Overview: FEMA and Argonne National Laboratory completed the first analysis of community resilience indicators in 2018 and repeated the process in 2022. The analysis process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the research team to ensure the methodologies are:

Quantitative, Data and methodology are publicly available, Calculated at the county level or lower, Examine generalized hazard risk (rather than a singular hazard), and Focused on pre-disaster community conditions.

After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visualization, conducts a correlation analysis, and creates a composite index called the "FEMA Community Resilience Challenges Index (CRCI)".

In 2022, the FEMA and Argonne research team updated the 2018 literature review and examined 14 methodologies published between 2003 and 2021. Examining the indicators used in these methodologies, the research team identified 22 indicators as commonly used (indicators used in five or more of the 14 methodologies). The research team produced the FEMA CRCI at the county and the census tract levels. More details on these indicators and the research process can be found in the FEMA CRCI Storymap. Data last updated on May 13, 2023. This is the latest available version of the CRCI. Questions or comments about this layer? Email the RAPT team at FEMA-TARequest@fema.dhs.gov

The Composite Risk Index Plus Sea Level Rise is generated by multiplying the SACS Composite Exposure Index by the SACS Combined Hazard Plus Sea Level Rise. The Composite Exposure Index is generated following the methodology for the Tier 1 Risk Assessment cited in the USACE North Atlantic Coastal Comprehensive Study (NACCS). The Composite Exposure Index is created by summing three separate exposure indices which are weighted on a percentage basis: Population and Infrastructure Index 60%, Environmental Cultural and Habitat 30%, and Social Vulnerability 10%. For additional information on the input datasets and methodology for the exposure indices, please reference the NACCS report, Appendix C, page 103: https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf.The SACS Combined Hazard Index Plus Sea Level Rise depicts the percentage annual chance of a specific flood hazard, with an additional 3 ft. of sea level rise added to the flood surface elevation. The three flooding hazards depicted are the 10% annual chance flooding event, the 1% annual chance flooding event, and Category 5 Hurricane Maximum of Maximums. The 10% annual chance flooding is derived via a statistical analysis of tide gauges within the SACS study area, utilizing methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance flooding event raster index grid cells are assigned a value of 0.1. The 1% annual chance flooding are aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and raster index grid cells are assigned a value of 0.01. The Category 5 Maximum of Maximums hazard is pulled from NOAA’s storm surge SLOSH inundation data. These data are assigned a value of 0.001 to reflect the low probability of a Category 5 event. Three feet of sea level rise were added to the 10% and 1% annual chance flooding events for this hazard grid.The Composite Risk Index Plus Sea Level Rise grid resolution is 30 meters.This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

Background: There is a current lack of any composite measure for the effective tracking and monitoring of clinical change in individuals exposed to repetitive head impacts (RHI). The aim of this study is to create a composite instrument for the purposes of detecting change over time in cognitive and behavioral function in individuals exposed to RHI.Methods: The data to derive the composite instrument came from the Professional Fighters Brain Health Study (PFBHS), a longitudinal study of active and retired professional fighters [boxers and mixed martial arts (MMA) fighters] and healthy controls. Participants in the PFBHS underwent assessment on an annual basis that included computerized cognitive testing and behavioral questionnaires. Multivariate logistic regression models were employed to compare active fighters (n = 117) with controls (n = 22), and retired fighters (n = 26) with controls to identify the predictors that could be used to differentiate the groups over time. In a second step, linear discriminant analysis was performed to derive the linear discriminant coefficients for the three groups by using the predictors from the two separate logistic regression models.Results: The composite scale is a weighted linear value of 12 standardized scores consisting of both current and yearly change scores in domains including: processing speed, choice reaction time, semantic fluency, letter fluency, and Barrett Impulsiveness Scale. Because the weighting of values differed between active and retired fighters, two versions emerged. The mean and standard deviation ratio (MSDR) showed that the new index had better sensitivity compared to the individual measures, with the ratio of MSDR of the new index to that of the existing measures of at least 1.84.Conclusion: With the increasing need for tools to follow individuals exposed to RHI and the potential of clinical trials on the horizon for CTE, the RHICI is poised to serve as an initial approach to a composite clinical measure.

