This map uses dot density patterns to indicate which population is larger in each area: urban (green) or rural (blue). Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico.The U.S. Census designates each census block as part of an urban area or as rural. Larger geographies in this map such as block group, tract, county and state can therefore have a mix of urban and rural population. This map illustrates the 100% urban areas with all green dots, and 100% rural areas in dark blue dots. Areas with mixed urban/rural population have a proportional mix of green and blue dots to give a visual indication of where change may be happening. From the Census:"The Census Bureau’s urban-rural classification is a delineation of geographic areas, identifying both individual urban areas and the rural area of the nation. The Census Bureau’s urban areas represent densely developed territory, and encompass residential, commercial, and other non-residential urban land uses. The Census Bureau delineates urban areas after each decennial census by applying specified criteria to decennial census and other data. Rural encompasses all population, housing, and territory not included within an urban area.For the 2020 Census, an urban area will comprise a densely settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or have a population of at least 5,000." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

To examine potential influence of tributaries on riparian habitat complexity along ~216 km of the Colorado River in Utah and ~300km of the Dolores River in Colorado and Utah, we first classified fluvial features and land cover of the bottomland on remotely sensed imagery. We then examined riparian and geomorphic patterns within the near channel zone with variably-sized spatial units. We used supervised image classification to create a 2-m resolution map of the primary land cover types within bottomlands of the Colorado and Dolores rivers, including two anthropogenic classes, four vegetation classes, bare ground, water and shadow. We selected these cover classes as major vegetation and land cover types that could be discerned from imagery. Our minimum mapping unit was 16m2. We were unable to map channel areas with flowing or standing water using supervised image classification, so we hand digitized channels based on a visual inspection of 2-m resolution imagery. We classified 6 channel classes based on their geomorphic characteristics and location within the river network (i.e., tributary vs. primary channel) or relation to the primary channel (e.g., split flow channels and secondary channels) and converted these to a 2-m resolution image (adapted from Moore et al 2012). We then combined land cover and channel classes to produce a single map representing both cover types along the Colorado and Dolores rivers. Our classification was based on 2-m resolution, multi-spectral (RGB NIR) aerial photographs for September 2013 and 2014 from the USDA National Agriculture Imagery Program (NAIP; http//www.fsa.usda.gov). We identified tributary junctions using the National Hydrography Dataset Plus Version 2 (NHDPlus V2) using the medium resolution (1:100,000 scale) National Hydrography Dataset (NHD) (http://nhd.usgs.gov/). To more accurately locate tributary junctions, we extracted flowlines corresponding to tributaries and converted each flowline to a point located at the terminus proximal to the channel centerline. We manually corrected tributary junction point locations with the NAIP images. We defined the near channel zone as within 20 meters of the edge of the Dolores low flow channel and within 100 meters of the edge of the Colorado low flow channel. These distances represented the average widths of the low flow channel for the two rivers. We assumed that habitat conditions closer to the channel would be more strongly influenced by fluvial processes and less strongly influenced by land management (e.g., farming, road development). We created spatial units for analysis within the near channel zone with Thiessen polygons - a polygon containing a point and defining an area closest to the point relative to all other systematically placed points (Fortin and Dale 2005). Beginning at the upstream study site boundary for each river, we placed regularly spaced points at three intervals: 10-, 25-, and 100-m to capture patterns for different sized spatial units around tributary junctions. For each point, we created a Thiessen polygon. Our use of Thiessen polygons as spatial units followed the example of other researchers (Alber and Piegay 2011). This data release includes shapefiles and associated metadata for: land and channel cover types along both rivers; tributary junction locations along both rivers; and the 10-, 25-, and 100-m Thiessen polygons along both rivers. Alber A., and Piégay H., 2011, Spatial disaggregation and aggregation procedures for characterizing fluvial features at the network-scale: application to the Rhône basin (France): Geomorphology, v. 125, p. 343-360. Fortin M.J., and Dale M.T., 2005, Spatial analysis: a guide for ecologists: Cambridge, Cambridge University Press, 365 p. Moore K., Jones K., Dambacher J., and Stein C., 2012, Aquatic inventories project methods for stream habitat surveys: Corvallis, OR, Conservation and recovery program, Oregon Department of Fish and Wildlife, 74 p.

