Ernesto F. L. Amaral and Nereyda Y. Ortiz Osejo de Lemus of Texas A&M University analyzed American Community Survey data spanning 2005 to 2024, covering adults aged eighteen and older across the entire country. Their central question was deceptively simple: are the places where migration rates run high the same for everyone, and do the groups with the strongest regional concentrations also have the highest individual likelihood of moving? The answer, they found, is frequently no—a discrepancy with significant implications for how researchers and policymakers understand population redistribution in America.

The backdrop to the study is what demographers have called the Great American Migration Slowdown. Between 1968 and 2009, annual interstate migration rates fell from 3.6 percent to just 1.6 percent, and between 1982 and 2015, moves across state and county lines dropped by roughly half. The new analysis confirms this trajectory continued into recent years: overall migration rates bottomed out at 4.57 percent in 2012, recovered modestly through 2019, dipped during the COVID-19 pandemic, and settled back down to 4.58 percent by 2024. The decline is pronounced compared with other developed countries and is thought to reflect reduced responsiveness to regional economic differences, growing place attachment, and shifts in job mobility.

But the aggregate numbers conceal enormous variation. Hispanic, non-Hispanic African American, and non-Hispanic Native American populations consistently showed lower migration rates than non-Hispanic whites and non-Hispanic Asians throughout the two decades studied, with foreign-born Hispanics recording the lowest rates of any group. Foreign-born individuals of all backgrounds moved less often than their U.S.-born counterparts, reflecting differences in settlement histories, social networks, and access to information about destinations. When the researchers modeled individual migration odds statistically, controlling for age, education, income, marital status, children, homeownership, employment, and region of residence, these group differences persisted rather than disappearing.

The spatial half of the analysis used a technique called Local Indicators of Spatial Association, or LISA, which identifies statistically significant clusters of high or low values across neighboring geographic areas. Applied to Migration Public Use Microdata Areas—the finest geography available in the public census microdata—the method revealed striking regional signatures. In 2019, hotspots of high internal migration appeared across parts of the West, including northern California, Nevada, Utah, southern Idaho, and northwestern Wyoming, along with central Texas and northern Florida. By 2024, most of the western hotspots had faded, while Texas and Florida concentrations endured. A long corridor of low migration, stretching from Texas through the Midwest to the Northeast, remained stable across the entire period and even expanded into California and New Mexico.

Those patterns, however, look very different depending on which demographic group is mapped. Non-Hispanic African Americans showed pronounced high-migration clusters across southeastern regions, alongside widespread low-migration zones in the Midwest, Northeast, and West. Hispanic clustering concentrated in California, the Mountain West, Texas, and Florida. Non-Hispanic Native Americans displayed high-migration clusters in the Northwest, Southwest, and parts of the Great Plains, including Arizona, Utah, New Mexico, Colorado, Oklahoma, and Alaska—areas where Indigenous populations are geographically concentrated. Non-Hispanic Asians, by contrast, were characterized mainly by low-migration clusters in southern and Midwestern regions, with some expansion into the Northwest by 2024.

Nativity proved to be a crucial modifier of these spatial patterns. U.S.-born Hispanics showed broad high-migration clustering across western and southern areas, while foreign-born Hispanics displayed far fewer hotspots, concentrated primarily in Texas and Florida by 2024. The contrast was equally stark for African Americans: U.S.-born Black migrants exhibited a broad southeastern pattern, whereas foreign-born Black migrants showed tightly localized clusters in eastern metropolitan areas, including Florida, Atlanta, and Washington, DC—patterns likely reflecting distinct immigrant origins, settlement networks, and labor market incorporation. Foreign-born whites, meanwhile, were marked predominantly by low-migration clusters across central regions of the country.

Perhaps the study’s most striking finding is that regional concentration and individual mobility are genuinely distinct dimensions of migration. Non-Hispanic African Americans and Hispanics both showed lower individual odds of moving than non-Hispanic whites after adjustment, yet both groups displayed strong, persistent regional hotspots. Conversely, non-Hispanic Asians showed relatively high individual mobility despite maps dominated by low-migration clusters. A group can be regionally concentrated precisely because most of its members who do move go to the same places, or because past settlement histories anchor its geography, without its members being frequent movers overall. The authors are careful to note that their LISA maps describe spatial clustering at selected points in time and cannot disentangle the cumulative flows, network effects, and historical settlement processes that may have produced them.

