An Integrated Multi-Source Dataset for Measuring Settlement Evolution in the United States from 1810 to 2020

Understanding changes in the built environment is vital for sustainable urban development and disaster preparedness. Recent years have seen the emergence of a variety of global, continent-level, and nation-wide datasets related to the current state and the evolution of the built environment, human settlements or building stocks. However, such datasets may face limitations like incomplete coverage, sparse building information, coarse resolution, and limited timeframes. This study addresses these challenges by integrating three spatial datasets to create an extensive, attribute-rich sequence of settlement layers spanning 200 years for the contiguous U.S. This integration process involves complex data processing, merging property-level real estate, parcel, and remote sensing-based building footprint data, and creating gridded multi-temporal settlement layers. This effort unveils the latest edition (Version 2) of the Historical Settlement Data Compilation for the U.S. (HISDAC-US), which includes the latest land use and structural information as of the year 2021. It enables detailed research on urban form and structure, helps assess and map the built environment's risk to natural hazards, assists in population modeling, supports land use analysis, and aids health studies.

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States

Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.

Assessing the relationship between morphology and mapping accuracy of built-up areas derived from global human settlement data

It is common knowledge that the level of landscape heterogeneity may affect the performance of remote sensing based land use / land cover classification. While this issue has been studied in depth for land cover data in general, the specific relationship between the mapping accuracy and morphological characteristics of built-up surfaces has not been analyzed in detail, an urgent need given the recent emergence of a variety of global, fine-resolution settlement datasets. Moreover, previous studies typically rely on aggregated, broad-scale landscape metrics to quantify the morphology of built-up areas, neglecting the fine-grained spatial variation and scale dependency of such metrics. Herein, we aim to fill this knowledge gap by assessing the associations between localized (focal) landscape metrics, derived from binary built-up surfaces and localized data accuracy estimates. We tested our approach for built-up surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA). Specifically, we examined the explanatory power of landscape metrics with respect to both commission and omission errors in the multi-temporal GHS-BUILT R2018A data product. We found that the Landscape Shape Index (LSI) calculated in focal windows exhibits, on average, the highest levels of correlation to focal accuracy measures. These relationships are scale-dependent, and become stronger with increasing level of spatial support. We found that thematic omission error, as measured by Recall, has the strongest relationship to measures of built-up surface morphology across different temporal epochs and spatial resolutions. The results of our regression analysis (R2>0.9), estimating accuracy based on landscape metrics, confirmed these findings. Lastly, we tested the generalizability of our findings by regionally stratifying our regression models and applying them to a different version of the GHSL (i.e., the GHS-BUILT-S2) and a different study area. We observed varying levels of model transferability, indicating that the relationship between accuracy and landscape metrics may be sensor-specific, and is heavily localized for most accuracy metrics, but quite generalizable for the Recall measure. This indicates that there is a strong and generalizable association between morphological properties of built-up land and the degree to which it is "undermapped".

Differences between gridded population data impact measures of geographic access to healthcare in sub-Saharan Africa

Background

Access to healthcare is imperative to health equity and well-being. Geographic access to healthcare can be modeled using spatial datasets on local context, together with the distribution of existing health facilities and populations. Several population datasets are currently available, but their impact on accessibility analyses is unknown. In this study, we model the geographic accessibility of public health facilities at 100-meter resolution in sub-Saharan Africa and evaluate six of the most popular gridded population datasets for their impact on coverage statistics at different administrative levels.

Methods

Travel time to nearest health facilities was calculated by overlaying health facility coordinates on top of a friction raster accounting for roads, landcover, and physical barriers. We then intersected six different gridded population datasets with our travel time estimates to determine accessibility coverages within various travel time thresholds (i.e., 30, 60, 90, 120, 150, and 180-min).

Results

Here we show that differences in accessibility coverage can exceed 70% at the sub-national level, based on a one-hour travel time threshold. The differences are most notable in large and sparsely populated administrative units and dramatically shape patterns of healthcare accessibility at national and sub-national levels.

