapidly. By July 2023, it covered more than 20% of the lagoon – levels not observed in more than a decade. Coverage then declined again during the winter of 2023–24, as expected based on seasonal growth cycles. But even our last observation, completed in January 2024, showed seagrass covering 4.3% of the lagoon, substantially higher than pre-recovery levels during the winter season.

In spring 2026, seagrass in Mosquito Lagoon has remained at stable levels. Although it still experiences fluctuations due to algal blooms, seasonality and other changes in the ecosystem, we have not seen a complete loss of seagrass again like what was occurring for over a decade.

Importantly, this pattern was not random. Regrowth occurred primarily in the central and southern parts of the lagoon, areas historically known to support dense seagrass meadows. The timing also aligned with established seagrass seasonal growth patterns, which strengthens our confidence that the observed changes reflect true ecological recovery.

How storms may have contributed

We cannot prove that hurricanes directly caused the seagrass recovery that we document in our study. Further study beyond the scope of our work is needed to evaluate this possibility. However, we believe the sequence of events suggests that the storms may have altered environmental conditions in ways that enabled regrowth.

Hurricane Ian delivered large volumes of fresh water into the lagoon, potentially suppressing salt‑tolerant macroalgae that compete with seagrass for sunlight and nutrients.

Six weeks later, Hurricane Nicole breached coastal dunes and created several new inlets between the lagoon and the Atlantic Ocean. These openings allowed salt water into the lagoon, likely altering salinity and changing water circulation and conditions.

The hurricanes may also have redistributed seagrass fragments and mobilized dormant seed banks, accelerating regrowth once conditions stabilized. Ecologists have observed similar mechanisms in other coastal systems affected by tropical cyclones.

Beyond Mosquito Lagoon

Mosquito Lagoon’s collapse and eventual tentative recovery illustrates both the vulnerability and resilience of coastal ecosystems. Even after years of decline, the Mosquito Lagoon coastal ecosystem demonstrated an ability to recover relatively rapidly when physical conditions shifted.

At the same time, resilience does not guarantee permanence, and we believe this recovery should be viewed cautiously.

From a practical standpoint, our study also highlights the value of satellite imagery and machine learning for ecosystem monitoring. These tools allow scientists, resource managers and local communities to detect change consistently and respond before losses spread.

Hannah V. Herrero is the Director of Science for the Lagoon Watermen Alliance, a Florida-based non-profit. The mission of Lagoon Watermen Alliance is to protect the entire Indian River Lagoon system by advocating for science-based solutions that will lead to improved water quality, protection of imperiled habitats and safeguarding of gamefish populations.

Stephanie Insalaco-Wyner does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Hurricanes devastated Florida’s East Coast – then seagrass made an unexpected comeback – https://theconversation.com/hurricanes-devastated-floridas-east-coast-then-seagrass-made-an-unexpected-comeback-279177

Source: The Conversation – USA – By Angelica P. Ahrens, Assistant Research Scientist in Data Science and Microbiology, University of Florida

Your early life may quietly set the stage for developing Type 1 diabetes, an increasingly common, lifelong condition that can significantly affect daily life.

Our team’s research, published in the journal Nature Communications, shows that biological pathways associated with future Type 1 diabetes may begin as early as pregnancy, and that these signs could be detected in umbilical cord blood.

As a group, we study how living systems respond to stress. Understanding the early biology of Type 1 diabetes can help uncover windows of opportunity to treat the disease sooner.

Early stressors and Type 1 diabetes

Type 1 diabetes affects the pancreas. Specifically, its insulin-producing beta cells that help control blood sugar are progressively destroyed.

While this condition has typically been attributed to a dysfunctional immune system, a growing body of research suggests that beta cells themselves play an active role in disease development. Beta cells become stressed when overworked or exposed to harmful conditions. In some cases, they may even self-destruct before the immune system shows signs of affecting the pancreas. Potential stressors include infection, increased energy demands and smaller pancreas size.

Type 1 diabetes does not fit neatly within the traditional definition of an autoimmune disease. It ultimately develops when the body can no longer make enough insulin. During periods of increased demand for insulin, such as after consuming a large amount of carbohdyrates or during infection, beta cells are forced to work harder. When stressed beta cells stop working properly or die, they release molecular signals that can activate an immune response. This raises the possibility that immune responses may, in some cases, follow rather than initiate beta cell injury.

These observations suggest that stressed beta cells are not merely a consequence of Type 1 diabetes but also a contributor to its onset.