In the era of smartphones, route-planning and navigation is supported by freely and globally available web mapping services, such as OpenStreetMap or Google Maps. These services provide digital maps, as well as route planning functions that visually highlight the suggested route in the map. Additionally, such digital maps contain landmark pictograms, i.e. representations of salient objects in the environment. These landmark representations are, amongst other reference points, relevant for orientation, route memory, and the formation of a cognitive map of the environment. The amount of visible landmarks in maps used for navigation and route planning depends on the width of the displayed margin areas around the route. The amount of further reference points is based on the visual complexity of the map. This raises the question how factors like the distance of landmark representations to the route and visual map complexity determine the relevance of specific landmarks for memorizing a route. In order to answer this question, two experiments that investigated the relation between eye fixation patterns on landmark representations, landmark positions, route memory and visual map complexity were carried out. The results indicate that the attentional processing of landmark representations gradually decreases with an increasing distance to the route, decision points and potential decision points. Furthermore, this relation was found to be affected by the visual complexity of the map. In maps with low visual complexity, landmark representations further away from the route are fixated. However, route memory was not found to be affected by visual complexity of the map. We argue that map users might require a certain amount of reference points to form spatial relations as a foundation for a mental representation of space. As maps with low visual complexity offer less reference points, people need to scan a wider area. Therefore, visual complexity of the area displayed in a map should be considered in navigation-oriented map design by increasing displayed margins around the route in maps with a low visual complexity. In order to verify our assumption that the amount of reference points not only affects visual attention processes, but also the formation of a mental representation of space, additional research is required.

The AHF system has been deployed in a series of survey campaigns to collect over 60,000 points covering Everglades National Park, Loxahatchee National Wildlife Refuge, Water Conservation Areas 2 and 3, portions of Big Cypress National Preserve, as well as areas along the Lake Okeechobee littoral zone. Since the AHF System is able to penetrate Everglades vegetation and water cover, it has provided an unprecedented regional view of Everglades topographic gradients and sub-water surface structure. These data are now being used to simulate Everglades water flow with higher resolution and greater accuracy, to estimate water depths in real-time for field study planning, and as input for habitat models used to forecast the effects of water level changes on various important species. The elevation data collected through this project also formed the basic input to generate a regional topographic surface that is the basis for the Everglades Depth Estimation Network (EDEN). These high accuracy elevation data are made available to anyone through the South Florida Information Access website (http://sofia.usgs.gov) data exchange pages.

MAP Activity Accomplishment The USGS Airborne Height Finder (AHF) System was used to perform topographic surveys in Water Conservation Area 3A within the extents of the Lone Palm Head and North of Lone Palm Head 7.5-minute topographic map quadrangles as specified in the MAP/COE Interagency Agreement. The AHF system has been used throughout South Florida for elevation data collection because traditional surveying methods are too difficult, too costly, or simply impossible to use in the harsh wetland environment and broadly inaccessible terrain of the Florida Everglades. This is especially true considering the shear size of the hydrodynamic and biological modeling domains. The AHF is a helicopter-based instrument that uses a GPS receiver, a computer, and a mechanized plumb bob to make measurements. These data were post processed to the reference stations that are part of the AHF geodetic control network. For reasons of accuracy, these reference stations are located no more then 15 kilometers from the helicopter during AHF operations. The GPS data were post processed using Ashtech’s PNAV On The Fly (OTF) software to obtain the trajectory of the AHF platform. These results are then processed through an in-house software package that separates the actual survey points and results from the trajectory. The points are manually checked to ensure data accuracy and completeness. Digital elevation models (DEMs) were then generated from the elevation point data. Existing elevation data derived from LiDAR data for this area were replaced with AHF derived DEMs for reasons of vertical accuracy. The DEMs have been posted on the South Florida Information Access (SOFIA) website: http://sofia.usgs.gov/exchange/desmond/desmondelev.html.

Annotations used in the TC Climatology for St. Louis prevailing track scenarios to assist in visualizing differences and key features in the patterns that steer TCs closer to St. Louis.

Work in progress: data might be changed

The data set contains the locations of public roadside parking spaces in the northeastern part of Hanover Linden-Nord. As a sample data set, it explicitly does not provide a complete, accurate or correct representation of the conditions! It was collected and processed as part of the 5GAPS research project on September 22nd and October 6th 2022 as a basis for further analysis and in particular as input for simulation studies.

Vehicle Detections

Based on the mapping methodology of Bock et al. (2015) and processing of Leichter et al. (2021), the utilization was determined using vehicle detections in segmented 3D point clouds. The corresponding point clouds were collected by driving over the area on two half-days using a LiDAR mobile mapping system, resulting in several hours between observations. Accordingly, these are only a few sample observations. The trips are made in such a way that combined they cover a synthetic day from about 8-20 clock.

