One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

Efficient body movement is required in our daily lives, as it facilitates responding to the external environment and producing movements in various directions and distances. While numerous studies have reported on goal-directed movements in the frontal direction during gait initiation, there is limited research on the efficient movement of the lower limbs in multiple directions and distances. Therefore, we aimed to examine changes in the kinematics of lower-limb reaching movements to determine skilled motor ability in terms of direction and distance. Sixteen adults (10 male participants) were requested to reach targets projected on the floor in seven directions and at three distances for a total of 21 points. The reaching time slowed down for the contralateral side (right foot to left-sided target) and was caused by a slower start of the toe movement. To identify the cause of this delay, we analyzed the onset of movement at each joint and found that movement to the contralateral side starts from the hip, followed by the knee, and subsequently the toe. The time-to-peak velocity was also calculated, and the motion required to reach the target in the shortest time varied depending on direction and distance. These results suggested that movement kinematics vary with direction and distance, resulting in a slower reaching time on the contralateral side. The results of our study hold promise for potential applications in sports and rehabilitation.

The mallard (Anas platyrhynchos) is an abundant and wide-spread duck species that exhibits considerable variation in migratory behavior due to a relatively large body size and behavioral plasticity in habitat use. Understanding migration and other movements of mallards has societal interest in a wildlife management context because mallards are a preferred species of waterfowl by hunters and have the largest annual harvest of all duck species in the United States. We monitored juvenile mallards during autumn-winters 2018-2019 and 2019-2020 to determine factors associated with timing, distance, and direction of regional and migration movements during autumn-winter across the midcontinent of North America.

Movement distance and direction data of tagged Acanthaster.

IntroductionThe cognitive map is an internal representation of the environment and allows us to navigate through familiar environments. It preserves the distances and directions between landmarks which help us orient ourselves in our surroundings. The aim of our task was to understand the role played by theta waves in the cognitive map and especially how the cognitive map is recalled and how the manipulation of distances and directions occurs within the cognitive map.MethodIn order to investigate the neural correlates of the cognitive map, we used the Cognitive Map Recall Test, in which 33 participants had to estimate distances and directions between familiar landmarks tailored to their own knowledge. We examined the role of theta waves in the cognitive map, as well as the brain regions that generated them. To that aim, we performed electroencephalographic source imaging while focusing on frequency spectral analysis.ResultsWe observed increases of theta amplitude in the frontal, temporal, parahippocampal gyri and temporal poles during the recall of the cognitive map. We also found increases of theta amplitude in the temporal pole and retrosplenial cortex during manipulation of directions. Overall, direction processing induces higher theta amplitude than distance processing, especially in the temporal lobe, and higher theta amplitude during recall compared to manipulation, except in the retrosplenial cortex where this pattern was reversed.DiscussionWe reveal the role of theta waves as a marker of directional processing in the retrosplenial cortex and the temporal poles during the manipulation of spatial information. Increases in theta waves in frontal, parahippocampal, temporal and temporal pole regions appear to be markers of working memory and cognitive map recall. Therefore, our Cognitive Map Recall Test could be useful for testing directional difficulties in patients. Our work also shows that there are two distinct parts to the cognitive map test: recall and manipulation of spatial information. This is often considered as two similar processes in the literature, but our work demonstrates that these processes could be different, with theta waves from different brain regions contributing to either recall or manipulation; this should be considered in future studies.