Studying diabetes in a general population

Our team wanted to see whether we could detect early signs of beta cell vulnerability before Type 1 diabetes symptoms start – or even before the immune system begins attacking the pancreas.

While genetics does play a role in Type 1 diabetes, an increasing number of people without a family history of diabetes are developing the disease. Much of the existing research has focused on children with high genetic risk. This is in part because, although Type 1 diabetes is increasing, it’s relatively rare – affecting less than 1% of people globally – making it hard to study before the disease starts.

In contrast, we sought to study children from a general population, not just those known to be at high risk for Type 1 diabetes. So we used data from the All Babies in Southeast Sweden cohort, a longitudinal study founded by one of us, Johnny Ludvigsson, which has been following mothers and their children since the late 1990s.

As part of the study, researchers collected and stored umbilical cord blood samples. Decades later, we selected samples from babies who later developed Type 1 diabetes for this study and screened them for proteins known to be involved in inflammation. We then used machine learning tools to identify factors linked to disease risk.

We found that the levels of several proteins in umbilical cord blood predicted the likelihood of whether a child in this cohort developed Type 1 diabetes in the future. These protein biomarkers fell into a few categories, including ones that help molecules get to where they need to be; ones that do not belong in the body, such as pollution; ones involved in the maintenance of cell structure; and ones that help regulate immune responses.

Our machine learning tool also identified some proteins that were associated with the absence of future Type 1 diabetes. These proteins, like tissue inhibitor of metalloproteinases-3 (TIMP3) and adenosine deaminase (ADA), are known to regulate inflammation by suppressing overactive immune responses, supporting healthy cellular communication and improving insulin production. Researchers have previously found that TIMP3 plays a role in glucose stabilization.

We found that levels of two specific proteins best predicted whether a baby would eventually develop Type 1 diabetes: IDS, which helps break down the long sugar molecules giving tissues strength and flexibility, and HLA-DRA, which is involved in activating the immune system. Type 1 diabetes is known to affect the long sugar molecules that IDS breaks down in several organs.

Importantly, the ability of these proteins to predict disease risk wasn’t heavily reliant on genetics. Although some differences were more pronounced in children with certain variants of HLA linked to increased risk of Type 1 diabetes, including this information in our machine learning algorithm only marginally improved accuracy. Instead, the proteins themselves were driving disease risk.

Type 1 diabetes isn’t inevitable

To be clear, the biomarkers we identified reflect possibility, not destiny. Like blood pressure and growth milestones, these measures could tell clinicians about someone’s risk of disease and ways to treat it.

Currently, screening for Type 1 diabetes typically relies on genetic testing and testing for the presence of autoantibodies, which are proteins that indicate the body is attacking insulin-producing cells. However, by the time autoantibodies appear, it may be too late to address the biological changes that set the stage for Type 1 diabetes.

Some of the markers we observed could be linked to widespread environmental exposures, including PFAS and other forever chemicals, that affect disease risk. Understanding how these toxic substances that pregnant people routinely and inadvertently encounter affect early biology could inform environmental and public health policies.

Our findings suggest that umbilical cord blood could help clinicians and parents more proactively address a child’s risk for Type 1 diabetes. Cord blood is often tossed out during the birthing process. But this “waste” can hold valuable information about early life and future health outcomes.

Beyond its potential value for early screening, cord blood is already used to source lifesaving stem cell treatments. Our work adds to growing evidence that cord blood is an important resource for supporting child health.

What’s next?

We are a long way from applying our findings to the clinic. Our study identified biomarkers associated with the later development of Type 1 diabetes in a group of Swedish children. But we now need to study broader populations and biomarkers, as well as figure out the biology behind these signals. Identifying whether there are specific factors in the first several years of life that could be addressed to offset these protein imbalances could help reduce disease risk.

Our group is also studying umbilical cord blood markers in relation to other conditions, including childhood obesity, depression, autism and inflammatory bowel disease. As a data scientist-, pediatrician- and microbiologist-led team, we use biological data to look for early signs of these conditions to find opportunities to support children before those disease pathways are set.

Eric W. Triplett receives funding from the EU Horizon program.

