In August of 2015 a committee was established between multiple Government of Ontario Ministries (MNRF, MTO, OMAFRA, MOECC, CSC, MEDEI/MRI, MCSS) to investigate and provide recommendations on how to best design maps so they are accessible to as wide an audience as possible. To achieve this, it was critical to consider the challenges that persons with disabilities could have when interacting with and interpreting maps.

The document focuses on the following considerations for accessible map design:

Contrast Colour Style and Patterns Font Selection Annotation and Labelling Simplicity and Consistency Alternative Formats and Descriptions

The document was endorsed in 2016 by the GIS in the OPS Director Manager Working Group (DMWG).

Although there are new and emerging technologies for creating maps for users with disabilities, the majority of this document is intended to assist designers creating map content for a range of users who have full sight to those with moderately low vision who do not use assistive technology.

The document does not address the technology or medium used to generate or publish the final map product, or the accessibility concerns that arise out of any technology, such as those outlined in Web Content Accessibility Guidelines published by the World Wide Web Consortium for content published on the web.

The concepts described within the document are applicable to map design, regardless of how a map is created, produced, or delivered. Consult with your Ministry's Accessibility Coordinator for assistance in these and other areas of accessibility considerations.

Additional Documentation

Map Design Considerations for Accessibility (Word)

Status Completed: Production of the data has been completed

Maintenance and Update Frequency As needed: Data is updated as deemed necessary

Contact Land Information Ontario, lio@ontario.ca

A global analysis of accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time.To model the time required for individuals to reach their most accessible city, we first quantified the speed at which humans move through the landscape. The principle underlying this work was that all areas on Earth, represented as pixels within a 2D grid, had a cost (that is, time) associated with moving through them that we quantified as a movement speed within a cost or ‘friction’ surface. We then applied a least-cost-path algorithm to the friction surface in relation to a set of high-density urban points. The algorithm calculated pixel-level travel times for the optimal path between each pixel and its nearest city (that is, with the shortest journey time). From this work we ultimately produced two products: (a) an accessibility map showing travel time to urban centres, as cities are proxies for access to many goods and services that affect human wellbeing; and (b) a friction surface that underpins the accessibility map and enables the creation of custom accessibility maps from other point datasets of interest. The map products are in GeoTIFF format in EPSG:4326 (WGS84) project with a spatial resolution of 30 arcsecs. The accessibility map pixel values represent travel time in minutes. The friction surface map pixels represent the time, in minutes required to travel one metre. This DANS data record contains these two map products. Issued: 2018-01-10

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as a contiguous area with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants.This dataset is described in "Mapping inequality in accessibility: a global assessment of travel time to cities in 2015" (Weiss et al 2018; doi:10.1038/nature25181)This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travelbased on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) wereused in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city.

This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

All-Island Accessibility. Published by All-Island Research Observatory. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).The All-Island Accessibility Mapping Tool provides an analysis of access to settlements and key service infrastructure such as transport, education and health facilities across Ireland. Accessibility score are available for Towns, Health Facilities, Education Services, Retail Outlets and Transport Services. Accessibility scores to a range of services have been developed for every residential address point on the island (approx 2.7m) based on average drive-time speeds (average speed on NAVTEC road network plus 10% urban area congestion charge). For the purposes of the mapping tool the accessibility scores have been averaged at the most detailed spatial statistical unit available – Small Areas for the Republic of Ireland (approx 18k) and Output Areas for Northern Ireland (approx 5k)...

This web map shows the accessible pathways and features throughout the MIT campus. Accessibility Map PUBLIC is for external purposes.

Understanding the location of potential barriers such as stairs or steep hills and access features such as accessible toilets or taxi ranks provides people with disability greater confidence to visit the city. More importantly, it lets people with disability participate in numerous activities with more independence and dignity.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

The West Africa Coastal Vulnerability Mapping: Subset of JRC Map of Accessibility data set is a 30 arc-second raster of travel time to major cities in West Africa within 200 kilometers of the coast. Extensive literature shows that road networks and market accessibility play an important role in development and access to health care and other social services. Greater spatial isolation is assumed to produce higher vulnerability to climate stressors. Market accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake, and ocean) based travel. A team at the Joint Research Centre (JRC) in Ispra, Italy, created a global raster of accessibility using a cost-distance algorithm which computes the "cost" (in Units of time) of traveling between two locations on a regular raster grid. The raster grid cells contain values which represent the cost required to travel across them, hence this raster grid is often termed a friction-surface. The friction-surface contains information on the transport network, and environmental and political factors that affect travel times between locations. Transport networks can include road and rail networks, navigable rivers, and shipping lanes. The locations of interest are termed targets, and in the case of this data set, the targets are cities with a population of 50,000 or greater in the year 2000.