The collected point clouds were georeferenced, processed, and automatically segmented semantically (see Leichter et al., 2021). To automatically extract cars, those points with car labels were clustered by observation epoch and bounding boxes were estimated for the clusters as a representation of car instances. The boxes serve both to filter out unrealistically small and large objects, and to rudimentarily complete the vehicle footprint that may not be fully captured from all sides.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/807618b6-5c38-4456-88a1-cb47500081ff/download/detection_map.png" alt="Overview map of detected vehicles" title="Overview map of detected vehicles"> Figure 1: Overview map of detected vehicles

Parking Areas

The public parking areas were digitized manually using aerial images and the detected vehicles in order to exclude irregular parking spaces as far as possible. They were also tagged as to whether they were aligned parallel to the road and assigned to a use at the time of recording, as some are used for construction sites or outdoor catering, for example. Depending on the intended use, they can be filtered individually.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/16b14c61-d1d6-4eda-891d-176bdd787bf5/download/parking_area_example.png" alt="Example parking area occupation pattern" title="Visualization of example parking areas on top of an aerial image [by LGLN]"> Figure 2: Visualization of example parking areas on top of an aerial image [by LGLN]

Parking Occupancy

For modelling the parking occupancy, single slots are sampled as center points every 5 m from the parking areas. In this way, they can be integrated into a street/routing graph, for example, as prepared in Wage et al. (2023). Own representations can be generated from the parking area and vehicle detections. Those parking points were intersected with the vehicle boxes to identify occupancy at the respective epochs.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/ca0b97c8-2542-479e-83d7-74adb2fc47c0/download/datenpub-bays.png" alt="Overview map of parking slots' average load" title="Overview map of parking slots' average load"> Figure 3: Overview map of average parking lot load

However, unoccupied spaces cannot be determined quite as trivially the other way around, since no detected vehicle can result just as from no measurement/observation. Therefore, a parking space is only recorded as unoccupied if a vehicle was detected at the same time in the neighborhood on the same parking lane and therefore it can be assumed that there is a measurement.

To close temporal gaps, interpolations were made by hour for each parking slot, assuming that between two consecutive observations with an occupancy the space was also occupied in between - or if both times free also free in between. If there was a change, this is indicated by a proportional value. To close spatial gaps, unobserved spaces in the area are drawn randomly from the ten closest occupation patterns around.

This results in an exemplary occupancy pattern of a synthetic day. Depending on the application, the value could be interpreted as occupancy probability or occupancy share.

https://data.uni-hannover.de/dataset/0945cd36-6797-44ac-a6bd-b7311f0f96bc/resource/184a1f75-79ab-4d0e-bb1b-8ed170678280/download/occupation_example.png" alt="Example parking area occupation pattern" title="Example parking area occupation pattern"> Figure 4: Example parking area occupation pattern

References

• F. Bock, D. Eggert and M. Sester (2015): On-street Parking Statistics Using LiDAR Mobile Mapping, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain, 2015, pp. 2812-2818. https://doi.org/10.1109/ITSC.2015.452
• A. Leichter, U. Feuerhake, and M. Sester (2021): Determination of Parking Space and its Concurrent Usage Over Time Using Semantically Segmented Mobile Mapping Data, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 185–192. https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-185-2021
• O. Wage, M. Heumann, and L. Bienzeisler (2023): Modeling and Calibration of Last-Mile Logistics to Study Smart-City Dynamic Space Management Scenarios. In 1st ACM SIGSPATIAL International Workshop on Sustainable Mobility (SuMob ’23), November 13, 2023, Hamburg, Germany. ACM, New York, NY, USA, 4 pages. https://doi.org/10.1145/3615899.3627930

This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This map shows density surfaces derived from the 2010 US Census block points.This data shows % of people who identified themselves as 'single race' and 'Black'The block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

This data set summarized biological and environmental sampling data from Reef Visual Census (RVC) surveys in southern Florida in conjunction with remote-sensed, high-resolution mapping data to take significant strides in moving from qualitative to quantitative habitat characterization of the RVC coral reef sampling frame. The data set contains two GIS shape files, one for the Dry Tortugas region and one for the Florida Keys, of survey sampling grids with habitat-depths quantitatively characterized to a 50 x 50 m resolution. Each sampling grid has region code, grid number, average depth (m), habitat code, zone code indicating onshore-offshore, MPA-code indicating whether inside or outside a protected area, depth strata code, rugosity strata code, fish strata code, and coral strata code. There is a dictionary file which describes the details of each habitat code categories. The refined sampling grid will have significant improvements to the accuracy, precision, and cost-effectiveness of RVC surveys in the Florida Keys and Tortugas regions. The study findings suggest some clear mapping priorities for fully characterizing the RVC sampling grid for the Florida Keys and Tortugas regions.