Johnny Ludvigsson receives funding from the EU Horizon program

Angelica P. Ahrens does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

– ref. Umbilical cord blood may hold clues for a child’s risk of developing Type 1 diabetes – https://theconversation.com/umbilical-cord-blood-may-hold-clues-for-a-childs-risk-of-developing-type-1-diabetes-273072

Source: The Conversation – USA – By Anne Toomey McKenna, Affiliated Faculty Member, Institute for Computational and Data Sciences, Penn State

On a Saturday morning, you head to the hardware store. Your neighbors’ Ring cameras film your walk to the car. Your car’s sensors, cameras and microphones record your speed, how you drive, where you’re going, who’s with you, what you say, and biological metrics such as facial expression, weight and heart rate. Your car may also collect text messages and contacts from your connected smartphone.

Meanwhile, your phone continuously senses and records your communications, info about your health, what apps you’re using, and tracks your location via cell towers, GPS satellites and Wi-Fi and Bluetooth.

As you enter the store, its surveillance cameras identify your face and track your movements through the aisles. If you then use Apple or Google Pay to make your purchase, your phone tracks what you bought and how much you paid.

All this data quickly becomes commercially available, bought and sold by data brokers. Aggregated and analyzed by artificial intelligence, the data reveals detailed, sensitive information about you that can be used to predict and manipulate your behavior, including what you buy, feel, think and do.

Companies unilaterally collect data from most of your activities. This “surveillance capitalism” is often unrelated to the services device manufacturers, apps and stores are providing you. For example, Tinder is planning to use AI to scan your entire camera roll. And despite their promises, “opting out” doesn’t actually stop companies’ data collection.

While companies can manipulate you, they cannot put you in jail. But the U.S. government can, and it now purchases massive quantities of your information from commercial data brokers. The government is able to purchase Americans’ sensitive data because the information it buys is not subject to the same restrictions as information it collects directly.

The federal government is also ramping up its abilities to directly collect data through partnerships with private tech companies. These surveillance tech partnerships are becoming entrenched, domestically and abroad, as advances in AI take surveillance to unprecedented levels.

As a privacy, electronic surveillance and tech law attorney, author and legal educator, I have spent years researching, writing and advising about privacy and legal issues related to surveillance and data use. To understand the issues, it is critical to know how these technologies function, who collects what data about you, how that data can be used against you, and why the laws you might think are protecting your data do not apply or are ignored.

Big money for AI-driven tech and more data

Congressional funding is supercharging huge government investments in surveillance tech and data analytics driven by AI, which automates analysis of very large amounts of data. The massive 2025 tax-and-spending law netted the Department of Homeland Security an unprecedented US$165 billion in yearly funding. Immigration and Customs Enforcement, part of DHS, got about $86 billion.

Disclosure of documents allegedly hacked from Homeland Security reveal a massive surveillance web that has all Americans in its scope.

DHS is expanding its AI surveillance capabilities with a surge in contracts to private companies. It is reportedly funding companies that provide more AI-automated surveillance in airports; adapters to convert agents’ phones into biometric scanners; and an AI platform that acquires all 911 call center data to build geospatial heat maps to predict incident trends. Predicting incident trends can be a form of predictive policing, which uses data to anticipate where, when and how crime may occur.

DHS has also spent millions on AI-driven software used to detect sentiment and emotion in users’ online posts. Have you been complaining about Immigration and Customs Enforcement policies online? If so, social media companies including Google, Reddit, Discord, and Facebook and Instagram owner Meta may have sent identifying data, such as your name, email address, phone number and activity, to DHS in response to hundreds of DHS subpoenas served on the companies.

Meanwhile, the Trump administration’s national policy framework for artificial intelligence, released on March 20, 2026, urges Congress to use grants and tax incentives to fund “wider deployment of AI tools across American industry” and to allow industry and academia to use federal datasets to train AI.

Using federal datasets this way raises privacy law concerns because they contain a lifetime of sensitive details about you, including biographical, employment and tax information.

Blurring lines and little oversight

In foreign intelligence work, the funding, development and controlled use of certain AI-driven gathering of data makes sense. The CIA’s new acquisition framework to turbocharge collaboration with the private sector may be legal with proper oversight. But the line between collaborating for lawful national security purposes versus unlawful domestic spying is becoming dangerously blurred or ignored.

For example, the Pentagon has declared a contractor, Anthropic, a national security risk because Anthropic insisted that its powerful agentic AI model, Claude, not be used for mass domestic surveillance of Americans or fully autonomous weapons.

On March 18, 2026, FBI Director Kash Patel confirmed to Congress that the FBI is buying Americans’ data from data brokers, including location histories, to track American citizens.