Movement distance and direction data of tagged Acanthaster in Viti Levu, Fiji from 2010-2012. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv acquisition_description=To test whether\u00a0Acanthaster\u00a0selectively migrated into the MPAs versus the fished areas, 120 adults of 36 \u00b1 2 cm diameter (from the tips of opposite arms) were collected from the MPAs and adjacent fished areas of reefs flats near\u00a0Votua, Vatu-o-lalai, and\u00a0Namada\u00a0villages, with 20 individuals collected from within and 20 from outside the MPAs at each village site (40 individuals village-1\u00a0site-1). Each individual was tagged with five plastic tag fasteners between the base of individual\u00a0arms,\u00a0and labeled flagging tape was attached to the end of each tag fastener to aid in location and identification. Individuals were then enclosed within cages located along the MPA border perpendicular to the coastline at each site (20 individuals border-1\u00a0location-1) for 48 h to allow for tag acclimation. Upon release, individuals\u2019\u00a0movements were monitored at 24 h intervals for four to eight days by physically locating each individual and recording its location via GPS (Garmin GPS 76CSX). GPS coordinates of individual\u00a0Acanthaster\u00a0positions were imported into\u00a0ArcMAP\u00a0(Version 10.3.1), and the Geospatial Modeling Environment extension (Version 0.7.4.0) was used to calculate individuals\u2019 initial and final directions of movement relative to their release point along their respective MPA border, as well as each individual\u2019s net displacement between consecutive days.\u00a0 awards_0_award_nid=480718 awards_0_award_number=OCE-0929119 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward?AWD_ID=0929119 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=David L. Garrison awards_0_program_manager_nid=50534 awards_1_award_nid=674109 awards_1_award_number=U01-TW007401 awards_1_data_url=https://projectreporter.nih.gov/project_info_description.cfm?icde=0&aid=7741942 awards_1_funder_name=National Institutes of Health awards_1_funding_acronym=NIH awards_1_funding_source_nid=636502 awards_1_program_manager=Flora Katz awards_1_program_manager_nid=674108 cdm_data_type=Other comment=Acanthaster Tagging M. Hay and C. Clements, PIs Version 23 Version 2017 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time

We studied >500 golden eagles tracked by telemetry over a 10-year period in western North America, of which 160 engaged in non-routine, long-distance (>300 km) movements. We identified spatial and temporal correlates of those movements at both small and large scales, and we quantified movement timing and direction. We further tested which age and sex classes of eagles were more likely to engage in these movements. This dataset includes data on daily distances and their correlates, long-distance event distances and durations and their correlates, event timing and directions, and eagle ages and sexes.

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This table contains information about the average distances for residents of the Netherlands from their home address to the nearest facilities (e.g. school, GP or library). The table also includes information about the number of facilities located within a certain distance from the residential address. These data are calculated as an average over all persons in the area.

The data are available at different regional levels. The lowest level is the municipality.

From 2022, the calculation of distances from addresses to the (nearest) facilities has been improved: from then on, driving directions are taken into account when determining the routes and distances from addresses to the (nearest) facilities. In previous years, this did not happen or only to a limited extent.

Data available from: 2006

Status of the figures: The figures in this table are final until reporting year 2022. From reporting year 2023 on, the figures will first be published as preliminary. Once all variables in a reporting year have been published as preliminary, they will be made final at the next publication moment.

Changes per April 2025 The data on the proximity of jobs have been removed from the table for the years 2021 and 2022. The source file for this facility has changed as of reporting year 2021, meaning that the location of a significant proportion of jobs is no longer known. Due to this change, the proximity of jobs can no longer be calculated with the same accuracy as in previous years.

The figures for all provisions for reporting year 2022 were recalculated following the introduction of a new version of the required software, which included a different method of rounding compared to previous years. Compared to the publication of October 2024, the results are unchanged for the whole of the Netherlands. For some municipalities, the distances have now decreased by at most 0.5 km. For some districts and neighbourhoods, the distances decreased by no more than 2.1 km.

The figures for all provisions for reporting year 2023 were recalculated following the introduction of a new version of the required software, which included a different method of rounding compared to previous years. Various improvements and corrections were also made to the road network. For the whole of the Netherlands on average, distances have decreased by up to 0.1 km. For some municipalities, the distances decreased by up to 1.2 km. For some districts and neighbourhoods, distances decreased by up to 4.6 km and increased by up to 2.3 km in other districts and neighbourhoods.

For reporting year 2023, figures on the following facilities have been added to the table:

Hospitality sector Cafeterias, etc. Hotels, etc.

When will new figures be released? In the summer of 2025, the first figures for reporting year 2024 will be added to this table.

