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, 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 P–MEDM 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. Although 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 can 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.

Problem, research strategy, and findings: The American Community Survey (ACS) is a crucial source of socio­demographic data for planners. Since ACS data are estimates rather than actual counts, they contain a degree of statistical uncertainty—referred to as margin of error (MOE)—that planners must navigate when using these data. The statistical uncertainty is magnified when one is working with data for small areas or subgroups of the population or cross-tabulating demographic characteristics. We interviewed (n = 7) and surveyed (n = 200) planners and find that many do not understand the statistical uncertainty in ACS data, find it difficult to communicate statistical uncertainty to stakeholders, and avoid reporting MOEs altogether. These practices may conflict with planners’ ethical obligations under the AICP Code of Ethics to disclose information in a clear and direct way. Takeaway for practice: We argue that the planning academy should change its curriculum requirements and that the profession should improve professional development training to ensure planners understand data uncertainty and convey it to users. We suggest planners follow 5 guidelines when using ACS data: Report MOEs, indicate when they are not reporting MOEs, provide context for the level of statistical reliability, consider alternatives for reducing statistical uncertainty, and always conduct statistical tests when comparing ACS estimates.

Stunting affects an individual's educational and wage-earning potential and can even affect the next generation of children. Most research of childhood stunting focuses on the determinants and correlates that lead to stunting—through nutritional or early infant experiences, with one potential solution to stunting being an increased supply of locally produced food. This research examines the interplay of community-level cropped area as a factor relating to childhood stunting. We use the most recently collected Demographic and Health Survey (DHS) data for Mali, very high resolution remotely sensed imagery, and other remotely sensed data relating to geophysical characteristics to examine the impact of local cultivation on children's health. We focus on evaluating the environmental, community, household, and individual characteristics of the children who report healthy anthropometrics despite the presence of specific stunting risk factors. In adopting this approach to studies of children's health we can shed light on how small-scale agricultural production impacts childhood stunting among at-risk children.

Despite the increasing availability of current national censuses, these datasets are limited by their lack of small area demographic depth. At the same time, spatial microdata that include detailed demographic information are only available for limited geographies, thus limiting the complex analysis of population subgroups within and between small areas. Techniques such as Iterative Proportional Fitting have been previously suggested as a means to generate new data with the demographic granularity of individual surveys and the spatial granularity of small area tabulations of censuses and surveys. This article explores internal and external validation approaches for synthetic, small area, household- and individual-level microdata using a case study for Bangladesh. Using data from the Bangladesh Census 2011 and the Demographic and Health Survey, we produce estimates of infant mortality rate and other household attributes for small areas using a variation of an iterative proportional fitting method called P-MEDM. We conduct an internal validation to determine: whether the model accurately recreates the spatial variation of the input data, how each of the variables performed overall, and how the estimates compare to the published population totals. We conduct an external validation by comparing the estimates with indicators from the 2009 Multiple Indicator Cluster Survey (MICS) for Bangladesh to benchmark how well the estimates compared to a known dataset which was not used in the original model. The results indicate that the estimation process is viable for regions that are better represented in the microdata sample, but also revealed the possibility of strong overfitting in sparsely sampled sub-populations

Landsat is among the most popular satellites used for forest change assessments. Traditionally, Landsat data users relied on annual or biennial images to measure forest recovery after disturbance, a process that is difficult to monitor at broad scales. With the availability of free Landsat data, intra-annual change analyses are now possible. Phenology, the timing of cyclical vegetation events, can be estimated using indices derived from intra-annual remote sensing data and used to classify different vegetation types after a disturbance. We used a smoothed harmonic modelling approach to estimate NDVI and NBR phenology patterns in pre- and post-fire Landsat sample pixels for two forest groups in South Carolina, using nearby unburned samples as an approximate control group. These methods take advantage of all available images collected by Landsat 5, 7, and 8 for the study area. We found that within burned samples, there were differences in phenology for the two forest groups, while the unburned samples showed no forest group differences. Phenology patterns also differed based on fire severity. These methods take advantage of the freely available Landsat archive and can be used to characterize intra-annual fluctuations in vegetation following a variety of disturbances in the southeastern U.S. and other regions. Our approach builds on other harmonic approaches that use the Landsat archive to detect forest change, such as the Continuous Change Detection and Classification (CCDC) algorithm, and provides a tool to describe post-disturbance forest change.