As the federal government accelerates the use of and investment in AI-driven spy tech, it is mandating less oversight around AI technology. In addition to the national AI policy framework, which discourages state regulation of AI, the president has issued executive orders to accelerate federal government adoption of AI systems, remove state law AI regulation barriers and require that the federal government not procure the use of AI models that attempt to adjust for bias. But using advanced AI systems is risky, given reports of AI agents going rogue, exposing sensitive data and becoming a threat, even during routine tasks.

Your data

The surveillance capitalism system requires people to unwittingly participate in a manipulative cycle of group- and self-surveillance. Neighborhood doorbell cameras, Flock license plate readers and hyperlocal social media sites like Nextdoor create a crowdsourced record of all people’s movements in public spaces.

Sensors in phones and wearable devices, such as earbuds and rings, collect ever more sensitive details. These include health data, including your heart rate and heart rate variability, blood oxygen, sweat and stress levels, behavioral patterns, neurological changes and even brain waves. Smartphones can be used to diagnose, assess and treat Parkinson’s disease. Earbuds could be used to monitor brain health.

This data is not protected under HIPAA, which prohibits health care providers and those working with them from disclosing your health information without your permission, because the law does not consider tech companies to be health care providers nor these wearables to be medical devices.

Legal protections

People have little choice when buying devices, using apps or opening accounts but to agree to lengthy terms that include consent for companies to collect and sell their personal data. This “consent” allows their data to end up in the largely unregulated commercial data market.

The government claims it can lawfully purchase this data from data brokers. But in buying your data in bulk on the commercial market, the government is circumventing the Constitution, Supreme Court decisions and federal laws designed to protect your privacy from unwarranted government overreach.

The Fourth Amendment prohibits unreasonable search and seizure by the government. Supreme Court cases require police to get a warrant to search a phone or use cellular or GPS location information to track someone. The Electronic Communications Privacy Act’s Wiretap Act prohibits unauthorized interception of wire, oral and electronic communications.

Despite some efforts, Congress has failed to enact legislation to protect data privacy, the use of sensitive data by AI systems or to restore the intent of the Electronic Communications Privacy Act. Courts have allowed the broad electronic privacy protections in the federal Wiretap Act to be eviscerated by companies claiming consent.

In my opinion, the way to begin to address these problems is to restore the Wiretap Act and related laws to their intended purposes of protecting Americans’ privacy in communications, and for Congress to follow through on its promises and efforts by passing legislation that secures Americans’ data privacy and protects them from AI harms.

This article is part of a series on data privacy that explores who collects your data, what and how they collect, who sells and buys your data, what they all do with it, and what you can do about it.

Anne Toomey McKenna serves on the Advisory Board to the Institute for Electrical and Electronics Engineers (IEEE)-USA’s Artificial Intelligence Policy Committee (AIPC) and Chairs multiple AIPC subcommittees. The AIPC work involves subject matter and education-related interaction with U.S. Senate and House congressional staffers and the Congressional AI Caucus. McKenna has received funding from the National Security Agency for the development of legal educational materials about cyberlaw (a course which the government still makes available online for the public) and funding from The National Police Foundation together with the U.S. Department of Justice-COPS division for legal analysis regarding the use of drones in domestic policing.

– ref. US government ramps up mass surveillance with help of AI tech, data brokers – and your apps and devices – https://theconversation.com/us-government-ramps-up-mass-surveillance-with-help-of-ai-tech-data-brokers-and-your-apps-and-devices-277440

Source: The Conversation – USA (2) – By Gregor Henze, Professor of Civil, Environmental and Architectural Engineering, University of Colorado Boulder

Many consumers – and state policymakers and even utility companies – are worried about the possibility of large numbers of data centers raising electricity demand and power prices.

Those are real concerns, but our engineering research finds that if designed, constructed and operated carefully, data centers can actually help the communities that host them.

On-site energy storage

Locating power-generating capacity on-site, even using modified jet engines to drive steam turbines, is one emerging option to address data centers’ high power needs.

But there are other options, too. Data centers can install backup batteries that would kick in during an outage or could be used to avoid an outage when demand spikes. The batteries could not only provide power to the data center but also to the surrounding area in times of need.

Various types of battery designs and chemistries offer options for storing enough energy to keep a data center running from a few hours to a few days. This would be critical in supplying electricity during outages because of extreme weather events or excess demand on the grid during periods of peak usage.