The dataset originates from 10 years of mid-distance values (2013-2022) of current and direction in the surface, in the water masses (intervals for 5 m, 15 m, 30 m, 50 m, 100 m, 150 m, 200 m and 250 m), as well as at the seabed. The distance from the seabed goes with the current model layout from a few cm on shallow water and up to 1.5 m when the total depth is 100 m or more. The data set is a point dataset available as a WMS and WFS service, as well as for download via the Institute of Marine Research’s Geoserver https://kart.hi.no/data - select Layer preview and search for the data set for multiple download options, e.g. csv or shape file. The dataset is relatively large (about 2.7 million points per sea depth). The coastal model Norkyst (version 3) is a calculation model that simulates e.g. current, salinity and temperature with 800 meters spatial resolution, in several vertical levels and with high resolution in time for the entire Norwegian coast, based on the model system ROMS (Regional Ocean Modeling System, http://myroms.org). NorKyst-800 has been developed by the Institute of Marine Research in collaboration with the Norwegian Meteorological Institute. https://imr.brage.unit.no/imr-xmlui/handle/11250/116053

In order to improve the city network mining method, the inter-cities’ connection strength, structure and density, and distribution patterns of city network in the Yangtze River Economic Belt of China have been empirically analyzed through the combined application of SNA method, “Dual-direction time distance” modified gravity model and ArcGIS geographic visualization method. The results show that the modified gravity model can better reveal the interaction differences between cities and reflect the current and potential economic, population and resource relations among cities. The city network density of this area has positively close relationship with the regional economic development level. The average value of degree centrality in the basin is high, but the difference between cities is obvious. The “agglomeration effect” of the central cities is significant, and the urban connections have an obvious cluster structure, showing an “M” shaped spatial distribution along the Yangtze River; The inner interaction strength of city network subgroups is high, but the connection between subgroups is low. The law of “downstream > midstream > upstream” also appears on the closeness centrality and betweenness centrality. In the future, it is essential to improve the integration and multi-level connections of urban agglomeration in the river basin and form a development pattern of “downstream driving - midstream transition - upstream connection”; strengthen the functions and connections of central and subcentral cities.

We make our dataset publicly avaiable. It consists of 50 H&E stained histopathology annotated images at the nuclei level. This dataset is ideal for those who want an exhaustive annotation of H&E breast cancer patient from a Tripple Negative Breast Cancer cohort.

Path integration is a powerful navigational mechanism whereby individuals continuously update their distance and angular vector of movement to calculate their position in relation to their departure location, allowing them to return along the most direct route even across unfamiliar terrain. While path integration has been investigated in several terrestrial animals, it has never been demonstrated in aquatic vertebrates, where movement occurs through volumetric space and sensory cues available for navigation are likely to differ substantially from those in terrestrial environments. By performing displacement experiments with Lamprologus ocellatus, we show evidence consistent with fish using path integration to navigate alongside other mechanisms (allothetic place cues and route recapitulation). These results indicate that the use of path integration is likely to be deeply rooted within the vertebrate phylogeny irrespective of the environment, and suggests that fish may possess a spatial..., Ethics and Approval The study was approved by the University of Oxford’s Animal Welfare and Ethical Review Body (AWERB) (project code: APA/1/5/ZOO/NASPA/Burt/DistanceEstimation). Animal husbandry We used 40 naive adult male cichlids (Lamprologus ocellatus, Figure 4), sourced from a captive-bred laboratory population (standard body length = 3-5cm). Individuals came originally from 6 subpopulations: B (n=4), C (n=7), D (n=1), E (n=4), F (n=16), G (n=8, Table S3). Subjects were housed individually in tanks measuring 35 cm x 32 cm x 60 cm (width x height x length) containing a sand substrate to a depth of 4 cm and a single home shell. Illumination via fluorescent light followed a 12h light/dark cycle. Individuals were fed twice a day, in the morning and the afternoon, with commercial flake food, supplemented with Mysis shrimps once a week. The tanks were cleaned once a week and the water quality checked to maintain constant pH, gH and KH of 8.4, 23 and 12ppm respectively. Ammonia and nitrit..., , # Taking a shortcut: What mechanisms do fish use?

