Extreme heat is a major public health concern, as cities are experiencing rising temperatures, with certain populations disproportionately exposed to high temperatures. A growing body of research has assessed inequities in the distribution of urban heat across population groups; however, this area has not yet been specifically reviewed. Thus, we conduct a scoping review of studies associating urban heat exposures with sociodemographic factors in the United States to provide an overview of the state of the literature. Of the 1381 unique studies identified from two academic search systems, 75 peer-reviewed studies that fit the eligibility criteria were included in the review. These studies were qualitatively summarized based on their geographic coverage, heat and sociodemographic indicators, analytical approaches, and overall findings. Most studies (65%) examined variations in urban heat using land surface temperatures. The most reported sociodemographic categories were income-related measures (81%) and race and ethnicity (72%). At least 11 different types of generalized statistical methods were used to investigate the association between urban heat and sociodemographic indicators, such as t-test (or non-parametric equivalent), cluster analysis, regression analysis etc. Nearly half of the studies (48%) analyzed these associations at the census tract level. Urban heat inequities were the sole focus in only 19% of studies and were studied as either a primary (64%) or secondary (17%) component in combination with other environmental and/or health factors. The review illustrated the spatial distribution of studied locations across cities, counties, metro areas, states, and climate zones, and identified the most represented geographic areas in the literature as New York City, Philadelphia, Phoenix metro area, California, Texas and the Northeast region. Most studied cities were concentrated in cold climate zones followed by the mixed-humid zone. The resulting database of existing literature developed by this scoping review can support future research by guiding the selection of study areas, indicators, and heat inequity analytic methods. The review also highlights the need for further longitudinal investigations that account for historical segregation for a more complete understanding of urban heat inequities.

To mitigate the devastating impacts of hurricanes on people's lives, communities, and societal infrastructures, disaster management would benefit considerably from a detailed understanding of evacuation, including the socio-demographics of the populations that evacuate, or remain, down to disaggregated geographic levels such as local neighborhoods. A detailed household evacuation prediction model for local neighborhoods requires both a robust household evacuation decision model and individual household data for small geographic units. This paper utilizes a recently published statistical meta-analysis for the first requirement and then conducts a rigorous population synthesis procedure for the second. Our model produces predicted non-evacuation rates for all US Census block groups for the Tampa-St. Petersburg-Clearwater Metropolitan Statistical Area for a Hurricane Irma-like storm along with their socio-demographic and hurricane impact risk profiles. Our model predictions indicate that non-evacuation rates are likely to vary considerably, even across neighboring block groups, driven by the variability in evacuation risk profiles. Our results also demonstrate how different predictors may come to the fore in influencing non-evacuation in different block groups, and that predictors which may have an outsize impact on individual household evacuation decisions, such as Race, are not necessarily associated with the greatest differentials in non-evacuation rates when we aggregate households to block group level and above. Our research is intended to provide a framework for the design of hurricane evacuation prediction tools that could be used in disaster management.

Disaster-impacted communities are expected to experience a brief economic disruption, but less resilient communities are at risk for prolonged economic decline, increased unemployment, and shifts in industries and workforces. Florida is historically susceptible to hurricanes, having experienced six major hurricanes (> 110 mph winds) from 2000 to 2021, including Hurricane Michael, a rare Category 5 (> 157 mph winds) in October 2018 that devastated the already economically vulnerable Florida Panhandle. The area experienced a stagnant recovery, and it wasn’t until 2021 that a state-funded economic revitalization program was implemented to aid business restoration. An analysis of unemployment and employment rate trends for all Florida counties that experienced a major hurricane between 2000 and 2021 was conducted to quantify Hurricane Michael's economic impact compared to the other major hurricanes. Using difference-in-differences analysis, results found that the coastal counties impacted by Hurricane Michael experienced up to 11 months of significantly increased unemployment compared to other major Florida storms, from which counties only experienced up to two months of increased unemployment. Additionally, to provide context to the results of Hurricane Michael, observations of the volume trend of employee counts by industry were used to show that during the post-storm year the area saw a reduction in the hospitality, retail, health care and social assistance, and educational services workforces, yet an increase in the construction sector. This study highlights the need for increased disaster resilience against economic disruptions, the anticipation of post-disaster workforce disruptions, as well as support services for workers in a longstanding disaster recovery area. Furthermore, while post-disaster revitalization programs can be beneficial, building economic resilience to support rapid adaptation and recovery is more sustainable.