Longer duration batteries are also in development. Plans for a new Google data center in Minnesota include solar panels and wind turbines with batteries that would become the world’s largest electricity storage system, with a power capacity of 300 megawatts. Google plans to install iron-air batteries, which are based on chemical reactions with iron to separate and store charge, that would store enough electrical energy to keep a data center running for as much as 100 hours.

Another long-duration battery design uses zinc and water as its key chemical ingredients. It needs relatively little cooling, so batteries can be stacked closely. Significant storage capacity could allow data center owners to flexibly decide when to use energy directly from the grid, when to run off the batteries, when to recharge the batteries, and even whether to sell power back to the grid to earn extra money.

Using waste heat in the community

Data centers produce large amounts of heat, which must be removed from the computer chips. A data center gives off enough heat to potentially keep nearby buildings warm.

Many cities around the world already have what are called “district heating systems,” in which a group of buildings are connected with a pipe network and receive their heat from a central heat source.

Data centers could serve as a heat source for these systems. Recent improvements in these systems, called a “thermal microgrid” or an “ambient loop,” don’t require steam or extremely hot water, but rather use cooler temperatures of water to transport heat between the buildings. Efficient electric heat pumps in each building use that water loop to adjust the building’s air temperature in both winter and summer, creating combined district heating and cooling systems.

In this scenario, data center heat becomes not wasted energy rejected into the air but a money- and energy-saving resource for the local community. For example, a 75 megawatt data center in the town of Mantsala, Finland, is supplying heat to approximately 2,500 homes in the community.

Combining energy production, storage and heating

In our research, we suggest that combining data centers equipped with on-site power generation and battery energy storage and systems that use the waste heat could make the data center a benefit to the community rather than a drain on its resources.

Locating a data center with on-site battery energy storage in a neighborhood and, crucially, connecting them both thermally and electrically could create a small-scale energy community. In addition to providing heat, the data center could help meet the neighborhood’s electricity needs during power outages, storms or peak usage periods.

Improved efficiency of computing

As a fourth dimension to achieving sustainability in data centers, an emerging approach involves drastically reducing the energy consumed for every unit of computation. That would mean exponential growth in computational tasks does not require a corresponding exponential growth in hardware or electricity usage.

Advances in computer chip designs are making data center processors significantly more efficient, able to do larger numbers of more complex calculations more quickly while using less electricity.

But however efficient the chips get, there is both need and opportunity to make them dramatically more so. A growing field called “unconventional computing” is poised to help.

This field, which includes computing approaches inspired by the architecture of the human brain in the emerging technology of neuromorphic AI, as well as engineering innovations such as chips that use their own waste heat, can exhibit thousands-, millions-, or even billionsfold increases in power efficiency. That could make data centers immensely more capable of the computing tasks needed for training AI systems.

Improvements in data center efficiency would reduce the demand for more computing chips and more electricity to run them, even while producing more output.

Researchers across academia, industry and government agencies are developing road maps to scaling these new pathways for energy-efficient computing and are planning for a future where new materials with fundamentally different properties improve efficiency even more.

Some of these advances may be months away, though others could be decades into the future. But we believe that taken together, the opportunities for power generation and storage, waste heat reuse and improved computational efficiency could make data centers beneficial for their communities, and society as a whole, in support of energy affordability and resilience.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. Data centers don’t have to be a burden on local communities – and can even support them by generating power and repurposing waste heat – https://theconversation.com/data-centers-dont-have-to-be-a-burden-on-local-communities-and-can-even-support-them-by-generating-power-and-repurposing-waste-heat-276729

Source: The Conversation – USA (2) – By Grigoris Argeros, Professor of Sociology, Eastern Michigan University

Oakland County, home to nearly 1.3 million residents, ranks among Michigan’s wealthiest counties.

But that description does not tell the whole story.

Since 2020, Oakland County’s population and income have grown steadily. Over the same period, Wayne County’s population declined, and Macomb County experienced slower growth.

Oakland County also has higher incomes overall. Median household income is about US$97,760 in Oakland County, compared with $77,837 in Macomb County and $60,539 in Wayne County.

Some of Oakland’s communities, such as Birmingham and Bloomfield Hills, rank among the most affluent in the tri-county Detroit metro region, with rapidly increasing home prices. Homes in these communities can sell for well over $1 million. Residents here have generally better health outcomes and have remained at the top of the socioeconomic ladder over time. The median household income is $153,510 in Birmingham and $189,942 in Bloomfield Hills.