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada. HSIs were calculated for spring, summer, and winter sage-grouse seasons, and then multiplied together to create this composite dataset. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap, SageStitch, LANDFIRE, and the CA Fire and Resource Assessment Program. The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished) and conifer (P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell for each season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. The three seasonal HSIs were then multiplied to create a composite annual HSI. REFERENCES: California Forest and Resource Assessment Program. 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

The Composite Risk Index is generated by multiplying the SACS Composite Exposure Index by the SACS Combined Hazard. The USACE South Atlantic Coastal Study’s (SACS) initial study product is the Tier 1 Risk Assessment. The main output of the Tier 1 Risk Assessment is the Composite Risk Index. In the SACS, risk is defined as the probability of a hazard, multiplied against the exposure of a specific element. The Composite Exposure Index is generated following the methodology for the Tier 1 Risk Assessment cited in the USACE North Atlantic Coastal Comprehensive Study (NACCS). The Tier 1 Composite Risk Index is derived from multiplying the SACS Composite Exposure Index by the SACS Combined Hazards Present index. The Composite Risk Index is depicted as a classified grid using the Jenks or Natural Breaks classification. The four classes are Low Potential Risk, Medium Potential Risk, Medium/High Potential Risk, and High Potential Risk. The resolution of the grid is 30 meters.The Composite Exposure Index is created by summing three separate exposure indices which are weighted on a percentage basis: Population and Infrastructure Index 60%, Environmental Cultural and Habitat 30%, and Social Vulnerability 10%. For additional information on the input datasets and methodology for the exposure indices, please reference the NACCS report, Appendix C, page 103: https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf.The SACS Combined Hazard Index depicts the percentage annual chance of a specific flood hazard. The three flooding hazards depicted are the 10% annual chance flooding event, the 1% annual chance flooding event, and Category 5 Hurricane Maximum of Maximums. The 10% annual chance flooding is derived via a statistical analysis of tide gauges within the SACS study area, utilizing methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance flooding event raster index grid cells are assigned a value of 0.1. The 1% annual chance flooding are aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and raster index grid cells are assigned a value of 0.01. The Category 5 Maximum of Maximums hazard was pulled from NOAA’s storm surge SLOSH inundation data. These data are assigned a value of 0.001 to reflect the low probability of a Category 5 event.The Composite Risk Index grid resolution is 30 meters.This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

The Composite Exposure Index was generated following the methodology of the Tier 1 Risk Assessment cited in the USACE North Atlantic Coast Comprehensive Study (NACCS): https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. The Composite Exposure Index is created by summing the three Tier 1 Risk Assessment exposure indices on a percentage basis, Population and Infrastructure Exposure Index 60%, Environmental and Cultural Resources Exposure Index 30%, and Social Vulnerability Exposure Index 10%. The resulting grid is displayed with a stretch symbology, percent clip (min -.5 max .5), with 0 being the lowest exposure, and 1 being the highest exposure. The resolution of the grid is 30 meters. This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

Random-effects regression analysis showing the change in mental health scores associated with the implementation of COVID-19 lockdown measures.

An Environmental Quality Index (EQI) for all counties in the United States for the time period 2000-2005 was developed which incorporated data from five environmental domains: air, water, land, built, and socio-demographic. The EQI was developed in four parts: domain identification; data source identification and review; variable construction; and data reduction using principal components analysis (PCA). The methods applied provide a reproducible approach that capitalizes almost exclusively on publically-available data sources. The primary goal in creating the EQI is to use it as a composite environmental indicator for research on human health. A series of peer reviewed manuscripts utilized the EQI in examining health outcomes. This dataset is not publicly accessible because: This series of papers are considered Human health research - not to be loaded onto ScienceHub. It can be accessed through the following means: The EQI data can be accessed at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: EQI data, metadata, formats, and data dictionary all available at website.