Conclusions

The results of this study show how valuable and critical a comparative analysis between population datasets is for the derivation of coverage statistics that inform local policies and monitor global targets. Large differences exist between the datasets and the results underscore an essential source of uncertainty in accessibility analyses that should be systematically assessed.

Gridded land use data for the conterminous United States 1940-2015

Multiple aspects of our society are reflected in how we have transformed land through time. However, limited availability of historical-spatial data at fine granularity have hindered our ability to advance our understanding of the ways in which land was developed over the long-term. Using a proprietary, national housing and property database, which is a result of large-scale, industry-fuelled data harmonization efforts, we created publicly available sequences of gridded surfaces that describe built land use progression in the conterminous United States at fine spatial (i.e., 250 m × 250 m) and temporal resolution (i.e., 1 year - 5 years) between the years 1940 and 2015. There are six land use classes represented in the data product: agricultural, commercial, industrial, residential-owned, residential-income, and recreational facilities, as well as complimentary uncertainty layers informing the users about quantifiable components of data uncertainty. The datasets are part of the Historical Settlement Data Compilation for the U.S. (HISDAC-US) and enable the creation of new knowledge of long-term land use dynamics, opening novel avenues of inquiry across multiple fields of study.

Creeping disaster along the U.S. coastline: Understanding exposure to sea level rise and hurricanes through historical development

Current estimates of U.S. property at risk of coastal hazards and sea level rise (SLR) are staggering—evaluated at over a trillion U.S. dollars. Despite being enormous in the aggregate, potential losses due to SLR depend on mitigation, adaptation, and exposure and are highly uneven in their distribution across coastal cities. We provide the first analysis of how changes in exposure (how and when) have unfolded over more than a century of coastal urban development in the United States. We do so by leveraging new historical settlement layers from the Historical Settlement Data Compilation for the U.S. (HISDAC-US) to examine building patterns within and between the SLR zones of the conterminous United States since the early twentieth century. Our analysis reveals that SLR zones developed faster and continue to have higher structure density than non-coastal, urban, and inland areas. These patterns are particularly prominent in locations affected by hurricanes. However, density levels in historically less-developed coastal areas are now quickly converging on early settled SLR zones, many of which have reached building saturation. These “saturation effects” suggest that adaptation polices targeting existing buildings and developed areas are likely to grow in importance relative to the protection of previously undeveloped land.

MTBF-33: A multi-temporal building footprint dataset for 33 counties in the United States (1900 - 2015)

Despite abundant data on the spatial distribution of contemporary human settlements, historical datasets on the long-term evolution of human settlements at fine spatial and temporal granularity are scarce, limiting our quantitative understanding of long-term changes of built-up areas. This is because commonly used large-scale mapping methods (e.g., computer vision) and suitable data sources (i.e., aerial imagery, remote sensing data, LiDAR data) have only been available in recent decades. However, there are alternative data sources such as cadastral records that are digitally available, containing relevant information such as building construction dates, allowing for an approximate, digital reconstruction of past building distributions. We conducted a non-exhaustive search of open and publicly available data resources from administrative institutions in the United States and gathered, integrated, and harmonized cadastral parcel data, tax assessment data, and building footprint data for 33 counties, wherever building footprint geometries and building construction year information was available. The result of this effort is a unique dataset that we call the Multi-Temporal Building Footprint Dataset for 33 U.S. Counties (MTBF-33). MTBF-33 contains over 6.2 million building footprints including their construction year, and can be used to derive retrospective depictions of built-up areas from 1900 to 2015, at fine spatial and temporal grain. Moreover, MTBF-33 can be employed for data validation purposes, or to train statistical learning models aiming to extract historical information on human settlements from remote sensing data, historical maps, or similar data sources.

Towards the automated large-scale reconstruction of past road networks from historical maps

Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography.