Please find below all descriptions of data files and code used for the manuscript: Taking a shortcut: what mechanisms do fish use? , by Sibeaux et al. 2024

To better understand the data analyses and processing we have organised this file into 5 subsections: 1-Matlab trajectory analysis 2-Distance Analysis with R 3-Distance to Random 4-Comparison fish versus model angle and distance 5-intrinsic and extrinsic effects on model chosen 6--Extra analyses asked by reviewers

Some files are reused among subsections

1-Matlab trajectory analysis

Please find below the description of the MATLAB code used for the analysis of the fish trajectories and the associated data files.

1_ "PathIntegrationVideoTracker.m" MATLAB file produced by C.N. to track the fish in the experimental apparatus and extract the x and y coordinates of the fish

2_ "PathAnalysisCropingtoSize.m" MATLAB file (by A.S.) allows to crop the fish trajectory and the mode...

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: 1. Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. 2. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. 3. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

This dataset provides a comprehensive set of wind turbine sound setbacks from every residential structure in the contiguous United States (CONUS). A sound setback is defined as the minimum required distance between a residential structure and a hypothetical turbine installation site to ensure that modeled sound levels received at the residence do not exceed local sound ordinances, which are commonly expressed in A-weighted decibels (dBA). Therefore, sound setbacks are a local spatial assessment combining multiple factors, including the sound pressure curve as a function of the observer _location (distance and direction) relative to the turbine, local sound regulations, and the geographical distribution of residential structures. The dataset is organized into multiple scenario-based products, detailed as follows: Existing and extrapolated sound setbacks. An existing scenario characterizes sound setbacks only in states or counties that have implemented sound regulations as of 2022. The extrapolated scenarios extend a constant sound threshold to counties that lack explicit sound regulations, with thresholds ranging from 35 to 60 dBA, in 5-dBA increments reflecting the variation observed in current sound ordinances. Sound setbacks in directional and worst scenarios. The directional scenario accounts for the distance and orientation of residential structures relative to a hypothetical turbine _location, utilizing the turbine's sound emissions in that specific direction. In contrast, the worst scenario takes loudest sound level at each distance step from the turbine, irrespective of directional considerations, which aligns with current industry practice. Supply curves for Open and Reference Access scenarios. This dataset includes supply curves generated by the reV model, which integrates each of the above sound setbacks into both Open and Reference siting scenarios. In addition, two Open and Reference baselines scenarios were included which do not consider sound setbacks for comparative analysis. All sound setback data are stored in TIF files, with partial maps of the data provided in PNG format. The values in the sound setback raster range from 0 to 1, representing the fraction of developable land within a 90 meter by 90 meter pixel due to sound ordinances. A value of 0 indicates areas where wind energy development is prohibited, while a value of 1 signifies areas fully permissible. The wind turbine parameters used in the sound modeling are based on the land-based turbine from International Energy Agency (IEA), featuring a rated electrical power of 3.4 MW, a rotor diameter of 130 meters, and a hub height of 110 meters. The atmospheric conditions, including wind speed/direction, turbulence, air temperature, relative humidity, and air pressure, that drive the sound generation are obtained from the WIND Toolkit dataset.

A dataset of repeated measures of distance perception at physical distances of 7, 8, 9, 10, and 11 meters. The data are also multivariate, with five dependent measures of distance perception. This is a 5 (physical distance) x 5 (dependent measure) within-participants design with a sample size of 46. Note data is missing for 15 trials due participant and experimenter errors.

The csv file has 230 rows and 7 columns.

Subject: Unique identifier for each participant. Physical Distance: Physical distance from the participant to the target cone, in meters. Blindwalk Away: Participants put on the blindfold after viewing the target. Next, participants took one step to the left and turned 180 degrees to face the opposite direction. Participants were instructed to walk forward until they had walked the original distance to the target.
Blindwalk Toward: Participants put on the blindfold after viewing the target. Next, participants walked forward until they thought they had reached the target cone. Triangulated BW: Participants put on the blindfold after viewing the target. Next, participants turned right 90 degrees and walked forward 5 meters. The experimenter told participants when to stop walking. Finally, participants turned to face toward the target and walked forward two steps. Verbal: Participants stated the distance between the target cone and themselves, in feet and inches. Visual Matching: An experimenter stood next to the target cone and walked away from the cone in a straight line that was perpendicular to the extent between the target and the participant. Participants instructed the experimenter to stop walking when they thought that the distance between the target and the experimenter was equal to the target distance.