The purpose of this dataset is to allow for an accessibility analysis and mapping project to be conducted on the University of British Columbia (UBC), Vancouver Campus.

In a university setting, campus navigation is a foundational part of seizing opportunities, networking with other scholars, and having an all-around positive student experience. Yet, inaccessible features of an urban landscape (like stairs, rough terrain, or steep slopes) often leave mobility-limited individuals at a great disadvantage or cut off from certain opportunities. The accessibility analysis and mapping project (AAMP) is geared to try and answer what and where barriers to wheelchair accessibility exist on the UBC campus. To do this, (1) two cost paths for accessible and inaccessible terrain were calculated and compared to identify barriers to accessibility, (2) a least-cost path analysis is conducted to test if wheelchair routes are statistically longer than walking routes, and (3) a wayfinding map geared toward wheelchair users is created with the intention of increasing campus navigation equity and as a visualization for urban planners to see where campus accessibility improvements need to be made. It was discovered that 10% of the total walkable path area was some sort of accessibility barrier to wheelchair users. Through a visual investigation and comparison with the previous literature, three main types of barriers were identified on the campus. Next, an online map was created of the study site which highlighted accessibility barriers and difficult terrain. Finally, the paper ends with a discussion around why certain types of accessibility barriers exist on the campus and what urban planners can do to fix these and create more equitable wayfinding experiences across urban landscapes.

This viewer contains the accessible pathways and features throughout the MIT campus.

The presented maps show different accessibility measures in the Brazilian Legal Amazon. They contain raster maps with travel times (in seconds) to major slaughterhouses, large grain silos, urban areas, state capitals and road pavement areas in 2004. Another dataset for 2012 is provided as a separate publication here. All maps are produced for rainy season accessibility and dry season accessibility. Furthermore, a friction map is provided that was utilized to produce the maps. An article with extensive information on how the maps were produced has been submitted for publication as Schielein, J.,Frey, G., Miranda, J., Souza, R. Börner, J. and J. Henderson (forthcoming). The Role of Accessibility For Land-use and Land-cover Change in the Brazilian Amazon. The article will be linked here as soon as it is published.

RNA structure plays a central role in post-transcriptional gene regulation, modulating RNA stability, translation, and interactions with RNA-binding proteins (RBPs). However, capturing RNA conformations at scale remains challenging. Here, we introduce DMS-TRAM-seq (Dimethyl Sulfate–Transcriptome-wide RNA Accessibility Mapping by sequencing), which probes RNA structure across nearly the entire transcriptome. Using DMS-TRAM-seq, we generated secondary structure predictions for over 9,000 human transcripts, including hundreds of non-coding RNAs, and identified more than 700 previously unannotated, high-confidence structured elements. Importantly, the enhanced coverage provided by DMS-TRAM-seq enabled comparative analyses, revealing RNAs that undergo structural rearrangements in response to cellular perturbations. Integration with RBP motifs and ribosome profiling uncovered altered RNA–RBP interactions during oxidative stress and showed that translation inhibition broadly drives RNAs toward their thermodynamically favored conformations. DMS-TRAM-seq enables interrogation of the RNA structurome and its plasticity at an unprecedented scale, opening new directions for elucidating the structural basis of RNA regulation.

ABSTRACT Nowadays, terms such as ‘media accessibility’ and ‘accessible audiovisual translation’ are becoming more common both in academia and in society in general. This popularization is due to a significant increase in national and international legislation regarding inclusion of people with disabilities in all social spheres and, in particular, regarding to cultural aspects (NAVES et al., 2016). With such a remarkable increase in legal, professional and academic production, Brazil presents a solid and diverse portfolio that stands out even when compared to the production of developed nations on the same topic. However, because of language barriers, this relevant content does not enjoy the deserved recognition and praise among international researchers. In order to try to change that scenario and help promote this content, the Media Accessibility Platform (MAP) can be a powerful tool as its main goal is to serve as a centralized database. The idea is to gather and organize in a free access and digital interface all different contents related to media accessibility, such as news reports, training courses, academic publication and legislation (GRECO et al., 2016). Unfortunately, so far, very little information regarding the Brazilian production on the topic has been uploaded to the platform. Therefore, our aim here is not only to promote MAP as a key tool for researchers, but also to highlight the importance of uploading as much Brazilian data as possible in order to bring our national content to the spotlight it deserves. Furthermore, we also aim to present the research project “MAPping audiovisual accessibility in Brazil and uploading data to the global Media Accessibility Platform (MAP)” currently been carried out by the research group “Audiovisual Translation: breaking barriers of language and accessibility” at Methodist University of Piracicaba.