Global Harmonization of Urbanization Measures: Proceed with Care

By 2050, two-thirds of the world's population is expected to be living in cities and towns, a marked increase from today's level of 55 percent. If the general trend is unmistakable, efforts to measure it precisely have been beset with difficulties: the criteria defining urban areas, cities and towns differ from one country to the next and can also change over time for any given country. The past decade has seen great progress toward the long-awaited goal of scientifically comparable urbanization measures, thanks to the combined efforts of multiple disciplines. These efforts have been organized around what is termed the "statistical urbanization" concept, whereby urban areas are defined by population density, contiguity and total population size. Data derived from remote-sensing methods can now supply a variety of spatial proxies for urban areas defined in this way. However, it remains to be understood how such proxies complement, or depart from, meaningful country-specific alternatives. In this paper, we investigate finely resolved population census and satellite-derived data for the United States, Mexico and India, three countries with widely varying conceptions of urban places and long histories of debate and refinement of their national criteria. At the extremes of the urban-rural continuum, we find evidence of generally good agreement between the national and remote sensing-derived measures (albeit with variation by country), but identify significant disagreements in the middle ranges where today's urban policies are often focused.

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems (e.g., the dynamics of the wildland-urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multitemporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values > 0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

Risky Development: Increasing Exposure to Natural Hazards in the United States

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered.

The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks.

Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States

The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c).

A century of decoupling size and structure of urban spaces in the United States

Most cities in the United States of America are thought to have followed similar development trajectories to evolve into their present form. However, data on spatial development of cities are limited prior to 1970. Here we leverage a compilation of high-resolution spatial land use and building data to examine the evolving size and form (shape and structure) of US metropolitan areas since the early twentieth century. Our analysis of building patterns over 100 years reveals strong regularities in the development of the size and density of cities and their surroundings, regardless of timing or location of development. At the same time, we find that trajectories regarding shape and structure are harder to codify and more complex. We conclude that these discrepant developments of urban size- and form-related characteristics are driven, in part, by the long-term decoupling of these two sets of attributes over time.

Two centuries of settlement and urban development in the United States

Over the past 200 years, the population of the United States grew more than 40-fold. The resulting development of the built environment has had a profound impact on the regional economic, demographic, and environmental structure of North America. Unfortunately, constraints on data availability limit opportunities to study long-term development patterns and how population growth relates to land-use change. Using hundreds of millions of property records, we undertake the finest-resolution analysis to date, in space and time, of urbanization patterns from 1810 to 2015. Temporally consistent metrics reveal distinct long-term urban development patterns characterizing processes such as settlement expansion and densification at fine granularity. Furthermore, we demonstrate that these settlement measures are robust proxies for population throughout the record and thus potential surrogates for estimating population changes at fine scales. These new insights and data vastly expand opportunities to study land use, population change, and urbanization over the past two centuries.

The heterogeneity and change in the urban structure of metropolitan areas in the United States, 1990-2010

While the population of the United States has been predominantly urban for nearly 100 years, periodic transformations of the concepts and measures that define urban places and population have taken place, complicating over-time comparisons. We compare and combine data series of officially-designated urban areas, 1990-2010, at the census block-level within Metropolitan Statistical Areas (MSAs) with a satellite-derived consistent series on built-up area from the Global Human Settlement Layer to create urban classes that characterize urban structure and provide estimates of land and population. We find considerable heterogeneity in urban form across MSAs, even among those of similar population size, indicating the inherent difficulties in urban definitions. Over time, we observe slightly declining population densities and increasing land and population in areas captured only by census definitions or low built-up densities, constrained by the geography of place. Nevertheless, deriving urban proxies from satellite-derived built-up areas is promising for future efforts to create spatio-temporally consistent measures for urban land to guide urban demographic change analysis.

Exploring Uncertainty in Canine Cancer Data Sources Through Dasymetric Refinement

In spite of the potentially groundbreaking environmental sentinel applications, studies of canine cancer data sources are often limited due to undercounting of cancer cases. This source of uncertainty might be further amplified through the process of spatial data aggregation, manifested as part of the modifiable areal unit problem (MAUP). In this study, we explore potential explanatory factors for canine cancer incidence retrieved from the Swiss Canine Cancer Registry (SCCR) in a regression modeling framework. In doing so, we also evaluate differences in statistical performance and associations resulting from a dasymetric refinement of municipal units to their portion of residential land. Our findings document severe underascertainment of cancer cases in the SCCR, which we linked to specific demographic characteristics and reduced use of veterinary care. These explanatory factors result in improved statistical performance when computed using dasymetrically refined units. This suggests that dasymetric mapping should be further tested in geographic correlation studies of canine cancer incidence and in future comparative studies involving human cancers.

Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990-2010

Most of future population growth will take place in the world’s cities and towns. Yet, there is no well-established, consistent way to measure either urban land or people. Even census-based urban concepts and measures undergo frequent revision, impeding rigorous comparisons over time and place. This study presents a new spatial approach to derive consistent urban proxies for the US. It compares census-designated urban blocks with proxies for land-based classifications of built-up areas derived from time-series of the Global Human Settlement Layer (GHSL) for 1990–2010. This comparison provides a new way to understand urban structure and its changes: Most land that is more than 50% built-up, and people living on such land, are officially classified as urban. However, 30% of the census-designated urban population and land is located in less built-up areas that can be characterized as mainly suburban and peri-urban in nature. Such insights are important starting points for a new urban research program: creating globally and temporally consistent proxies to guide modelling of urban change.

Exposing the urban continuum: Implications and cross-comparison from an interdisciplinary perspective

There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modelling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e., presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.

HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years

Human settlement plays a key role in understanding social processes such as urbanization and interactions between human and environmental systems but not much is known about the landscape evolution before the era of operational remote sensing technology. In this study, housing and property databases are used to create new gridded settlement layers describing human settlement processes at fine spatial and temporal resolution in the conterminous United States between 1810 and 2015. The main products are a raster composite layer representing the year of first settlement, and a raster time series of built-up intensity representing the sum of building areas in a pixel. Several accompanying uncertainty surfaces are provided to ensure the user is informed about inherent spatial, temporal and thematic uncertainty in the data. A validation study using high quality reference data confirms high levels of accuracy of the resulting data products. These settlement data will be of great interest in disciplines in which the long-term evolution of human settlement represents crucial information to explore novel research questions.

Data-enriched Interpolation for Temporally Consistent Population Compositions

This research evaluates the performance of areal interpolation coupled with dasymetric refinement to estimate different demographic attributes, namely population sub-groups based on race, age structure and urban residence, within consistent census tract boundaries from 1990 to 2010 in Massachusetts. The creation of such consistent estimates facilitates the study of the nuanced micro-scale evolution of different aspects of population, which is impossible using temporally incompatible small-area census geographies from different points in time. Various unexplored ancillary variables, including the Global Human Settlement Layer (GHSL), the National Land-Cover Database (NLCD), parcels, building footprints and the proprietary ZTRAX^® dataset are utilized for dasymetric refinement prior to areal interpolation to examine their effectiveness in improving the accuracy of multi-temporal population estimates. Different areal interpolation methods including Areal Weighting (AW), Target Density Weighting (TDW), Expectation Maximization (EM) and its data-extended approach are coupled with different dasymetric refinement scenarios based on these ancillary variables. The resulting consistent small area estimates of white and black subpopulations, people of age 18-65 and urban population show that dasymetrically refined areal interpolation is particularly effective when the analysis spans a longer time period (1990-2010 instead of 2000-2010) and the enumerated population is sufficiently large (e.g., counts of white vs. black). The results also demonstrate that current census-defined urban areas overestimate the spatial distribution of urban population and dasymetrically refined areal interpolation improves estimates of urban population. Refined TDW using building footprints or the ZTRAX^® dataset outperforms all other methods. The implementation of areal interpolation enriched by dasymetric refinement represents a promising strategy to create more reliable multi-temporal and consistent estimates of different population subgroups and thus demographic compositions. This methodological foundation has the potential to advance micro-scale modeling of various subpopulations, particularly urban population to inform studies of urbanization and population change over time as well as future population projections.