The research also has limitations that the authors acknowledge candidly. The analysis is associational rather than causal, and the logistic regression models do not formally account for spatial autocorrelation, which could lead to underestimated standard errors. The aggregated geographic units limit the ability to study very local dynamics, and detailed subgroup-specific flow analysis at the county level would require restricted Census data. The 2020 ACS, disrupted by pandemic data-collection problems, was tested in robustness checks and found not to alter the main results. Still, the authors argue the two-pronged approach—descriptive spatial clustering paired with adjusted individual-level models—offers a descriptive framework that previous single-lens studies could not provide.

What emerges is a compelling guide for future research. The findings point directly to places and populations where localized studies could uncover the mechanisms behind the patterns: Hispanic migration in Texas and Florida, African American migration in the Southeast, foreign-born African American settlement in Atlanta and Washington, DC, and Native American migration across the Southwest, Great Plains, and Alaska. The authors suggest combining administrative county-to-county flow data with survey microdata, and applying spatially explicit methods such as geographically weighted regression, to test whether the relationships they identify vary across places. As the United States continues to grow more demographically diverse while its residents grow more rooted, understanding precisely where and how different communities redistribute themselves—and why geography and mobility do not always move in tandem—has become an essential question for demographers, economists, and anyone concerned with regional inequality.

Subject of Research: Spatial patterns and demographic heterogeneity of internal migration in the United States by nativity and race/ethnicity

Article Title: Internal migration, demographic heterogeneity, and spatial patterns in the United States

Article References: Internal migration, demographic heterogeneity, and spatial patterns in the United States. (n.d.). https://doi.org/10.1007/s12546-026-09434-0

Image Credits: AI Generated

DOI: 10.1007/s12546-026-09434-0

Keywords: internal migration, demographic heterogeneity, spatial clustering, LISA, nativity, race and ethnicity, American Community Survey, population redistribution, spatial demography, United States, logistic regression, migration slowdown

The research, conducted by Juarez Antônio da Silva Júnior of the Federal University of Pernambuco, examined landslide occurrences recorded by Recife’s Civil Defense between 2015 and 2024. Rather than treating each neighborhood as an isolated unit, as conventional frequency counts and ordinary statistical models tend to do, the study measured whether landslides in one neighborhood were related to landslides in adjacent ones. The central tool was the Global Moran’s Index, a measure of spatial autocorrelation that quantifies whether similar values—here, landslide counts—sit closer together in space than chance would predict. Positive, significant values indicate clustering; values near zero indicate randomness.

The results were unambiguous. In most years of the historical series, the Global Moran’s Index was positive and statistically significant, ranging from moderate values of roughly 0.17 to 0.27 between 2015 and 2017, dipping during an attenuation period from 2018 to 2020, and then strengthening again to a peak of about 0.33 in 2023. In other words, throughout nearly the entire decade, neighborhoods with many landslides tended to be surrounded by other neighborhoods with many landslides, while quiet zones stayed quiet together. The risk, the analysis shows, is not diffuse across the city but concentrated in stable geographic poles.

To locate those poles precisely, the study turned to Local Indicators of Spatial Association, or LISA, a technique proposed by geographer Luc Anselin in 1995 that decomposes global autocorrelation into neighborhood-level clusters. LISA classifies each area into categories such as High-High, where a high-incidence neighborhood is surrounded by similarly high neighbors, or Low-Low, where low values cluster together, along with outlier categories where a unit diverges from its surroundings. The spatial weighting was built using Queen-type contiguity, meaning neighborhoods were considered neighbors if they shared a boundary or a vertex—an approach well suited to the irregular geometries of urban districts.

The local analysis revealed two large, statistically significant High-High hotspots at opposite ends of the municipality. In the North Zone, the neighborhoods of Guabiraba, Passarinho, Dois Unidos, Nova Descoberta, and Linha do Tiro emerged as chronic hotspots, classified as High-High in four to six of the ten years analyzed. In the South Zone, COHAB, Ibura, Jordão, and Barro showed recurring clusters, particularly in 2017, 2018, and above all 2022—a year of exceptional rainfall that produced the highest landslide count in the entire series, with some neighborhoods exceeding 50 occurrences and LISA values greater than 1 across broad contiguous areas. By contrast, central and coastal districts such as Boa Viagem, Pina, Graças, and Derby were dominated by Low-Low patterns, confirming consistently low risk in the flat, highly urbanized heart of the city.