Current seagrass distribution and species composition in Florida Bay reflect decades of human impact on the hydrology of the Everglades and Florida Bay. Consequently, one widely accepted goal of the Comprehensive Everglades Restoration Plan (CERP) is the reestablishment of more natural seagrass distribution patterns in Florida Bay. Anecdotal observations indicate that, as a consequence of lower, and fluctuating salinity in the past, seagrass distribution was less continuous along the northern portion of Florida Bay and turtle grass was much less abundant. Seagrass scientists and water managers anticipate that restoration of more normal water flow patterns through the Everglades will lower salinities in northern Florida Bay causing shifts in seagrass spatial patterns and species composition. A map of the current location and patterns of submersed aquatic vegetation (SAV) is needed as a baseline before water flow patterns are altered as planned in the CERP. The South Florida Water Management District (SFWMD) and Florida Fish and Wildlife Conservation Commission (FWC) developed a plan to begin mapping the SAV in Florida Bay in 2003 and perform landscape metrics analyses on several study areas. This two-pronged project will result in products that will be useful to a variety of researchers and managers as a view of the current status of SAV as well as a tool in assessing temporal changes in Florida Bay SAV. The overall goals of this project are to 1) assess current seagrass distribution, abundance, and spatial patterns in Florida Bay and 2) provide current benchmarks against which the effects of hydrologic restoration activities can be measured. To those ends, this project has two principal objectives: 1. Construct a map of seagrass distribution from 1:24000 scale, natural color aerial photography flown in 2003; and 2. Develop and test a series of spatial metrics for use in measuring historic and future patterns in seagrass distribution and patchiness.

Values were aggregated across participants in order to create one value per landmark pictogram.

This data publication includes data used in "Spatial aspects of structural complexity in Sitka spruce – western hemlock forests, including evaluation of a new canopy gap delineation method" by Schneider and Larson (2017). These data represent trees and plots from a study led by Vernon LaBau for his M.S. Thesis at Oregon State University, which he completed in 1967. Data were collected in 1964 on ten, 1.42 hectare plots (laid out as 5 by 7 chains). Data include tree location within subplots, tree species, diameter at breast height, and height in logs.Data were originally collected to assess the utility of clustered point (prism) sampling to quantify old-growth forest structure in the unique rainforests of southeast Alaska. LaBau wrote: "This study was a test of eight basal area factors and five point sampling cluster patterns in a computer oriented sampling study of coastal Alaska old-growth spruce-hemlock stands. It was an attempt to learn which basal area factor and which type of point sample cluster pattern should be used in such stands. A test of the effect of stand density on point sampling was also made." This data continues to be valuable for research and has been most recently used to assess spatial pattern of old-growth forests in southeast Alaska.These data were originally published on 04/01/2020. Minor metadata updates were made on 07/21/2022.

Abstract INTRODUCTION The recent emergence and rapid spread of Zika and Chikungunya fevers in Brazil, occurring simultaneously to a Dengue fever epidemic, together represent major challenges to public health authorities. This study aimed to identify and compare the 2015-2016 spatial diffusion pattern of Zika, Chikungunya, and Dengue epidemics in Salvador-Bahia. METHODS We used two study designs comprising a cross-sectional-to-point pattern and an ecological analysis of lattice data. Residential addresses involving notified cases were geocoded. We used four spatial diffusion analysis techniques: (i) visual inspection of the sequential kernel and choropleth map, (ii) spatial correlogram analysis, (iii) spatial local autocorrelation (LISA) changes analysis and, (iv) nearest neighbor index (NNI) modeling. RESULTS Kernel and choropleth maps indicated that arboviruses spread to neighboring areas near the first reported cases and occupied these new areas, suggesting a diffusion expansion pattern. A greater case density occurred in central and western areas. In 2015 and 2016, the NNI best-fit model had an S-curve compatible with an expansion pattern for Zika (R2 = 0.94; 0.95), Chikungunya (R2 = 0.99; 0.98) and Dengue (R2 = 0.93; 0.99) epidemics, respectively. Spatial correlograms indicated a decline in spatial lag autocorrelations for the three diseases (expansion pattern). Significant LISA changes suggested different diffusion patterns, although a small number of changes were detected. CONCLUSIONS These findings indicate diffusion expansion, a unique spatial diffusion pattern of Zika, Chikungunya, and Dengue epidemics in Salvador-Bahia, namely. Knowing how and where arboviruses spread in Salvador-Bahia can help improve subsequent specific epidemic control interventions.