Enhancing Areal Interpolation Frameworks through Dasymetric Refinement to Create Consistent Population Estimates across Censuses

To assess micro-scale population dynamics effectively, demographic variables should be available over temporally consistent small area units. However, fine-resolution census boundaries often change between survey years. This research advances areal interpolation methods with dasymetric refinement to create accurate consistent population estimates in 1990 and 2000 (source zones) within tract boundaries of the 2010 census (target zones) for five demographically distinct counties in the U.S. Three levels of dasymetric refinement of source and target zones are evaluated. First, residential parcels are used as a binary ancillary variable prior to regular areal interpolation methods. Second, Expectation Maximization (EM) and its data-extended version leverage housing types of residential parcels as a related ancillary variable. Finally, a third refinement strategy to mitigate the overestimation effect of large residential parcels in rural areas uses road buffers and developed land cover classes. Results suggest the effectiveness of all three levels of dasymetric refinement in reducing estimation errors. They provide a first insight into the potential accuracy improvement achievable in varying geographic and demographic settings but also through the combination of different refinement strategies in parts of a study area. Such improved consistent population estimates are the basis for advanced spatio-temporal demographic research.

The importance of regional models in assessing canine cancer incidences in Switzerland

Fitting canine cancer incidences through a conventional regression model assumes constant statistical relationships across the study area in estimating the model coefficients. However, it is often more realistic to consider that these relationships may vary over space. Such a condition, known as spatial non-stationarity, implies that the model coefficients need to be estimated locally. In these kinds of local models, the geographic scale, or spatial extent, employed for coefficient estimation may also have a pervasive influence. This is because important variations in the local model coefficients across geographic scales may impact the understanding of local relationships. In this study, we fitted canine cancer incidences across Swiss municipal units through multiple regional models. We computed diagnostic summaries across the different regional models, and contrasted them with the diagnostics of the conventional regression model, using value-by-alpha maps and scalograms. The results of this comparative assessment enabled us to identify variations in the goodness-of-fit and coefficient estimates. We detected spatially non-stationary relationships, in particular, for the variables related to biological risk factors. These variations in the model coefficients were more important at small geographic scales, making a case for the need to model canine cancer incidences locally in contrast to more conventional global approaches. However, we contend that prior to undertaking local modeling efforts, a deeper understanding of the effects of geographic scale is needed to better characterize and identify local model relationships.

Assessing the Accuracy of Multi-Temporal Built-Up Land Layers across Rural-Urban Trajectories in the United States

Global data on settlements, built-up land and population distributions are becoming increasingly available and represent important inputs to a better understanding of key demographic processes such as urbanization and interactions between human and natural systems over time. One persistent drawback that prevents user communities from effectively and objectively using these data products more broadly, is the absence of thorough and transparent validation studies. This study develops a validation framework for accuracy assessment of multi-temporal built-up land layers using integrated public parcel and building records as validation data. The framework is based on measures derived from confusion matrices and incorporates a sensitivity analysis for potential spatial offsets between validation and test data as well as tests for the effects of varying criteria of the abstract term built-up land on accuracy measures. Furthermore, the framework allows for accuracy assessments by strata of built-up density, which provides important insights on the relationship between classification accuracy and development intensity to better instruct and educate user communities on quality aspects that might be relevant to different purposes. We use data from the newly-released Global Human Settlement Layer (GHSL), for four epochs since 1975 and at fine spatial resolution (38m), in the United States for a demonstration of the framework. The results show very encouraging accuracy measures that vary across study areas, generally improve over time but show very distinct patterns across the rural-urban trajectories. Areas of higher development intensity are very accurately classified and highly reliable. Rural areas show low degrees of accuracy, which could be affected by misalignment between the reference data and the data under test in areas where built-up land is scattered and rare. However, a regression analysis, which examines how well GHSL can estimate built-up land using spatially aggregated analytical units, indicates that classification error is mainly of thematic nature. Thus, caution should be taken in using the data product in rural regions. The results can be useful in further improving classification procedures to create measures of the built environment. The validation framework can be extended to data-poor regions of the world using map data and Volunteered Geographic Information.

