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30 results for “Spatial accessibility”
EXPLORATORY SPATIAL ANALYSIS OF "ACCESS" TO PHYSICAL AND DIGITAL RETAIL BANKING CHANNELS IN THE UK
<p>File built in order to explore access to banking channels in the UK (February 2019)</p> <p>The report "Exploratory Spatial Analysis of Access to Physical and Digital Retail Banking Channels in the UK" has been published by Think Forward Initiative in October 2019. You can download the full report from here: <a href="https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk">https://www.thinkforwardinitiative.com/research/exploratory-spatial-analysis-of-access-to-physical-and-digital-retail-banking-channels-in-the-uk</a></p> <p>Related code: <a href="https://github.com/andrasonea">andrasonea</a>/<strong><a href="https://github.com/andrasonea/TFI_AccessToBanking">TFI_AccessToBanking</a></strong></p> <p> </p>
Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity - ACCESS-OM2 data and plotting routines
<p>This repository contains the processed data and plotting routines associated with the article</p> <p>Holmes, Groeskamp, Stewart and McDougall (2022), Sensitivity of a Coarse-Resolution Global Ocean Model to a Spatially Variable Neutral Diffusivity, Journal of Advances in Modeling Earth Systems (JAMES), doi: 10.1029/2021MS002914, http://dx.doi.org/10.1029/2021MS002914</p> <p>The contents includes post-processed data output from the 1-degree ACCESS-OM2 ocean-sea-ice model simulations and the python/jupyter plotting routines required to make the plots.</p> <p>The processing script is Holmes2022JAMES_Neutral_Diffusion_ACCESS-OM2_Plotting_Script.ipynb. The data files consist of time-averages or time series of certain metrics processed using NCO tools from the raw ACCESS-OM2 simulation output.</p>
Analysing the intra and interregional components of spatial accessibility gravity model to capture the level of equity in the distribution of hospital services: does they influence patient mobility?
<p>aggregated_data_age55+.csv and distance_matrix_age55+.csv have been included in the second version of the dataset as the reference population is limited to resident with 55 years old or more.</p>
Code + simulated + publically accessable data for "Evaluating health facility access using Bayesian spatial models and location analysis methods"
<p># README</p> <p>These files contain r data objects and R files that represent the key details of the paper, "Evaluating health facility access using Bayesian spatial models and location analysis methods".</p> <p>The following datasources are available for simulation of some of the ideas in the paper.</p> <p>- dat_grid_sim: simulated data of the grid and grid cells<br> - dat_ohca_cv_sim: simulated data containing the cross validated test/training sets of OHCA data<br> - dat_ohca_sim: simulated OHCA event data<br> - dat_aed_sim: simulated AED location data<br> - dat_bldg_sim: simulated building location data<br> - dat_municipality_sim: simulated municipality information<br> - table_1: Table 1 information containing key demographic data</p> <p>These data were produced using the code in 01-create-sim-data.R, and one of the statistical models is demonstrated in 02-demo-inla-model.R</p> <p>In terms of the paper itself, the functions and code used in the manuscript are located in:</p> <p>* 01_tidy.Rmd - analysis code used to tidy up the data</p> <p>* 02_fit_fixed_all_cv.Rmd - analysis code used to place AEDs</p> <p>* 02_model.Rmd - analysis code used to fit the model in INLA</p> <p>* 03_manuscript.Rmd - Full code and text used to create the paper</p> <p>* 04_supp_materials.Rmd - full code and text used to create the supplementary materials</p> <p>The following files are a part of an R package "swatial" that was developed along with the paper. These files are:</p> <p>* DESCRIPTION</p> <p>* NAMESPACE</p> <p>* LICENSE</p> <p>* LICENSE.md</p> <p>* decay.R</p> <p>* spherical-distance.R</p> <p>* test-figure-data-matches.R</p> <p>* test-table-data-matches.R</p> <p>* testthat.R</p> <p>* tidy-inla.R</p> <p>* tidy-posterior-coefs.R</p> <p>* tidy-predictions.R</p> <p>* utils-pipe.R</p> <p>* All files that end in .Rd are documentation files for the functions.</p> <p>## Regarding data sources</p> <p>Census information for Ticino was transcribed from the Annual Statistical Report of Canton Ticino from years 2010 to 2015. This data was taken from their publicly accessible annual reports - for example: (https://www3.ti.ch/DFE/DR/USTAT/allegati/volume/ast_2015.pdf). The raw data was extracted from these annual reports, and placed into the file: "swiss_census_popn_2010_2015.xlsx". These data are put into analysis ready format in the file “01_tidy.Rmd”</p> <p>Housing and other relevant geospatial data can be accessed via http://map.housing-stat.ch/ and https://data.geo.admin.ch/. The maps of buildings from the REA (Register of Buildings and Dwellings) can be found here: https://map.geo.admin.ch/?zoom=11&bgLayer=ch.swisstopo.pixelkarte-grau&lang=en&topic=ech&layers=ch.bfs.gebaeude_wohnungs_register,ch.swisstopo.swissboundaries3d-gemeinde-flaeche.fill,ch.bfs.volkszaehlung-gebaeudestatistik_gebaeude,ch.bfs.volkszaehlung-gebaeudestatistik_wohnungen,ch.swisstopo.swissbuildings3d_1.metadata,ch.swisstopo.swissbuildings3d_2.metadata&E=2717616.28&N=1096597.25&catalogNodes=687,696&layers_timestamp=,,2016,2016,,&layers_visibility=true,false,false,false,false,false&layers_opacity=1,1,1,1,1,0.75</p> <p>For further enquiries on this data, contact the Swiss federal Office of Statistics at the details listed here: https://www.bfs.admin.ch/bfs/en/home/services/contact.html</p> <p>The shapefiles of the Comuni can be accessed here: https://www4.ti.ch/dfe/de/ucr/documentazione/download-file/?noMobile=1</p> <p>Data from the people living in the Municipalities in Ticino can be downloaded here: https://www3.ti.ch/DFE/DR/USTAT/index.php?fuseaction=dati.home&tema=33&id2=61&id3=65&c1=01&c2=02&c3=02</p> <p>## Future work</p> <p>In the future, these functions from the paper may be generalised and put into their own package. If that happens, this repository will be updated with a link to updated functions.</p>
Data and code from: Spatially-explicit foraging by an apex predator linked to nearshore prey and their accessibility in lakes
Open the record for dataset details and reuse information.
