In recent years, remote sensing, morphometric analysis, and other computational concepts and tools have invigorated the field of geomorphological mapping. Automated interpretation of digital terrain data based on impartial rules holds substantial promise for large dataset processing and objective landscape classification. However, the geomorphological realm presents tremendous complexity and challenges in the translation of qualitative descriptions into geomorphometric semantics. Here, the simple, conventional distinction of V-shaped fluvial and U-shaped glacial valleys was analyzed quantitatively using multi-scale curvature and a novel morphometric variable termed Difference of Minimum Curvature (DMC). We used this automated terrain analysis approach to produce a raster map at a scale of 1:6,000,000 showing the distribution of glaciated valleys across Europe and western Asia. The data set has a cell size of 3 arc seconds and consists of more than 40 billion grid cells. Glaciated U-shaped valleys commonly associated with erosion by warm-based glaciers are abundant in the alpine regions of mid Europe and western Asia but also occur at the margins of mountain ice sheets in Scandinavia. The high-level correspondence with field mapping and the fully transferable semantics validate this approach for automated analysis of yet unexplored terrain around the globe and qualify for potential applications on other planetary bodies like Mars.

For animals with spatially complex behaviours at relatively small scales, the resolution of a global positioning system (GPS) receiver location is often below the resolution needed to correctly map animals' spatial behaviour. Natural conditions such as canopy cover, canyons or clouds can further degrade GPS receiver reception. Here we present a detailed, high-resolution map of a 4.6 ha Neotropical river island and a 8.3 ha mainland plot with the location of every tree >5 cm DBH and all structures on the forest floor, which are relevant to our study species, the territorial frog (Dendrobatidae). The map was derived using distance- and compass-based survey techniques, rooted on dGPS reference points, and incorporates altitudinal information based on a LiDAR survey of the area.

The neighborhood has been used as a sampling unit for exploring variations in health outcomes. In a variety of studies census tracts or ZIP codes have been used as proxies for neighborhoods because the boundaries are pre-defined units for which other data are readily available. However these spatial units can be arbitrary and do not account for social-cultural behaviors and identities that are significant to residents. In this study for the city of Accra, Ghana, our goal was to create a neighborhood map that represented the boundaries generally agreed upon by the residents of the city using the smallest available census unit, the enumeration area (EA), as the base unit. This neighborhood map was then used as the basis for mapping spatial variations in health within the city. The first step in demarcating the boundaries was to identify features that limit a person's movement including the major roads, drainage features, and railroad tracks that people use to partially define their neighborhood boundaries. Once an initial set of boundaries were established, they were iteratively modified by walking the neighborhoods, talking to residents, public officials, and others. The resulting neighborhood map consolidated 1,723 EAs into 108 neighborhoods covering the entire Accra metropolitan area. Results indicated that the team achieved 71 percent accuracy in mapping neighborhoods when the neighborhood keyed to the survey EA was compared with the response given by the interviewees in the 2008-2009 Women's Health Survey of Accra when asked which neighborhood they lived in.

The overall objective of our research project is to understand the spatial inequality in health in Accra, the capital city of Ghana. We also utilize GIS technology to measure the association of adverse health and mortality outcomes with neighborhood ecology. We approached this in variety of ways, including multivariate analysis of imagery classification and census data. A key element in the research has been to obtain in-person interviews from 3,200 female respondents in the city, and then relate health data obtained from the women to the ecology of the neighborhoods in which they live. Detailed maps are a requirement for these field-based activities. However, commercially available street maps of Accra tend to be highly generalized and not very useful for the kind of health and social science research being undertaken by this project, The purpose of this paper is to describe street maps that were created for the project's office in downtown Accra and used to locate households of respondents. They incorporate satellite imagery with other geographic layers to provide the most important visual interpretation of the linkage between imagery and neighborhoods. Ultimately, through a detailed analysis of spatial disparities in health in Accra, Ghana, we aim to provide a model for the interpretation of urban health inequalities in cities of urbanizing and often poor countries.

Multi-day GPS data is increasingly being used in research-including in the field of spatial epidemiology. We present several maps as ways to present multi-day GPS data. Data come from the NYC Low-Income Housing, Neighborhoods and Health Study (=120). Participants wore a QStarz BT-Q1000XT GPS device for about a week (mean: 7.44, SD= 2.15). Our maps show various ways to visualize multi-day GPS data; these data are presented by overall GPS data, by weekday/weekend and by day of the week. We discuss implications for each of the maps.

The Greater Meridiani Planum region on Mars is a key locale for a diverse range of fluvial landforms. Valley networks in this region have a range of geomorphologic styles that include negative relief, positive relief, or some combination of both along their lengths. Using high-resolution ~5-6 m/pixel orbital images in ArcGIS Desktop software, we mapped previously under-recognized fine-scale valley networks within the Greater Meridiani Planum region and recorded their geomorphic characteristics as feature attributes. The objectives in using the mapped features are to 1) document the full range of valley network morphologic types in the region, 2) document changes in morphologic types both on a regional scale and along the valley network segments, and 3) to use the mapped features along with other geologic information from previous studies to better understand landscape evolution in the Greater Meridiani Planum region.