Beyond mapping clusters in individual years, the study introduced two temporal indicators derived from the LISA results: the frequency of High-High classification and a persistence ranking that distinguishes chronic hotspots, significant in at least five of ten years, from episodic and intermittent ones. This persistence analysis showed that the northern hotspot is not a product of any single disaster but a structural condition—vulnerability that is rooted in time as well as space. Descriptive statistics reinforced the picture: neighborhoods such as Dois Unidos displayed extreme interannual swings, recording no cases in 2018, 104 cases in 2019, and only 2 in 2020, while Sen’s slope estimates suggested a modest overall downward trend in several historically critical areas, possibly reflecting municipal interventions.

The spatial patterns aligned closely with independent evidence. When the identified clusters were overlaid on landslide susceptibility maps prepared by the Geological Survey of Brazil (SGB/CPRM) and on the city’s continuous landslide monitoring map, the correspondence was strong. The CPRM maps classify the hilly terrain of the North and South zones as highly susceptible to mass movements, and it is precisely there that the chronic hotspots sit. The city’s own monitoring points, concentrated in the North, Northeast, and South administrative regions, coincide with the High-High clusters. This convergence between empirical occurrence data and model-based susceptibility mapping strengthens confidence that the statistical signals reflect genuine geotechnical and social conditions rather than artifacts of reporting.

The underlying drivers are as much social as geological. Recife, home to nearly 1.5 million people at a density of more than 6,800 per square kilometer, combines a humid tropical climate with steep slopes occupied by self-built housing. Studies cited in the paper document chaotic occupation, narrow streets, inadequate drainage, vegetation loss, and construction in permanent preservation areas in neighborhoods such as Passarinho, where urban growth reached 41 percent between 1975 and 2022 while vegetation cover fell by 30 percent. An estimated 207,000 residents live in at-risk areas, and Recife was ranked the fifth most impacted city by flash floods and landslides in terms of population in CEMADEN studies following the catastrophic May 2022 rains, which affected more than 200,000 inhabitants and caused dozens of deaths.

The methodological lesson extends beyond Recife. Spatial autocorrelation analysis has proven its value in fields from dengue epidemiology in Nepal to hepatitis A mapping in Indonesia and vegetation fragmentation studies in Zimbabwe, and recent work has integrated LISA with machine learning models to improve landslide prediction in China. By demonstrating that a decade of municipal civil defense records, freely available under an open data license and processed with open-source Python libraries such as GeoPandas and PySAL, can yield actionable risk intelligence, the study offers a replicable template for other cities with rugged terrain in the Brazilian Northeast and beyond.

The practical implications are direct. The author recommends prioritizing slope containment works, expanded drainage, vegetation recovery, control of irregular occupation, and relocation of families in imminent danger in the chronic northern hotspots, while reinforcing containment structures, permanent geotechnical monitoring, and contingency planning in the southern cluster. As climate change intensifies extreme rainfall events across the region, distinguishing chronic from episodic risk areas becomes essential for allocating scarce public resources. The decade of data from Recife delivers a clear message: landslide risk has an address, and prevention efforts should go precisely there.

Subject of Research: Spatiotemporal autocorrelation of landslide occurrences across neighborhoods of Recife, Brazil, from 2015 to 2024 using the Global Moran's Index and LISA.

Article Title: Spatiotemporal autocorrelation of landslides in neighborhoods of Recife, Brazil (2015–2024) based on the Moran and Lisa index

Article References: da Silva Júnior, J. A. (2026). Spatiotemporal autocorrelation of landslides in neighborhoods of Recife, Brazil (2015–2024) based on the Moran and Lisa index. Discover Geoscience, 4(1), Article 354. https://doi.org/10.1007/s44288-026-00722-z

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00722-z

Keywords: landslides, spatial autocorrelation, Moran's Index, LISA, Recife, Brazil, spatial hotspots, urban risk, geotechnical instability, disaster risk management, GIS, susceptibility mapping