Internal and International Mobility as Adaptation to Climatic Variability in Contemporary Mexico: Evidence from the Integration of Census and Satellite Data

Migration provides a strategy for rural Mexican households to cope with, or adapt to, weather events and climatic variability. Yet prior studies on "environmental migration" in this context have not examined the differences between choices of internal (domestic) or international movement. In addition, much of the prior work relied on very coarse spatial scales to operationalize the environmental variables such as rainfall patterns. To overcome these limitations, we use fine-grain rainfall estimates derived from NASA's Tropical Rainfall Measuring Mission (TRMM) satellite. The rainfall estimates are combined with Population and Agricultural Census information to examine associations between environmental changes and municipal rates of internal and international migration 2005-2010. Our findings suggest that municipal-level rainfall deficits relative to historical levels are an important predictor of both international and internal migration, especially in areas dependent on seasonal rainfall for crop productivity. Although our findings do not contradict results of prior studies using coarse spatial resolution, they offer clearer results and a more spatially nuanced examination of migration as related to social and environmental vulnerability and thus higher degrees of confidence.

Increasing phenological asynchrony between spring green-up and arrival of migratory birds

Consistent with a warming climate, birds are shifting the timing of their migrations, but it remains unclear to what extent these shifts have kept pace with the changing environment. Because bird migration is primarily cued by annually consistent physiological responses to photoperiod, but conditions at their breeding grounds depend on annually variable climate, bird arrival and climate-driven spring events would diverge. We combined satellite and citizen science data to estimate rates of change in phenological interval between spring green-up and migratory arrival for 48 breeding passerine species across North America. Both arrival and green-up changed over time, usually in the same direction (earlier or later). Although birds adjusted their arrival dates, 9 of 48 species did not keep pace with rapidly changing green-up and across all species the interval between arrival and green-up increased by over half a day per year. As green-up became earlier in the east, arrival of eastern breeding species increasingly lagged behind green-up, whereas in the west—where green-up typically became later—birds arrived increasingly earlier relative to green-up. Our results highlight that phenologies of species and trophic levels can shift at different rates, potentially leading to phenological mismatches with negative fitness consequences.

Assessing effects of structural zeros on models of canine cancer incidence: a case study of the Swiss Canine Cancer Registry

Epidemiological research of canine cancers could inform comparative studies of environmental determinants for a number of human cancers. However, such an approach is currently limited because canine cancer data sources are still few in number and often incomplete. Incompleteness is typically due to under-ascertainment of canine cancers. A main reason for this is because dog owners commonly do not seek veterinary care for this diagnosis. Deeper knowledge on under-ascertainment is critical for modelling canine cancer incidence, as an indication of zero incidence might originate from the sole absence of diagnostic examinations within a given sample unit. In the present case study, we investigated effects of such structural zeros on models of canine cancer incidence. In doing so, we contrasted two scenarios for modelling incidence data retrieved from the Swiss Canine Cancer Registry. The first scenario was based on the complete enumeration of incidence data for all Swiss municipal units. The second scenario was based on a filtered sample that systematically discarded structural zeros in those municipal units where no diagnostic examination had been performed. By means of cross-validation, we assessed and contrasted statistical performance and predictive power of the two modelling scenarios. This analytical step allowed us to demonstrate that structural zeros impact on the generalisability of the model of canine cancer incidence, thus challenging future comparative studies of canine and human cancers. The results of this case study show that increased awareness about the effects of structural zeros is critical to epidemiological research.

Dasymetric Modeling and Uncertainty

Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maximum Entropy Dasymetric Model (P-MEDM) - is presented that enables these sources of uncertainty to be represented and modeled. The P-MEDM propagates uncertainty through the model and yields fine-resolution population estimates with associated measures of uncertainty. This methodology contains a number of other benefits of theoretical and practical interest. In dasymetric modeling, researchers often struggle with identifying a relationship between population and ancillary data layers. The PEDM model simplifies this step by unifying how ancillary data are included. The P-MEDM also allows a rich array of data to be included, with disparate spatial resolutions, attribute resolutions, and uncertainties. While the P-MEDM does not necessarily produce more precise estimates than do existing approaches, it does help to unify how data enter the dasymetric model, it increases the types of data that may be used, and it allows geographers to characterize the quality of their dasymetric estimates. We present an application of the P-MEDM that includes household-level survey data combined with higher spatial resolution data such as from census tracts, block groups, and land cover classifications.