We systematically review and meta-analyze quantitative prediction models for hurricane evacuation decisions. Drawing on data from 33 prediction models and 29,873 households, we estimate distributions of effects on evacuation decisions for 25 predictors. Mobile home occupancy, evacuation orders, and having an evacuation plan showed the largest positive effects on evacuation, whereas increased age and Black race showed the largest negative effects. These results highlight the importance of both social-economic-structural factors and government action, such as evacuation orders, for enabling evacuation behaviors. Moderator analyses showed that models built using real-hurricane decisions showed larger effects than models of hypothetical decisions, especially for the strongest predictors. Additionally, models in Florida had more consistent results than for other U.S. states, and models with a larger number of covariates showed smaller effect sizes than models with fewer covariates. Importantly, our study improves methodologically and inferentially over previous reviews of this literature.

En route to a comprehensive literature review of map literacy in the next chapter, we come at the subject with an arc through “quantitative literacy,” the term by which numeracy is more generally known in the United States. Our goal in this targeted review of numeracy and quantitative literacy is to build a directed concept chain – namely, literacy → numeracy → quantitative literacy → graph literacy → graphicacy → maps – the next step of which is map literacy.

A new three-literacy Venn model is introduced building on the two-set Venn diagram introducing the discussion of quantitative literacy in Chap. 1 . The three sets represent the quantitative literacy and map literacy of Fig. 1.1 and an additional literacy for required background knowledge. For reference maps, this third set generally represents geographic literacy, with its focus on the locational information of mapped features and/or information regarding their formation. For thematic maps, the third set generally represents thematic literacy and is focused on the thematic information embedded in the mapped features.

This chapter focuses on the right side of the triangular plot. Without map scale as an organizing framework, this chapter, instead, uses a broad selection of published thematic maps that address public interest or research questions. This chapter considers both the knowledge and skills involved in the map reading and interpretation of these various maps, along with where they would position in the triangular plot.

This book discusses the field of Quantitative Map Literacy, and approaches the study of map literacy to inform educational pedagogy and practices.

With the triangular-plot graphic to discuss various types of maps, and the three-set Venn model for various literacies, the knowledge and skills involved in map reading and interpretation can be explored systematically. This chapter focuses on the left side of the triangle. Specific map literacy (ML), quantitative literacy (QL), and geographic literacy (GL) knowledge and skills involved in solving word problems are identified for a university campus parking map, topographic maps, a Mercator projection world map, and a subway map.

The origin of electrical activity accompanying volcanic ash plumes is an area of heightened interest in volcanology. However, it is unclear how intense an eruption needs to be to produce lightning flashes as opposed to “vent discharges,” which represent the smallest scale of electrical activity. This study targets 97 carefully monitored plumes <3 km high from Sakurajima volcano in Japan, from June 1 to 7, 2015. We use multiparametric measurements from sensors including a nine‐station lightning mapping array and an infrared camera to characterize plume ascent. Findings demonstrate that the impulsive, high velocity plumes (>55 m/s) were most likely to create vent discharges, whereas lightning flashes occurred in plumes with high volume flux. We identified conditions where volcanic lightning occurred without detectable vent discharges, highlighting their independent source mechanisms. Our results imply that plume dynamics govern the charging for volcanic lightning, while the characteristics of the source explosion control vent discharges.

Various types of maps are explored and located in the triangular-plot graphic introduced in the previous chapter. Reference maps tend to the left side of this triangular plot while thematic maps to the right side, whereas land-use maps, and others, where both locational and thematic information are of fairly equal importance are in a vertical wedge along the medial height. Large-scale topographic maps, regional maps, world maps, topological maps (e.g., subway maps), choropleth maps, cartograms, weather maps, geologic maps, and maps of airline routes – all these and more – can be positioned on this triangular-plot visualization.

In this chapter, a conceptual triangular-plot model is introduced to discuss how maps vary according to two parameters that we consider important to map literacy and to the distribution of map-reading knowledge and skills. The graphic is an upright equilateral triangle. The first parameter represents a map’s position on a continuum from purely locational information on the left to purely thematic information on the right. The second parameter, which represents the level (a judgment) of the map’s generalization and distortion, positions the map vertically in the triangle.

In this chapter, we review previous literature on map literacy for both reference and thematic maps. We note how prior individual studies have historically been skewed to one or the other of these two broad categories, have focused mainly on low-level skills, and have often been limited to studies of single types of maps (within a category) or to studies of map symbolization.