This mapping completed the Larsemann Hills photogrammetric mapping project. The project was commenced on 14 December 2001 and completed in April 2003. It includes the integration of newly mapped data with dataset gis136. (Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000)

A report on the project is available at the url given below.

The World Stress Map (WSM) is the global compilation of information on the present-day stress field in the Earth's crust. The current WSM database release 2016 (Heidbach et al., 2016) has 42,870 data records, but the data are unevenly distributed and clustered.To analyse the wave-length of the crustal stress pattern of the orientation of maximum horizontal stress Shmax, we use so-called smoothed stress maps that show the mean SHmax orientation on regular grids. The mean SHmax orientation is estimated with the Matlab® script stress2grid (Ziegler and Heidbach, 2017) which is based on the statistics of bi-polar data. The script provides two different approaches to calculate the mean SHmax orientation on regular grids.The first is using a constant search radius around the grid point and computes the mean SHmax orientation if sufficient data records are within the given fixed search radius. This can result in mean SHmax orientations with a high standard deviation of the individual mean SHmax orientation and it may hide local perturbations. Thus, the mean SHmax orientation is not necessarily reliable for a local stress field analysis.The second approach is using variable search radii and determines the search radius for which the standard deviation of the mean SHmax orientation is below a user-defined threshold. This approach delivers the mean SHmax orientations with a user-defined degree of reliability. It resolves local stress perturbations and is not available in areas with no data or conflicting information that result in a large standard deviation.The search radius starts with 1000 km and is decreased in 100 km steps down to 100 km. Mean SHmax orientation is taken and plotted here for the largest search radius when the standard deviation of the mean SHmax orientation at the individual grid points is smaller than 25°. For the estimation of the mean Shmax we selected the following data: A-C quality data without PBE flag.Furthermore, only data records located on the same tectonic plate as the grid point is used to calculate the mean SHmax orientation. Minimum number of data records within the search radius is n = 5 and data records within a distance of d ≤ 200 km to the nearest plate boundary are not used. Plate boundaries are taken from the global model PB2002 from Bird (2003).Furthermore, a distance and data quality weight is applied; the distance threshold is set to 10% of the search radius. We provide the resulting smoothed stress data for four global grids (0.2°, 0.5°, 1°, and 2° grid spacing) using two fixed search radii (250 and 500 km) and the approach with variable search radii. Details on the format of the data files with the mean SHmax orientation are provided in the 2018-002_readme file.

How does growth encode form in developing organisms? Many different spatiotemporal growth profiles may sculpt tissues into the same target 3D shapes, but only specific growth patterns are observed in animal and plant development. In particular, growth profiles may differ in their degree of spatial variation and growth anisotropy, however, the criteria that distinguish observed patterns of growth from other possible alternatives are not understood. Here we exploit the mathematical formalism of quasiconformal transformations to formulate the problem of `growth pattern selection'' quantitatively in the context of 3D shape formation by growing 2D epithelial sheets. We propose that nature settles on growth patterns that are thesimplest' in a certain way. Specifically, we demonstrate that growth pattern selection can be formulated as an optimization problem and solved for the trajectories that minimize spatiotemporal variation in areal growth rates and deformation anisotropy. The result is a complete prediction for the growth of the surface, including not only a set of intermediate shapes, but also a prediction for cell displacement along those surfaces in the process of growth. Optimization of growth trajectories for both idealized surfaces and those observed in nature show that relative growth rates can be uniformized at the cost of introducing anisotropy. Minimizing the variation of programmed growth rates can therefore be viewed as a generic mechanism for growth pattern selection and may help to understand the prevalence of anisotropy in developmental programs. Methods Analysis of growing appendage in Parhyale hawaiensis