Effects of varying temporal scale on spatial models of mortality patterns attributed to pediatric diarrhea

Public health data is often highly aggregated in time and space. The consequences of temporal aggregation for modeling in support of policy decisions have largely been overlooked. We examine the effects of changing temporal scale on spatial regression models of pediatric diarrhea mortality patterns, mortality rates and mortality peak timing, in Mexico. We compare annual and decadal level univariate models that incorporate known risk factors. Based on normalized sums of squared differences we compare between annual and decadal coefficients for variables that were significant in decadal models. We observed that spurious relationships might be created through aggregating time scales; obscuring interannual variation and resulting in inflated model diagnostics. In fact, variable selection and coefficient values can vary with changing temporal aggregation. Some variables that were significant at the decadal level were not significant at the annual level. Implications of such aggregation should be part of risk communication to policy makers.

Spatially and Temporally Varying Associations between Temporary Outmigration and Natural Resource Availability in Resource-Dependent Rural Communities in South Africa: A Modeling Framework

Migration-environment models tend to be aspatial within chosen study regions, although associations between temporary outmigration and environmental explanatory variables likely vary across the study space. This research extends current approaches by developing migration models considering spatial non-stationarity and temporal variation - through examination of the migration-environment association at nested geographic scales (i.e. whole-population, village, and subvillage) within a specific study site. Demographic survey data from rural South Africa, combined with indicators of natural resource availability from satellite imagery, are employed in a nested modeling approach that brings out distinct patterns of spatial variation in model associations derived at finer geographic scales. Given recent heightened public and policy concern with the human migratory implications of climate change, we argue that consideration of spatial variability adds important nuance to scientific understanding of the migration-environment association.

Robust assessment of spatial non-stationarity in model associations related to pediatric mortality due to diarrheal disease in Brazil

Approximately 1.5 million people, mostly children, die annually due to disease attributed to diarrhea reflecting urgent needs for improved understanding of associations between the disease and potential risk factors. Numerous epidemiological studies found spatially varying (non-stationary) disease associations attributable to changing geographic or demographic context. Spatial non-stationarity implies that average relationships from statistical models fitted to the whole study area might be inappropriate since they do not reflect local conditions. Spatial modeling techniques such as geographically weighted regression (GWR) have limitations in providing statistically robust analysis of spatial non-stationarity. Thus, there is a need for development or expansion of modeling techniques to address this issue. Using data for pediatric diarrheal mortality in Brazil in 2000, and different risk factors, we develop an analytical framework to determine regions of similar (stationary) local associations by combining GWR and max-p regionalization. We fit statistical models to these regions, and compare goodness-of-fit and regionally varying coefficients to the national-scale model measures. The proposed framework allows us to examine (a) impact of non-stationarity for regions of different geographic extent with acceptable statistical power, (b) the explanatory power of each risk factor in each region, and (c) if these regions reflect changing data quality or truly existing variations in putative associations.

Spatial modeling of personalized exposure dynamics: the case of pesticide use in small-scale agricultural production landscapes of the developing world

Background

Pesticide poisoning is a global health issue with the largest impacts in the developing countries where residential and small-scale agricultural areas are often integrated and pesticides sprayed manually. To reduce health risks from pesticide exposure approaches for personalized exposure assessment (PEA) are needed. We present a conceptual framework to develop a spatial individual-based model (IBM) prototype for assessing potential exposure of farm-workers conducting small-scale agricultural production, which accounts for a considerable portion of global food crop production. Our approach accounts for dynamics in the contaminant distributions in the environment, as well as patterns of movement and activities performed on an individual level under different safety scenarios. We demonstrate a first prototype using data from a study area in a rural part of Colombia, South America.