Use the Nearby template to guides your app users to places of interest close to an address. This template helps users find focused types of locations (such as schools) within a search distance of an address, their current location, or other place they specify. They can adjust distance values to change the search radius and get directions to locations they select. For users who are searching, you can set a range for the distance slider so users can define their search buffer or pan the map to see results from the map view. Include directions to help users navigate to locations within a defined search radius. Include the export tool to allow users to capture images of the map along with results from the search. Examples: Create a store locator app that allows customers to input a location, find a nearby store, and navigate to it. Create an app for finding health care facilities within a specified distance of a searched address. Provide users with directions and information for election polling locations. Build an app where users can find nearby trails and view an elevation profile of each result. Data requirements The Nearby template requires a feature layer to take full advantage of its capabilities. Key app capabilities Distance slider - Set a minimum and maximum search radius for finding results. Map extent result - Show all the results in the map view. Panel options - Customize result panel location information with feature attributes from a configured pop-up. Results-focused layout - Keep the map out of the app to maintain focus on the search and results. Attribute filter - Configure map filter options that are available to app users. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Alternatively, download the search results as a .csv file. Directions - Provide directions from a searched location to a result location. Elevation profile - Generate an elevation profile graph across an input line feature that can be selected in the scene or from drawing a single or multisegment line using the tool. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This dataset provides results of mean offshore wind speed measured in metres per second at a height 100m above sea level. Wind speed is the rate of the movement of wind in distance per unit of time. It is the rate of the movement of air flow. The geographic coverage of wind speed includes an area including the Irish Internal Waters and the Irish Territorial Sea up to 12 nautical miles from the baseline. Wind speed measurements modelled during 2013. The Sustainable Energy Authority of Ireland (SEAI) Wind Atlas 2013 was a digital map of Ireland's wind energy resource. The SEAI is Ireland's national sustainable energy authority tasked with making Ireland’s energy sustainable, secure, affordable, and clean. Wind speed measurements were created to support wind energy resource potential to assist all those concerned with the wind planning process and be of great use to developers and policy makers alike. Data completed during 2013 for geographic area coverage.

This dataset provides results of mean offshore wind speed measured in metres per second at a height 100m above sea level. Wind speed is the rate of the movement of wind in distance per unit of time. It is the rate of the movement of air flow. The geographic coverage of wind speed includes an area including the Irish Internal Waters and the Irish Territorial Sea up to 12 nautical miles from the baseline. Wind speed measurements modelled during 2003. The Sustainable Energy Authority of Ireland (SEAI) Wind Atlas 2003 was a digital map of Ireland's wind energy resource. The SEAI is Ireland's national sustainable energy authority tasked with making Ireland’s energy sustainable, secure, affordable, and clean. Wind speed measurements were created to support wind energy resource potential to assist all those concerned with the wind planning process and be of great use to developers and policy makers alike. Data completed during 2003 for geographic area coverage.

The Climatological Database for the World's Oceans: 1750-1854 (CLIWOC) project, which concluded in 2004, abstracted more than 280,000 daily weather observations from ships' logbooks from British, Dutch, French, and Spanish naval vessels engaged in imperial business in the eighteenth and nineteenth centuries. These data, now compiled into a database, provide valuable information for the reconstruction of oceanic wind field patterns for this key period that precedes the time in which anthropogenic influences on climate became evident. These reconstructions, in turn, provide evidence for such phenomena as the El Niño-Southern Oscillation and the North Atlantic Oscillation. Of equal importance is the finding that the CLIWOC database the first coordinated attempt to harness the scientific potential of this resource represents less than 10 percent of the volume of data currently known to reside in this important but hitherto neglected source.