This dataset is associated with the following publications: Gray, C., L. Messer, K. Rappazzo, J. Jagai, S. Grabich, and D. Lobdell. The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(8): e0203301, (2018). Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018). Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

Children and adolescents are increasingly susceptible to issues related to anxiety and depression symptoms. The literature does not present a consensus on the composition of indicators that make predictions, prognostic algorithms, or management strategies in mental health promotion and prevention. Most studies primarily focus on the consequences observed in adulthood. This study develops a multidimensional representation of the propensity of children and adolescents to have difficulties in the field of anxiety and depression. The Ordered Weighted Averaging (OWA) operator was used to create a composite indicator, and three quality tests validated the results. For this, it uses information about different dimensions associated with adversity in childhood and adolescence from 54 countries sourced from UNICEF’s Multiple Indicator Cluster Surveys to compare the values of proposed dimensions across continents. The generated composite indicator reveals that, on average, countries in Africa show a higher propensity for children and adolescents to present difficulties in the anxiety and depression fields. In the opposite position, the Americas have the lowest average propensity for these mental health conditions. The validation of the results through quality tests reinforces confidence in the direction indicated by the findings, enhancing the decision-making process when dealing with multidimensional phenomena.

This shapefile represents proposed management categories (Core, Priority, General, and Non-Habitat) derived from the intersection of habitat suitability categories and lek space use. Habitat suitability categories were derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Management Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite annual HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). SPACE USE INDEX CALCULATION: Updated lek coordinates and associated trend count data were obtained from the 2015 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/20/2015). Leks count data from the California side of the Buffalo-Skedaddle and Modoc PMU's that contributed to the overall space-use model were obtained from the Western Association of Fish and Wildlife Agencies (WAFWA), and included count data up to 2014. We used NDOW data for border leks (n = 12), and WAFWA data for those fully in California and not consistently surveyed by NDOW. We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years (through the 2014 breeding season). Pending leks comprised leks without consistent breeding activity during the prior 3 - 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’, or newly discovered leks with at least 2 males. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2011 - 2015) for the number of male grouse (or NDOW classified 'pseudo-males' if males were not clearly identified but likely) attending each lek was calculated. Compared to the 2014 input lek dataset, 36 leks switched from pending to inactive, and 74 new leks were added for 2015 (which included pending ‘new’ leks with one year of counts. A total of 917 leks were used for space use index calculation in 2015 compared to 878 leks in 2014. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2011 - 2015) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was re-scaled between zero and one by dividing by the maximum pixel value. The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 - 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was re-scaled between zero and one by dividing by the maximum cell value. A Spatial Use Index (SUI) was calculated by taking the average of the lek utilization distribution and non-linear distance-to-lek rasters in ArcGIS, and re-scaled between zero and one by dividing by the maximum cell value. The volume of the SUI at cumulative at specific isopleths was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the Nevada state boundary. MANAGEMENT CATEGORIES: The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was categorized into 4 classes: High, Moderate, Low, and Non-Habitat as described above, and intersected with the space use index to form the following management categories . 1) Core habitat: Defined as the intersection between all suitable habitat (High, Moderate, and Low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality habitat

Tactile Ground Surface Indicator Market Outlook

According to our latest research, the global tactile ground surface indicator market size reached USD 1.11 billion in 2024, reflecting robust demand across transportation and public infrastructure projects worldwide. The market is projected to grow at a CAGR of 6.4% from 2025 to 2033, reaching a forecasted value of USD 1.93 billion by 2033. This growth is driven by expanding urbanization, stringent regulations for disability access, and increasing investments in transportation and public safety infrastructure.