Sakurajima volcano in Japan is known for frequent eruptions containing prolific volcanic lightning. Previous studies from eruptions at Redoubt have shown preliminary correlations between seismic, infrasound, and radio frequency signals. This study uses field data collected at Sakurajima from 28 May–7 June 2015 and multivariable statistical modeling to quantify these relationships. We build regression equations to examine each of the following parameters of electrical activity: (1) the presence of electrical activity, (2) the presence of the radio frequency signal called continual radio frequency impulses (CRF), (3) the presence of lightning, (4) the overall duration of electrical activity, and (5) the total number of radio frequency sources located by a lightning mapping array. We model these response variables against: (1) seismic energy, (2) infrasound energy, (3) seismic duration, (4) infrasound duration, and (5) the volcano acoustic seismic ratio. Our final regression equations show that each parameter of electrical activity is best defined by a separate set of response parameters, but overall events with greater explosivity correlate with higher amounts of electrical activity. Specifically, (1) the probability of CRF occurring, and the overall number of located radio frequency sources are likely related to deeper fragmentation depths; (2) the probability of electrical activity occurring at all, and specifically the probability of lightning being generated are correlated with high infrasound energies indicating that the gas thrust phase of plume formation plays an important role in charge generation; and (3) the longer an eruption (as determined by the duration of the infrasound signal) the longer we can expect to see radio frequency signals generated.

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range.

The capability of GIS to be able to store, retrieve, display, and analyze large quantities of spatially referenced data has facilitated the rapid growth of geographic-based research within various health fields, including epidemiology and health care provisioning. This is reflected in the numerous texts focused entirely on GIS and health, as well as texts focused on geography and health that include substantial material on GIS. There are also journals where GIS-based health research frequently appears. This bibliography lists a number of these texts and journals. GIS-based health research is wide ranging, but several major themes can be identified. In this bibliography the first major theme presented is GIS-based visualization of health information, a topic which involves geocoding, disease-mapping methodologies, and alternative cartographic schemes of representation. The second major theme is GIS-derived measures for health research, where the focus is on how GIS has transformed how accessibility to health care is measured and enabled complex forms of environmental exposures to be derived for use in epidemiologic studies. The final theme is GIS-enabled analysis for health research where a deliberate choice was made to focus on those analytical areas which are only feasible through the specific spatial analytical capabilities of GIS.

We define quantitative map literacy (QML), a cross between map literacy and quantitative literacy (QL), as the concepts and skills required to accurately read, use, interpret, and understand the quantitative information embedded in a geospatial representation of data on a geographic background. Long used as tools in technical geographic fields, maps are now a common vehicle for communicating quantitative information to the public. As such, QML has potential to stand alongside health numeracy and financial literacy as an identifiable subdomain of transdisciplinary QL. What concepts and skills are crucial for QML? The obvious answer is, “It depends on the type of map.” Therefore, our first task, and the subject of this paper, is to develop a framework to think and talk about the panoply of maps in a way that permits us to consider the range and distribution of QML content. We use an equilateral triangular plot to conceptualize maps in terms of locational information (L), thematic information (T), and generalization-distortion (G-D), and parameterize the plot with an L/T ratio (horizontal; reflecting the historical practice of cartographers to distinguish locational-reference maps from thematic maps) and G-D levels increasing from base to apex. We show positions for a wide variety of maps (e.g., topographic maps, weather maps, engineering-survey plots, subway maps, maps of air routes, a cartoon map of Orlando for tourists, driving-time maps, county-wide population maps, county-wide multivariable population and income maps, world political map, land use maps, and cartograms). The analysis of how these maps vary across the triangle allows us to proceed with an examination of how QML varies across the panoply of maps.

This paper presents an agent based model simulating animal tracking datasets for individual animals based on observed habitat use characteristics, movement behaviours and environmental context. The model is presented as an alternative simulation methodology for movement trajectories for animal agents, useful in home range, habitat use and animal interaction studies. The model was implemented in NetLogo 5.1.0 using observed behavioural data for the Muscovy duck, obtained in a previous study. Four test scenarios were completed to evaluate the fidelity of model results to behavioural patterns observed in the field. Results suggest the model framework illustrated in this paper provides an effective alternative to traditional animal movement simulation methods such as correlated random walks.