Accessibility is defined as the travel time to a location of interest using land (road/off road) or water (navigable river, lake and ocean) based travel. This accessibility is computed using a cost-distance algorithm which computes the “cost” of traveling between two locations on a regular raster grid. Generally this cost is measured in units of time.The input GIS data and a description of the underlying model that were developed by Andrew Nelson in the GEM (Global Environment Monitoring) unit in collaboration with the World Bank’s Development Research Group between October 2007 and May 2008. The pixel values representing minutes of travel time. Available dataset: Joint Research Centre - Land Resource Management Unit

This EnviroAtlas web service includes maps that illustrate factors affecting transit accessibility, and indicators of accessibility. Accessibility measures how easily people can reach destinations such as their workplaces and can be measured in terms of both time and distance. It is affected by factors such as the proximity of housing to jobs, transit stops, stores, and services; the availability of various transit modes; and land use patterns. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Many government websites and mobile content are inaccessible for people with vision, hearing, cognitive, and motor impairments. The COVID-19 pandemic highlighted these disparities when health authority website information, critical in providing resources for curbing the spread of the virus, remained inaccessible for numerous disabled populations. The Web Content Accessibility Guidelines provide comparatively universally accepted guidelines for website accessibility. We utilized these parameters to examine the number of countries with or without accessible health authority websites. The resulting data indicate a dearth of countries with websites accessible for persons with disabilities. Methods of information dissemination must take into consideration individuals with disabilities, particularly in times of global health crises.

Chronic lymphocytic leukemia (CLL) is characterized by substantial clinical heterogeneity, despite relatively few genetic alterations. To provide a basis for studying epigenome deregulation in CLL, we established genome-wide chromatin accessibility maps for 88 CLL samples from 55 patients using the ATAC-seq assay, and we also performed ChIPmentation and RNA-seq profiling for ten representative samples. Based on the resulting dataset, we devised and applied a bioinformatic method that links chromatin profiles to clinical annotations. Our analysis identified sample-specific variation on top of a shared core of CLL regulatory regions. IGHV mutation status – which distinguishes the two major subtypes of CLL – was accurately predicted by the chromatin profiles, and gene regulatory networks inferred for IGHV-mutated vs. IGHV-unmutated samples identified characteristic differences between these two disease subtypes. In summary, we found widespread heterogeneity in the CLL chromatin landscape, established a community resource for studying epigenome deregulation in leukemia, and demonstrated the feasibility of chromatin accessibility mapping in cancer cohorts and clinical research. Genome-wide profiling of chromatin states and gene expression levels in 88 CLL samples from 55 individuals gave rise to 88 ATAC-seq profiles, 40 ChIPmentation profiles (10 samples, each with 3 different antibodies and matched immunoglobulin control), and 10 RNA-seq profiles.Raw sequence data has been deposited at the EBI's European Genome-phenome Archive (EGA) under the accession number EGAS00001001821 (controlled access to protect patient privacy).

This dataset contains vectorized indoor map data for accessible indoor navigation. It is highly accurate georeferenced indoor data from a steadily growing number of buildings. In most cases, at least the following information is included: Walls - Doors - Stairs, elevators, escalators - Rooms with name and room type and, if necessary, keywords and information on accessibility. After the account has been released, the data can be queried directly via a graphql interface (see usage instructions). In addition, directly usable SDKs for Android and iOS applications are available after licensing. This makes it particularly easy to make the data usable in mobile applications and to use it directly in connection with the Fraunhofer system everGuide, e.g. for barrier-free turn-by-turn navigation.

Here we share the access map data typology and data dictionary to support access mapping projects. Keywords: accessibility, disability, navigation, mapping, university, accessible way finding, inclusive cartography