One of the most significant growth factors for the tactile ground surface indicator market is the global emphasis on accessibility and inclusivity in public spaces. Governments and regulatory bodies are mandating the installation of tactile indicators in transportation hubs, commercial complexes, and public infrastructure to ensure safety and ease of navigation for visually impaired individuals. The implementation of standards such as the Americans with Disabilities Act (ADA) in the United States, the Disability Discrimination Act (DDA) in Australia, and similar regulations across Europe and Asia Pacific has accelerated the adoption of tactile ground surface indicators. These regulations not only drive compliance but also encourage innovation in product design and material selection, further fueling market growth.

The rapid pace of urbanization and infrastructure development, particularly in emerging economies, is another key driver for the tactile ground surface indicator market. As cities expand and modernize, there is a growing need to upgrade transportation networks, pedestrian pathways, and public buildings to accommodate increasing populations and diverse user needs. Investments in smart city initiatives and public transit systems are creating substantial opportunities for tactile indicator manufacturers. Moreover, the integration of tactile ground surface indicators in new and retrofit projects is becoming a standard practice, supported by government funding and public-private partnerships aimed at enhancing urban mobility and safety.

Technological advancements and material innovation are also contributing to the market's upward trajectory. Manufacturers are focusing on developing durable, weather-resistant, and aesthetically pleasing tactile indicators using advanced materials such as polyurethane, stainless steel, and composite blends. These innovations address the challenges of wear and tear in high-traffic areas, ensuring long-term functionality and minimal maintenance. Additionally, the trend toward modular and customizable solutions allows for greater flexibility in design and installation, catering to the unique requirements of different applications and end-users. As a result, the tactile ground surface indicator market is witnessing increased adoption across a wide range of sectors, from transportation and public infrastructure to commercial and residential buildings.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for tactile ground surface indicators, driven by large-scale infrastructure projects and heightened awareness of accessibility standards. North America and Europe continue to dominate in terms of market share, supported by mature regulatory frameworks and ongoing investments in public safety. Meanwhile, Latin America and the Middle East & Africa are showing promising growth potential, fueled by urban development and efforts to align with international accessibility norms. The global outlook remains positive, with sustained demand anticipated across all major regions throughout the forecast period.

Product Type Analysis

The tactile ground surface indicator market is segmented by product type into warning indicators, directional indicators, composite indicators, and others. Warning indicators, also known as hazard or attention indicators, are designed to alert visually impaired individuals to potential hazards such as platform edges,

Children and adolescents are increasingly susceptible to issues related to anxiety and depression symptoms. The literature does not present a consensus on the composition of indicators that make predictions, prognostic algorithms, or management strategies in mental health promotion and prevention. Most studies primarily focus on the consequences observed in adulthood. This study develops a multidimensional representation of the propensity of children and adolescents to have difficulties in the field of anxiety and depression. The Ordered Weighted Averaging (OWA) operator was used to create a composite indicator, and three quality tests validated the results. For this, it uses information about different dimensions associated with adversity in childhood and adolescence from 54 countries sourced from UNICEF’s Multiple Indicator Cluster Surveys to compare the values of proposed dimensions across continents. The generated composite indicator reveals that, on average, countries in Africa show a higher propensity for children and adolescents to present difficulties in the anxiety and depression fields. In the opposite position, the Americas have the lowest average propensity for these mental health conditions. The validation of the results through quality tests reinforces confidence in the direction indicated by the findings, enhancing the decision-making process when dealing with multidimensional phenomena.