The recording of the transgenic Parhyale embryo with a construct for heat-inducible expression of a nuclear marker (H2B-mRFPruby) was generated using multi-view lightsheet fluorescence microscopy (LSFM) with 7.5 minute time intervals beginning 3 days after egg lay (AEL). More details regarding data acquisition and pre-processing can be found in [1]. Our analysis focused on a period of dramatic outgrowth in the T2 appendage from 95 − 109h AEL and utilized tissue cartography methods to generate coarse-grained flow patterns on cells on the growing limb [2, 3]. Down-sampled data volumes were effectively denoised using Ilastik [4] by training a classifier to distinguish tissue from background. The result of this step was a pixel probability map for each time point (with high values in tissue regions and low values in background regions). Segmented nuclei positions from [1] were then used to help distinguish the limb from surrounding tissue structures. Alpha shapes of the sparse nuclei locations were generated using MATLAB’s alphaShape function with a sufficiently high hole threshold to ensure that the resulting surfaces were water tight. The pixel probability maps were then multiplied by an exponentially decaying radial basis function of the distance to the alpha surface to suppress probability away from the actual limb. The original tracking from [1] was only logged every 45 minutes, so we linearly interpolated between these locations to take advantage of the full time resolution of the data set. The processed probability maps were then segmented using an active contour method [5]. The result was a binary level set indicating the location of all limb specific tissue. A point cloud approximating the mid-surface of the limb was obtained using a weighted locally optimal projection point cloud (WLOP) simplification algorithm [6]. In order to produce surface triangulations, we first estimated the boundary of the mid-surface point cloud using a custom modified version of the algorithm in [7]. Next, we found the point that was farthest from the boundary by calculating geodesic distances directly on the point cloud [8]. We then used the vector heat method [9] to compute the logarithmic map around this point. The logarithmic map is a local parameterization about a point, where for each point on the surface the magnitude of the log map gives the geodesic distance from the source, and the polar coordinate of the log map gives the direction at which a geodesic must leave the source to arrive at the point. This enabled us to embed the points in 2D, construct a Delaunay triangulation, and then lift the triangulation back into 3D. Triangulated time-dependent surfaces were then mapped conformally into the unit disk using a custom-implementation of the discrete Ricci flow [10]. The conformal degrees of freedom in the time-dependent parameterization were pinned by finding an optimal Möbius transformation that matched the neighborhood structure of nuclei locations at subsequent times without explicit reference to nuclei identity [11].

Once pulled back into the plane, an updated tracking for the nuclei was performed in 2D using a custom built MATLAB GUI enabling the reconstruction of nuclear lineages and cell tracks. The 3D displacement vectors between identified nuclei at subsequent times constituted a sparse set of surface velocities at isolated points. We once again employed the vector heat method to extend these velocities to the entire surface and then smooth them. These velocities were then used to compute the components of the growth tensor (i.e. the time derivative of the Lagrangian metric tensor) with respect to the instantaneous virtual isothermal parameterization of the surface. The infinitesimal change in deformation anisotropy γ from time t to t+1 could then be directly extracted from the growth tensor. We then computed the corresponding update to the 2D quasiconformal parameterization by feeding γ into a custom implementation of the Beltrami Holomorphic Flow algorithm [12]. The complete material flow was then assembled by iteratively propagating the surface mesh at the final time backwards along these quasiconformal mappings in the plane and pushing the resulting 2D parameterizations forward into 3D using the instantaneous conformal mappings and a natural neighbor interpolation scheme [13].