Results

Different safety scenarios of PEA were run by including weighting schemes for activities performed under different safety conditions. We examined the sensitivity of individual exposure estimates to varying patterns of pesticide application and varying individual patterns of movement. This resulted in a considerable variation in estimates of magnitude, frequency and duration of exposure over the model runs for each individual as well as between individuals. These findings indicate the influence of patterns of pesticide application, individual spatial patterns of movement as well as safety conditions on personalized exposure in the agricultural production landscape that is the focus of our research.

Conclusion

This approach represents a conceptual framework for developing individual based models to carry out PEA in small-scale agricultural settings in the developing world based on individual patterns of movement, safety conditions, and dynamic contaminant distributions.

The results of our analysis indicate our prototype model is sufficiently sensitive to differentiate and quantify the influence of individual patterns of movement and decision-based pesticide management activities on potential exposure. This approach represents a framework for further understanding the contribution of agricultural pesticide use to exposure in the small-scale agricultural production landscape of many developing countries, and could be useful to evaluate public health intervention strategies to reduce risks to farm-workers and their families. Further research is needed to fully develop an operational version of the model.

The development and validation of a dysphagia-specific quality-of-life questionnaire for patients with head and neck cancer: the M. D. Anderson dysphagia inventory

OBJECTIVE

To design a reliable and validated self-administered questionnaire whose purpose is to assess dysphagia's effects on the quality of life (QOL) of patients with head and neck cancer.

DESIGN

Cross-sectional survey study.

METHODS

Focus groups were convened for questionnaire development and design. The M. D. Anderson Dysphagia Inventory (MDADI) included global, emotional, functional, and physical subscales. One hundred consecutive adult patients with a neoplasm of the upper aerodigestive tract who underwent evaluation by our Speech Pathology team completed the MDADI and the Medical Outcomes Study 36-Item Short Form Health Survey (SF-36). Speech pathologists completed the Performance Status Scale for each patient. Validity and reliability properties were calculated. Analysis of variance was used to assess how well the MDADI discriminated between groups of patients.

RESULTS

The internal consistency reliability of the MDADI was calculated using the Cronbach alpha coefficient. The Cronbach alpha coefficients of the MDADI subscales ranged from 0.85 to 0.93. Test-retest reliability coefficients of the subscales ranged from 0.69 to 0.88. Spearman correlation coefficients between the MDADI subscales and the SF-36 subscales demonstrated construct validity. Patients with primary tumors of the oral cavity and oropharynx had significantly greater swallowing disability with an adverse impact on their QOL compared with patients with primary tumors of the larynx and hypopharynx (P<.001). Patients with a malignant lesion also had significantly greater disability than patients with a benign lesion (P<.001).

CONCLUSIONS

The MDADI is the first validated and reliable self-administered questionnaire designed specifically for evaluating the impact of dysphagia on the QOL of patients with head and neck cancer. Standardized questionnaires that measure patients' QOL offer a means for demonstrating treatment impact and improving medical care. The development and validation of the MDADI and its use in prospective clinical trials allow for better understanding of the impact of treatment of head and neck cancer on swallowing and of swallowing difficulty on patients' QOL.

Experience with Barton button and peristomal breathing valve attachments for hands-free tracheoesophageal speech

BACKGROUND

Tracheostoma breathing valves permit hands-free tracheoesophageal (TE) speech production; however, few laryngectomees routinely use them because of problems with attachment.

METHODS

We retrospectively reviewed the charts of 45 TE speakers to determine the success rate and factors associated with successful breathing valve use based on attachment. All patients attempted to use a tracheostoma breathing valve with either a standard or customized peristomal housing, or a standard or customized Barton button. Device selection was based on inspection of the patient's neck and peristomal contour. Six to eight consecutive hours of attachment defined success.

RESULTS

Overall, 9% of subjects succeeded with any peristomal attachment as compared to 68% with either a standard (57%) or customized (85%) Barton button. Smooth stomal contour, a contiguous stomal lip, and correct button length were important for successful Barton button use.

CONCLUSIONS

Standard or customized Barton buttons offer excellent alternatives to peristomal housing attachments for hands-free TE speech in select patients.