The Composite Risk Index is generated by multiplying the SACS Composite Exposure Index by the SACS Combined Hazard. The USACE South Atlantic Coastal Study’s (SACS) initial study product is the Tier 1 Risk Assessment. The main output of the Tier 1 Risk Assessment is the Composite Risk Index. In the SACS, risk is defined as the probability of a hazard, multiplied against the exposure of a specific element. The Composite Exposure Index is generated following the methodology for the Tier 1 Risk Assessment cited in the USACE North Atlantic Coastal Comprehensive Study (NACCS). The Tier 1 Composite Risk Index is derived from multiplying the SACS Composite Exposure Index by the SACS Combined Hazards Present index. The Composite Risk Index is depicted as a classified grid using the Jenks or Natural Breaks classification. The four classes are Low Potential Risk, Medium Potential Risk, Medium/High Potential Risk, and High Potential Risk. The resolution of the grid is 30 meters.The Composite Exposure Index is created by summing three separate exposure indices which are weighted on a percentage basis: Population and Infrastructure Index 60%, Environmental Cultural and Habitat 30%, and Social Vulnerability 10% (For the US Virgin Islands, 65% Population and Infrastructure data and 35% Environmental, Cultural and Habitat due to a lack of CDC Social Vulnerability data for the USVI). For additional information on the input datasets and methodology for the exposure indices, please reference the NACCS report, Appendix C, page 103: https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf.The SACS Combined Hazard Index depicts the percentage annual chance of a specific flood hazard. The three flooding hazards depicted are the 10% annual chance flooding event, the 1% annual chance flooding event, and Category 5 Hurricane Maximum of Maximums. The 10% annual chance flooding is derived via a statistical analysis of tide gauges within the SACS study area, utilizing methodology developed by the USACE Engineering and Research Development Center (https://hdl.handle.net/11681/7353 or https://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-15-00031.1). The 10% annual chance flooding event raster index grid cells are assigned a value of 0.1. The 1% annual chance flooding are aggregated from FEMA’s National Flood Hazard Layer (https://www.fema.gov/national-flood-hazard-layer-nfhl) and raster index grid cells are assigned a value of 0.01. The Category 5 Maximum of Maximums hazard was pulled from NOAA’s storm surge SLOSH inundation data. These data are assigned a value of 0.001 to reflect the low probability of a Category 5 event.The Composite Risk Index grid resolution is 30 meters.This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

The Composite Exposure Index was generated following the methodology of the Tier 1 Risk Assessment cited in the USACE North Atlantic Coast Comprehensive Study (NACCS): https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdf. The Composite Exposure Index is created by summing the three Tier 1 Risk Assessment exposure indices on a percentage basis, Population and Infrastructure Exposure Index 60%, Environmental and Cultural Resources Exposure Index 30%, and Social Vulnerability Exposure Index 10%. The resulting grid is displayed with a stretch symbology, percent clip (min -.5 max .5), with 0 being the lowest exposure, and 1 being the highest exposure. The resolution of the grid is 30 meters. This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

Children and adolescents are increasingly susceptible to issues related to anxiety and depression symptoms. The literature does not present a consensus on the composition of indicators that make predictions, prognostic algorithms, or management strategies in mental health promotion and prevention. Most studies primarily focus on the consequences observed in adulthood. This study develops a multidimensional representation of the propensity of children and adolescents to have difficulties in the field of anxiety and depression. The Ordered Weighted Averaging (OWA) operator was used to create a composite indicator, and three quality tests validated the results. For this, it uses information about different dimensions associated with adversity in childhood and adolescence from 54 countries sourced from UNICEF’s Multiple Indicator Cluster Surveys to compare the values of proposed dimensions across continents. The generated composite indicator reveals that, on average, countries in Africa show a higher propensity for children and adolescents to present difficulties in the anxiety and depression fields. In the opposite position, the Americas have the lowest average propensity for these mental health conditions. The validation of the results through quality tests reinforces confidence in the direction indicated by the findings, enhancing the decision-making process when dealing with multidimensional phenomena.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Latvia was 1,006 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Increasing environmental and socioeconomic transformations in African drylands are driving land degradation. Using the Composite Land Degradation Index, this study assessed physical, chemical and biological degradation by determining their extent and severity. Palapye, an agro-pastoral region in eastern Botswana was used as a case study. Land degradation maps (status and indicators) were created with data from the field, soil chemical properties and image interpretation. Areas in the vicinity of settlements with Luvisols at elevations between 773 and 893 m were most degraded, implying impacts from human activities. This study developed a comprehensive list of of land degradation indicators for Botswana and created additional symbols for mapping indicators. Creation of these reference data for 2015 will facilitate the monitoring of land degradation in Palapye. The integrative and spatially explicit procedure utilized in this study can be adapted for assessing and validating local-level land degradation baseline and estimates towards operationalizing Land Degradation Neutrality in all countries.