[1] C. Wolff, J.-Y. Tinevez, T. Pietzsch, E. Stamataki, B. Harich, L. Guignard, S. Preibisch, S. Shorte, P. J. Keller, P. Tomancak, and A. Pavlopoulos, "Multi-view light-sheet imaging and tracking with the MaMuT software reveals the cell lineage of a direct developing arthropod limb", eLife 7, e34410 (2018). [2] I. Heemskerk and S. J. Streichan, "Tissue cartography: compressing bio-image data by dimensional reduction", Nature Methods 12, 1139 (2015). [3] N. P. Mitchell and D. J. Cislo, "Tubular: tracking in toto deformations of dynamic tissues via constrained maps", Nature Methods 20, 1980 (2023). [4] S. Berg, D. Kutra, T. Kroeger, C. N. Straehle, B. X. Kausler, C. Haubold, M. Schiegg, J. Ales, T. Beier, M. Rudy, K. Eren, J. I. Cervantes, B. Xu, F. Beuttenmueller, A. Wolny, C. Zhang, U. Koethe, F. A. Hamprecht, and A. Kreshuk, "ilastik: interactive machine learning for (bio)image analysis", Nature Methods 16, 1226 (2019). [5] T. Chan and L. Vese, "Active contours without edges", IEEE Transactions on Image Processing 10, 266 (2001). [6] H. Huang, D. Li, H. Zhang, U. Ascher, and D. Cohen-Or, "Consolidation of unorganized point clouds for surface reconstruction", ACM Transactions on Graphics 28, 1 (2009). [7] C. Mineo, S. G. Pierce, and R. Summan, "Novel algorithms for 3d surface point cloud boundary detection and edge reconstruction", Journal of Computational Design and Engineering 6, 81 (2019). [8] K. Crane, C. Weischedel, and M. Wardetzky, "The heat method for distance computation", Commun. ACM 60, 90 (2017). [9] N. Sharp, Y. Soliman, and K. Crane, "The vector heat method", ACM Trans. Graph. 38 (2019). [10] W. Zeng and X. D. Gu, "Ricci Flow for Shape Analysis and Surface Registration", SpringerBriefs in Mathematics (Springer New York, New York, NY, 2013). [11] H. Le, T.-J. Chin, and D. Suter, "Conformal Surface Alignment with Optimal Möbius Search", in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2016-Decem (IEEE, 2016) pp. 2507–2516. [12] L. M. Lui, T. W. Wong, W. Zeng, X. Gu, P. M. Thompson, T. F. Chan, and S.-T. Yau, "Optimization of Surface Registrations Using Beltrami Holomorphic Flow", Journal of Scientific Computing 50, 557 (2012). [13] R. Sibson, "A brief description of natural neighbor interpolation", in Interpreting Multivariate Data, edited by V. Barnett (John Wiley & Sons, New York, 1981) pp. 21–36.

Spearman correlations of fixations on landmark pictograms between the two map area conditions (landmark position close to the start or end of the route).

Differential SAR interferometry (DInSAR) uses the phase difference between two SAR signals acquired on two dates over the same area to measure small-scale ground motion. During the last decade the method has been adapted for monitoring permafrost-related ground motion. Here we perform DInSAR on TerraSAR-X data to assess its viability for seasonal thaw subsidence detection in a yedoma landscape of the Lena River Delta. TerraSAR-X is a right-looking SAR satellite launched in 2007, operating in the X-band (wavelength 3.1 cm, frequency 9.6 GHz), with a revisit time of eleven days. All data that we used were acquired in StripMap mode with HH polarization from a descending orbit at 08:34 local acquisition time (22:34 UTC). The incidence angle of the track we use is approximately 31 degrees. The scene size covered an area of approximately 18 km x 56 km. The slant range and azimuth pixel spacing were approximately 0.9 m and 2.4 m, respectively. Based on the ground temperature data we roughly estimated the beginning and the end of thaw season in 2013. The corresponding TerraSAR-X time series used for this study includes nine Single-Look Slant Range Complex (SSC) images taken between 7 June and 14 September 2013. The time span between the acquisitions that we used for interferometry was 11 days, with one exception when the time span was 22 days due to a missing acquisition. The data were processed using the Gamma radar software. The SSC data were converted to Gamma Single Look Complex (SLC) format and the SLC data were then consecutively co-registered with subpixel accuracy (typically better than 0.2 pixels) in such a way that the co-registered slave image became the master for the next image. This way of co-registering also ensures subpixel co-registration accuracy for all interferometric combinations of the nine images. Multilooking was performed with the factor 4 in the range and factor 3 in the azimuth directions to reduce the noise and obtain roughly square ground range pixels. The ground size of the multilooked pixel is approximately 7 m. We removed the topographic phase term using ArcticDEM that is a freely available high-resolution (5 m) circum-Arctic DEM produced from optical stereographic WorldView imagery acquired from 2012–2016. Obtained differential interferograms were then filtered with an adaptive filter based on the local fringe spectrum with the filtering window size of 128 pixels and an alpha exponent of 0.4. Interferograms, featuring especially low coherence, were additionally filtered with a window size of 64 pixels. For the phase unwrapping we used a branch-cut algorithm with the seeding point located approximately in the middle of the study area with relatively high coherence. We did not attempt to unwrap the areas, separated from the main study area by the river channels. The influence of atmospheric phase delays was evident in the unwarpped interferograms. In order to enhance the displacement signal and reduce atmospheric noise, all eight unwrapped interferograms were summed up in a time-continuous stack. Phase rate per day was calculated from the stack. A strong linear ramp was present across the phase rate map. To remove the trend, a 2D linear function was fit to the data and then subtracted from the phase rate map. The phase rate was then converted to vertical displacement rate in meters, under the assumption that the ground movement is purely vertical. The resulting displacement rate map was geocoded using ArcticDEM to the Universal Transverse Mercator (UTM) projection, zone 52N WGS84 with a pixel size of 5 m. The map was finally converted to the displacement magnitude by multiplying the rate by 99 days (from 7 June to 14 September 2013) and converted to centimeters. As opposed to the results, published in the related paper, here we did not start the unwrapping from the known bedrock position, as it was partly affected by low coherence as well as rather remote from the main area of interest and only weakly connected to the rest of the map over a small and noisy area of valid pixels. It means that the displacement map published here, features only displacement values relative to each other, without a fixed reference point. The spatial pattern of the signal, however, did not change with this alteration in processing. The DInSAR map showed a distinct subsidence in most of the thermokarst basins relative to the upland. Moreover, the spatial pattern of DInSAR signal was in high agreement with the surface wetness in the basins, identified with the near infra-red band of a high-resolution optical image. Drier parts of the basins were clearly separated from wetter parts that showed a prominent subsidence. In general, low coherence in combination with atmospheric effects as well as remoteness of a reference ground point were severe obstacles for the retrieval of a wide-area seasonal thaw subsidence map with TerraSAR-X data.