Neighborhood physical disorder, or the deterioration of urban environments, is associated with negative mental and physical health outcomes. Eleven trained raters used CANVAS, a web-based system for conducting reliable virtual street audits, to collect data on nine indicators of physical disorder using Google Street View imagery of 532 block faces in New York City, New York, USA. We combined the block face indicator data into a disorder scale using item response theory; indicators ranged in severity from presence of litter, a weak indicator of disorder, to abandoned cars, a strong indicator. Using this scale, we estimated disorder at the center point of each sampled block. We then used ordinary kriging to interpolate estimates of disorder levels throughout the city. The resulting map condenses a complex estimation process into an interpretable visualization of the spatial distribution of physical disorder in New York City.

Little is known about the spatial clustering of neighborhood deprivation across the United States (U.S.). Using data from the 2010 U.S. Census Bureau, we created a neighborhood deprivation index (NDI: higher NDI indicates higher deprivation/ lower neighborhood socioeconomic status) for each county within the U.S. County level scores were loaded into ArcGIS 10.5.1 where they were mapped and analyzed using Moran's I and Anselin Local Moran's I. Ultimately, NDI varies spatially across the US. The highest NDI scores were found in the Southeastern and Southwestern U.S. states, and inland regions of Southern California. This information is critical for public health initiative development as planners may need to tailor the scale of their efforts based on the higher NDI neighborhoods of the county or geographic region with potentially greater chronic disease burden.

We examine the distribution of residential care in California, showing geographical disparities in care supply and need. We mapped the ratio of beds to older women in Los Angeles and San Diego County census tracts and concentrations of small and large facilities in the Cities of Los Angeles and San Diego. The largest ratios of residential care beds per older women occur on the border of the City of San Diego and on the periphery of Los Angeles County away from the City of Los Angeles. Clusters of small facilities take place in northern Los Angeles and southeastern San Diego, while clusters of large facilities occur in Downtown Los Angeles and near La Jolla. Understanding geographical disparities in residential care supply and need in California can help residential care developers, service providers, and local and state agencies partner in planning for residential care facility development in underserved areas.

Subglacial drainage systems are crucial elements of glaciers and ice sheets because they modulate ice flow velocity. However, logistical challenges of measuring subglacial processes beneath contemporary ice and natural limitations in long-term monitoring hinder our understanding about their spatio-temporal evolution. Subglacial meltwater landforms created by palaeo-ice sheets are records of past subglacial drainage systems and offer the potential to study their large-scale development throughout deglaciation. Although collectively recording subglacial drainage, individual meltwater landforms such as eskers, meltwater channels and meltwater corridors, which comprise tunnel valleys and meltwater tracks (assemblages of landforms in broad, elongated paths with irregular surface texture), have mostly been investigated as separate entities. Using high-resolution (1-2 m) digital elevation models, we map integrated networks of subglacial meltwater landforms, herein called subglacial meltwater routes, on an ice-sheet scale in Fennoscandia. Our map provides a basis for future research on the long-term evolution of subglacial drainage networks and its effect on ice dynamics of the Fennoscandian Ice Sheet.

Social and spatial contexts affect health, and understanding nuances of context is key to informing successful interventions for health equity. Layering mixed methods and mixed scale data sources to visualize patterns of health outcomes facilitates analysis of both broad trends and person-level experiences across time and space. We used micro-scale citizen scientist-collected data from four Bay Area communities along with aggregate epidemiologic and population-level data sets to illustrate barriers to, and facilitators of, physical activity in low-income aging adults. These data integrations highlight the synergistic value added by combining data sources, and what might be missed by relying on either a micro- or macro-level data source alone. Mixed methods and granularity data integration can generate a deeper understanding of environmental context, which in turn can inform more relevant and attainable community, advocacy, and policy improvements.

The health and societal impacts of COVID-19 have created tremendous interest in the scientific community, resulting in interdisciplinary research teams that combine their expertise to provide new insights into the epidemic. However, spatial computation, exploratory data analysis, and spatial data exploration tools have yet to be integrated into these dashboards. Despite the availability of these tools, many of the existing COVID-19 dashboards have provided a limited set of data (i.e., last week's total cases), which limits the user's ability to interact with or customize the data visualization. We present a Spatial Online Analytical Platform that integrates spatial analysis tools that enable users to explore and learn more about spatial patterns of COVID-19. We present three interaction classes designed to support users' needs for knowledge about COVID-19 data trends. Our first interaction class allows users to apply user-defined data classifications (i.e., quantile, equal interval, user-defined) and map color choices. The second interaction class applies a risk index across the time series, informing users of the recent temporal trends. The third interaction class allows users to hypothesize about the presence of spatial clusters and receive results on demand. Our SOLAP platform supports the data analysis and exploration needs of big spatial-temporal data.