Date of Publication: 07/21/2021Name of Person Responsible: Alan HalterDate to be removed/updated: Ongoing updates. Last updated on 10/61/2021.This map includes the variables used to calculate Tree Equity Scores for Austin, Texas. For more information, contact the original data author, American Forests. Layer colors are HEX F99D3E (orange) to 6CC396 (green).A Tree Equity Score is a metric that helps cities assess how well they are delivering equitable tree canopy cover to all residents. The score combines measures of tree canopy cover need and priority for trees in urban neighborhoods (defined as Census Block Groups). It is derived from tree canopy cover, climate, demographic and socioeconomic data. Geographies represent selected Census blockgroups for Caldwell, Hays, Travis, and Williamson counties. They cover the Census "urbanized area" for Austin and might not represent the full City of Austin jurisdiction.The score is calculated at the neighborhood (block group) level.Methodology (For more information about methodology, visit https://treeequityscore.org/methodology/ )Step 1: A Neighborhood GoalDensity Adjusted Canopy TargetThe canopy target – which is meant to be equitable, aspirational and achievable – requires the following data:Tree canopy cover. High resolution tree canopy where available, the National Land Cover Database where it is not.Census American Community Survey (ACS) 2018 5-year Block Group population estimatesCensus ACS 2018 5-year city and block group Median Income estimatesTo identify a baseline canopy target, we use generalized natural biome baseline targets selected in conjunction with the USDA Forest Service. We select the baseline target based on the location of the municipality.Forest: 40%Grassland: 20%Desert: 15%This target is then adjusted based on population density to estimate a neighborhood goal. Based on research completed by The Nature Conservancy, adjustments are made using the following table:Adjusting for population density makes for more achievable targets, while recognizing differences in plantable areas suitable for tree canopy. Note: Neighborhood goals are capped at 150% of the natural biome baseline target.The formula for each neighborhood goal, GOAL, is as follows:GOAL = Baseline target * Density adjustment factorStep 2: The Canopy GapThe neighborhood canopy gap, GAP, is calculated by subtracting the existing neighborhood canopy from the density adjusted target, that is: GAP = GOAL – EC, where EC is % existing canopy for that neighborhoodThe canopy Gap is then normalized to a score from 0-100.GAPScore = 100 * GAP / GAPmax , where:GAPmax is the maximum GAP value citywide for that indicator; andNotes: If the GAP is negative (i.e. Existing canopy is greater than the neighborhood goal), it is adjusted to 0 before normalizing to create the gap score. Also, if Gapmax = 0, then GapScore is set to 0 as well.Step 3: The Priority IndexThe Priority Index is developed to help prioritize the need for planting to achieve Tree Equity. The priority index includes the following equally-weighted characteristics:Income: Percentage of population below 200% of povertyEmployment: Unemployment rateRace: Percentage of people who are not white non-HispanicAge: Ratio of seniors and children to working-age adultsClimate: Urban Heat Island severityHealth: Prevalence of poor mental, physical, respiratory, and cardiac health (composite index)These measures are normalized and combined to create a simple priority index from 0 to 1, where 1 indicates a greater amount of inequity. The indices, N, are calculated as follows:Ni = (xi - xi,min ) / (xi,max - xi,min) , where, for each indicator, Ni,xi is the value for that neighborhood for that indicator, i;xi,max is the maximum value citywide for that indicator, i; andxi,min is the minimum value citywide for that indicator, i.The Priority index, E, is then calculated as follows: E = (N1 + N2 + N3 + N4 + N5 + N6) / 6 , where Ni refers to each indicator value (income, employment, race, age, or climate)Step 4: Tree Equity ScoreTree Equity Score, TES, is calculated by multiplying the Baseline Gap Score by the Priority Index, simply:TES = 100 (1 - GAPScore E)A lower Tree Equity Score indicates a greater priority for closing the tree canopy gap.Tree equity scores of 100 indicate tree equity has been achieved.Data Dictionarygeoid: the blockgroup idtotal_pop: the total population of the block groupstate: the state the blockgroup is incounty: the county the blockgroup is inpctpov: the percent of people in poverty inside the blockgrouppctpoc: the percent of people of color inside the block groupunemplrate: the unemployment rate inside of the block groupmedhhinc: the median household income of the block groupdep_ratio: the dependency ratio (childrens + seniors / 18-64 adults)child_perc: the percent of children inside of the blockgroupseniorperc: the percent of seniors inside of the blockgrouparea: the area of the blockgroup in square kilometerssource: the source of the tree canopy of the block groupavg_temp: the average temperature of the blockgroup on a hot summer's dayua_name: the urbanized area the block group is located insideincorpname: the incorporated place the block group is located insidecongressio: the congressional district of the block groupbgpopdense: the density of the blockgroup (total population over area)popadjust: the population adjustment factor (based on the population density)biome: the biome of the blockgroupbaselinecanopy: baseline tree canopy target generalized to natural biome (percent)treecanopy: the tree canopy percentage of the blockgroup (set to negative 1 if the source is 'ED')tc_gap: the tree canopy gap of the block group (goal minus canopy)tc_goal: the tree canopy goal of the block group (set to negative 1 if the source is 'ED')phys_hlth: the self reported physical health challenges of the people in the block group (a percentage)ment_hlth: the self reported mental health challenges of people in the block group (a percentage)asthma: the self reported asthma challenges of people in the block group (a percentage)core_m: the self reported male coronary heart challenges of people in the block group (a percentage)core_w: the self reported female coronary heart challenges of people in the block group (a percentage)core_norm: the normalized total coronary challenges of people in the block grouphealthnorm: the normalized health index of the block grouppriority: the priority index of the block grouptes: the tree equity score of the block grouptesctyscor: the tree equity score of the incorporated place/municipality of the block group