The Davis Coastal Seabed Mapping Survey, Antarctica (GA-4301 / AAS2201 / HI468) was conducted on the Australian Antarctic Division workboat Howard Burton during February-March 2010 as a component of Australian Antarctic Science (AAS) Project 2201 - Natural Variability and Human Induced Change on Antarctic Nearshore Marine Benthic Communities. The survey was undertaken as a collaboration between Geoscience Australia, the Australian Antarctic Division and the Australian Hydrographic Service (Royal Australian Navy). The survey acquired multibeam bathymetry and backscatter datasets from the nearshore region of the Vestfold Hills around Davis Station, Antarctica. These datasets are described by the metadata record with ID Davis_multibeam_grids. This dataset comprises an interpreted geomorphic map produced for the central survey area using multibeam bathymetry and backscatter grids and their derivatives (e.g. slope, contours). Six geomorphic units; basin, valley, embayment, pediment, bedrock outcrop and scarp were identified and mapped using definitions suitable for interpretation at the local scale (nominally 1:10 000). Polygons were created using a combination of automatic extraction and manual digitisation in ArcGIS. For further information on the geomorphic mapping methods and a description of each unit, please refer to OBrien P.E., Smith J., Stark J.S., Johnstone G., Riddle M., Franklin D. (2015) Submarine geomorphology and sea floor processes along the coast of Vestfold Hills, East Antarctica, from multibeam bathymetry and video data. Antarctic Science 27:566-586. This metadata record was created using information in Geoscience Australia's metadata record at http://www.ga.gov.au/metadata-gateway/metadata/record/89984/

Data from Electron Backscatter Diffraction analysis for a small (83 x 110) point map captured using a Bruker eFlash HR (1st generation) with full pattern resolution on a FEI Quanta instrument. The orientation data can be loaded using MTEX 5.0.3 (http://mtex-toolbox.github.io/). The data is released to facilitate the development of new EBSD analysis methodologies, including AstroEBSD (https://github.com/benjaminbritton/AstroEBSD/) which has been developed by the Experimental Micromechanics Research Group (http://www.expmicromech.com) & the Oxford Micromechanics group (http://users.ox.ac.uk/~ajw/). The data is from a lightly deformed sample of interstitial free steel (Ferrite). Orientation analysis was performed using eSprit 2.1 and this is contained within the h5 file. Figures from this data set are provided to illustrate the correct representation of the data. The x axis points right to left, the y axis points top to bottom, and the z axis is out of the page (as per conventions described in http://dx.doi.org/10.1016/j.matchar.2016.04.008). Data has been captured with a 0.15 um step size.

This data was collected within the Harvey Flower EM Suite within the Department of Materials, Imperial College London. The equipment was funded under the Shell-Imperial Advanced Interfaces in Materials Science University Technology Center.

Please contact Dr Ben Britton if you have any queries or require further information (b.britton@imperial.ac.uk).