Spatial Access Ratio, Albuquerque, NM
GIS-based spatial access measures have been used extensively to monitor social equity and to help develop policy and planning for provision of public services. However, uncertainties in the road datasets used to calculate measures of spatial access remain largely underreported. These uncertainties might result in biases within decision-making that strives for social equity based on seemingly egalitarian accessibility metrics. To better understand and address these uncertainties, we evaluated variations in travel impedance resulting from street layer uncertainty (e.g. proprietary, free, and volunteer-information-based streets) and its propagation in a multi-modal enhanced 2-step floating catchment area (MM-E2SFCA) model of spatial accessibility for car and bus transportation, using datasets in the metropolitan area of Albuquerque, NM, USA. We proposed and demonstrated a novel approach as a solution – the spatial access ratio (SPAR). Results indicate that travel impedance disagreement among different street sources propagate through the modeling process to effect Spatial Access Index (SPAI) estimates. Less urbanized regions were found to experience higher street-source variations when compared with the core-metropolitan area. SPAR reduced uncertainties introduced by the choice of model parameter or street datasets, providing a suitable alternative to SPAI for analyses that do not require an absolute measure of supply to demand ratio. Careful selection of street source data and consideration of the potential for bias, particularly for less urbanized areas and areas reliant on public transportation, is warranted when leveraging SPAI to inform policy.
Replication package for: "Highways, Market Access, and Spatial Sorting".
<ul> <li>The replication package contains data that we are making publicly available.</li> <li>We use two main datasets: census data (Swiss Federal Statistical Office) and taxpayer data (Swiss Federal Tax Administration).</li> <li>Census data are aggregated at the municipality level.</li> <li>Taxpayer data are proprietary. We aggregated these data into four income bins for each municipality. There are fewer than 5 observations in one of these income bins for a bit less than a third of tax data units (municipality-year) and the Swiss Federal Tax Administration asked us to code these bins as “missing” in the dataset we are making publicly available. Hence, the full dataset we are using and the dataset we are making publicly available differ slightly.</li> <li>The ReadMe file includes an Appendix with all Tables and Figures of the paper produced with the restricted, publicly available data.</li> </ul>
Data from: Interspecific interference competition at the resource patch scale: do large herbivores spatially avoid elephants while accessing water?
Open the record for dataset details and reuse information.
ATAC-Se1 to investigate the spatial choreography of the accessible genome
GEO Series GSE76006. Homo sapiens. 22 samples. Type: Other.
Spatial distribution and chromatin accessibility determine the therapeutic capacity of microglial subsets during neurodegeneration [scRNAseq2_Csf1]
GEO Series GSE296027. Mus musculus. 17 samples. Type: Expression profiling by high throughput sequencing.
Spatially-resolved chromatin accessibility profiling of the developing mouse embryo
GEO Series GSE214986. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Spatial gradients of chromatin accessibility endow embryonic cells with positional information [Multiome]
GEO Series GSE224722. Gallus gallus. 2 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Spatial gradients of chromatin accessibility endow embryonic cells with positional information [RNA-seq]
GEO Series GSE224436. Gallus gallus. 168 samples. Type: Expression profiling by high throughput sequencing.
Spatial organization, chromatin accessibility and gene-regulatory programs defining sensory neurons
GEO Series GSE287551. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Spatial distribution and chromatin accessibility determine the therapeutic capacity of microglial subsets during neurodegeneration [scRNAseq1_LPS_ABX]
GEO Series GSE296026. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Spatial profiling of chromatin accessibility in formalin-fixed paraffin-embedded tissues
GEO Series GSE278007. Mus musculus; Homo sapiens. 18 samples. Type: Other.
Spatial distribution and chromatin accessibility determine the therapeutic capacity of microglial subsets during neurodegeneration [ATAC-seq]
GEO Series GSE296025. Mus musculus. 9 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Spatial profiling of chromatin accessibility in mouse and human tissues
GEO Series GSE171943. Homo sapiens; Mus musculus. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
IDH-mutant gliomas arise from glial progenitor cells harboring the initial driver mutation (Related accession no. GSE275791) - Spatial transcriptomics dataset
GEO Series GSE302642. Homo sapiens. 6 samples. Type: Other.
ATAC-Seq to investigate the spatial choreography of the accessible genome
GEO Series GSE79921. Homo sapiens. 8 samples. Type: Other.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.