The study aimed to analyze factors associated with the availability of specialized dental services in Brazilian municipalities. This was an ecological study with a sample of 776 municipalities that participated in the first cycle of the Program to Improve Access and Quality of Dental Specialization Centers (PMAQ-CEO, in Portuguese) survey held in 2014. The study’s dependent variables consisted of a coefficient created with variables on the number of professionals and the workweek of dentists in the minimum set of specialties, per 10,000 inhabitants. Exploratory factor analysis was performed to create a score for the municipalities’ performance with the availability of specialized dental services. Factors associated with the municipalities’ performance were assessed with Pearson’s chi-square test, with the following municipal indicators as independent variables, categorized in tertiles: per capita income, Municipal Human Development Index (HDI-M), resident population, total health spending per inhabitant, and Oral Health Teams per 10,000 inhabitants. Higher performance with the availability of specialized oral health services was associated with municipalities having smaller populations (67.3%; CI: 61.6-73.0; p < 0.001), lower HDI-M (41.9%; CI: 35.8-48.0; p < 0.001), lower per capita income (41.2%; CI: 35.2-47.3; p < 0.001), and higher mean number of oral health teams per 10,000 inhabitants (50.6%; CI: 46.0-58.4; p < 0.001). The results show positive impacts from the implementation of the National Oral Health Policy in Brazil, meeting the goal of expanding the supply of secondary care services according to the principle of equity in care.

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.