However, median household incomes can be misleading and mask important differences within the county. Prosperity is not evenly shared, a sign of long-standing economic inequality.

My sociology research focuses on neighborhood and socioeconomic change in American cities. To see where and how divides are emerging, it is necessary to look beyond overall averages and focus on communities within individual counties. Let’s see what we find when we look deeper into the communities in Oakland County.

Measuring inequality

To do that analysis, I used an index of neighborhood socioeconomic status, developed by geographer Joe Darden and political scientist Sameh Kamel. Darden is known for his research on residential segregation and neighborhood inequality in the Detroit region.

Urban researchers and public health scholars use this index to compare neighborhood conditions within and across metropolitan regions and to examine how inequality is distributed.

The index uses census data to combine measures of income, education, housing and employment into a single score ranging from 0 to 100. Higher scores indicate higher socioeconomic position. Like any composite index, it summarizes complex social conditions into a single measure and cannot capture every difference between communities.

Oakland County’s wealth isn’t evenly shared

On this index, Oakland County’s communities are spread across the full socioeconomic range rather than clustering entirely at the top.

In 2023 about 61% fell into the highest socioeconomic tier. The rest were divided between the middle and lowest tiers.

Communities such as Birmingham, Bloomfield Hills, Troy and Rochester Hills remain relatively well-off, with some of the highest scores on the county’s socioeconomic index.

Cities such as Pontiac, along with suburbs such as Oak Park, Hazel Park and Madison Heights, fall in the county’s lowest socioeconomic tier with some of the lowest scores on the index.

Pockets of socioeconomic change

About 80% of communities in Oakland county remained in the same tier between 2010 and 2023.

Socioeconomic stability was strongest at the top: 9 in 10 high-tier communities stayed there.

But the rest of the county tells a different story.

Several communities outside the top tier changed position over time. Wixom and Keego Harbor moved up from the lowest tier into the middle, while Oxford and Rose townships rose from the middle tier into the highest.

Addison, Brandon and White Lake townships shifted from the highest tier into the middle, while Holly township moved from the middle tier into the lowest.

Wealth gaps point to growing disadvantage

These differences point to a growing socioeconomic divide within one of Michigan’s wealthiest counties, similar to trends in other parts of the U.S.

Understanding these divides is key to making sense of the region’s broader challenges, from rising housing costs to differences in job opportunities across metropolitan Detroit.

Communities with a low socioeconomic score have higher poverty and unemployment rates, lower median household income and fewer residents with a college degree or higher. Higher-tier communities show the opposite pattern, with lower poverty and unemployment, higher incomes, higher educational attainment and much higher home values.

The middle tier includes communities such as Ferndale, Auburn Hills, Waterford Township, South Lyon and Wixom. As a group, middle-tier communities resemble the county’s wealthiest areas on some indicators – such as unemployment and homeownership. On others, especially poverty, they remain closer to lower-income places.

A key distinction, however, is the continuing gap between the middle and the top. Middle-tier communities have lower incomes, fewer college graduates and far lower home values than higher-tier communities. The typical home in a middle-tier place is worth about $259,000, compared with more than $405,000 in the highest tier. The gap in median home values leads to significant differences in family wealth, which in turn affects retirement savings, the ability to pay for college and the financial cushion available during economic downturns.

These differences suggest that Oakland County’s stratification is not limited to a divide between struggling areas and wealthy ones. Instead, even its middle-tier communities lag behind the county’s most affluent places, especially when it comes to education and wealth. The divide, therefore, runs not only between the bottom and the top but also between the middle and the most advantaged communities.

How does Oakland compare with nearby counties?

In Oakland County, movement was evenly split, with 10% of communities moving up and 10% moving down, suggesting that gains and losses occurred at roughly the same rate.

In Macomb County, 13% of communities moved up, while 4% moved down. Wayne County showed the least change overall, with about 91% of communities remaining in the same tier between 2010 and 2023. This may be due to decades of economic hardship that have made it more unlikely for communities there to move in either direction.

Oakland County remains one of Michigan’s wealthiest counties. But its communities are not all moving in the same direction. Understanding these differences will be important as the region plans for the future.

The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

– ref. Signs of economic instability emerge in Oakland County, one of Michigan’s wealthiest – https://theconversation.com/signs-of-economic-instability-emerge-in-oakland-county-one-of-michigans-wealthiest-274506