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This archive reproduces a figure titled "Figure 3.2 Boone County population distribution" from Wang and vom Hofe (2007, p.60). The archive provides a Jupyter Notebook that uses Python and can be run in Google Colaboratory. The workflow uses the Census API to retrieve data, reproduce the figure, and ensure reproducibility for anyone accessing this archive.The Python code was developed in Google Colaboratory, or Google Colab for short, which is an Integrated Development Environment (IDE) of JupyterLab and streamlines package installation, code collaboration, and management. The Census API is used to obtain population counts from the 2000 Decennial Census (Summary File 1, 100% data). Shapefiles are downloaded from the TIGER/Line FTP Server. All downloaded data are maintained in the notebook's temporary working directory while in use. The data and shapefiles are stored separately with this archive. The final map is also stored as an HTML file.The notebook features extensive explanations, comments, code snippets, and code output. The notebook can be viewed in a PDF format or downloaded and opened in Google Colab. References to external resources are also provided for the various functional components. The notebook features code that performs the following functions:install/import necessary Python packagesdownload the Census Tract shapefile from the TIGER/Line FTP Serverdownload Census data via CensusAPI manipulate Census tabular data merge Census data with TIGER/Line shapefileapply a coordinate reference systemcalculate land area and population densitymap and export the map to HTMLexport the map to ESRI shapefileexport the table to CSVThe notebook can be modified to perform the same operations for any county in the United States by changing the State and County FIPS code parameters for the TIGER/Line shapefile and Census API downloads. The notebook can be adapted for use in other environments (i.e., Jupyter Notebook) as well as reading and writing files to a local or shared drive, or cloud drive (i.e., Google Drive).

CANUE staff developed the Green Roads data set by combining street network files from Open Street Map 9OSM) (downloaded Nov 29, 2020) and annual average normalized difference vegetation index (NDVI) data from LandSat 8 circa 2016 from Google Earth Engine. OSM roads categorized as primary, secondary, tertiary, tertiary link, residential, unclassified and unknown were extracted from OSM, combined into a single file and clipped to urban areas. Urban areas were defined as all dissemination blocks classified as small population centres (population 1,000 to 29,999), medium population centres (population 30,000 to 99,999) or large population centres (population 100,000 or greater) in the 2016 Census. The urban roads layer was used to extract all LandSat 8 pixels with NDVI data (30m resolution). All extracted pixels with an NDVI value of 0.3 or greater, indicating green vegetation, were converted into points. Finally, the total number or points and the average NDVI value was calculated within buffers of 250m, 500m, 750m and 1000m of DMTI single-link postal codes from 2016.

This MSOA atlas provides a summary of demographic and related data for each Middle Super Output Area in Greater London. The average population of an MSOA in London in 2010 was 8,346, compared with 1,722 for an LSOA and 13,078 for a ward.

The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, births, deaths, health, housing, crime, commercial property/floorspace, income, poverty, benefits, land use, environment, deprivation, schools, and employment.

If you need to find an MSOA and you know the postcode of the area, the ONS NESS search page has a tool for this.

The MSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5).

CURRENT MSOA BOUNDARIES (2011)

PREVIOUS MSOA BOUNDARIES (2001)

NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard.

Tips:

1. - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data.

2. - To view data just for one borough*, use the filter tool.

3. - The legend settings can be altered by clicking on the pencil icon next to the MSOA tick box within the map legend.

4. - The areas can be ranked in order by clicking at the top of the indicator column of the data table.

Themes included here are Census 2011 Population, Mid-year Estimates, Population by Broad Age, Households, Household composition, Ethnic Group, Country of Birth, Language, Religion, Tenure, Dwelling type, Land Area, Population Density, Births, General Fertility Rate, Deaths, Standardised Mortality Ratio (SMR), Population Turnover Rates (per 1000), Crime (numbers), Crime (rates), House Prices, Commercial property (number), Rateable Value (£ per m2), Floorspace; ('000s m2), Household Income, Household Poverty, County Court Judgements (2005), Qualifications, Economic Activity, Employees, Employment, Claimant Count, Pupil Absence, Early Years Foundation Stage, Key Stage 1, GCSE and Equivalent, Health, Air Emissions, Car or Van availability, Income Deprivation, Central Heating, Incidence of Cancer, Life Expectancy, and Road Casualties.

• The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster.

These profiles were created using the most up to date information available at the time of collection (Spring 2014).

You may also be interested in LSOA Atlas and Ward Atlas.

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

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

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

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

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

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

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

travel_time_to_ports_x (x ranges from 1 to 5)

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

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

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

Temporal resolution 2015

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

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

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

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

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

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

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

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

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

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

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

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

DATASET: Alpha version 2010, 2015 and 2010 estimates of numbers of people per pixel ('ppp') and people per hectare ('pph'), with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Random Forest FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM_ppp_v2b_2010_UNadj.tif = Vietnam (VNM) population per pixel (ppp) map for 2010 (popmap10) adjusted to match UN national estimates (UNadj), version 2b (v2b). DATE OF PRODUCTION: January 2013 Also included: (i) Metadata html file, (ii) Google Earth file, (iii) Population datasets produced using original census year data

Publicly available geocoded social determinants of health and mobility datasets used in the analysis of "Chronic Acid Suppression and Social Determinants of COVID-19 Infection".These datasets are required for the analytical workflow shared on Github which demonstrates how the analysis in the manuscript was done using randomly generated samples to protect patient privacy.zcta_county_rel_10.txt - Population and housing density from the 2010 decennial census. Obtained from: https://www2.census.gov/geo/docs/maps-data/data/rel/zcta_county_rel_10.txtcre-2018-a11.csv - Community Resilience Estimates which is is the capacity of individuals and households to absorb, endure, and recover from the health, social, and economic impacts of a disaster such as a hurricane or pandemic. Data obtained from: https://www.census.gov/data/experimental-data-products/community-resilience-estimates.htmlzcta_tract_rel_10.txt - Relationship between ZCTA and US Census tracts (used to map census tracts to ZCTA). Data obtained from: https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.html#par_textimage_674173622mask-use-by-county.txt - Mask Use By County comes from a large number of interviews conducted online by the global data and survey firm Dynata at the request of The New York Times. The firm asked a question about mask use to obtain 250,000 survey responses between July 2 and July 14, enough data to provide estimates more detailed than the state level. Data obtained from: https://github.com/nytimes/covid-19-data/tree/master/mask-usemobility_report_US.txt - Google mobility report which charts movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. Data obtained from: https://github.com/ActiveConclusion/COVID19_mobility/blob/master/google_reports/mobility_report_US.csvACS2015_zctaallvars.csv - Social Deprivation Index is a composite measure of area level deprivation based on seven demographic characteristics collected in the American Community Survey (https://www.census.gov/programs-surveys/acs/) and used to quantify the socio-economic variation in health outcomes. Factors are: Income, Education, Employment, Housing, Household Characteristics, Transportation, Demographics. Data obtained from: https://www.graham-center.org/rgc/maps-data-tools/sdi/social-deprivation-index.html

WorldPop Asia dataset details_DATASET: Alpha version 2010, 2015 and 2010 estimates of numbers of people per pixel (ppp) and people per hectare (pph), with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.REGION: AsiaSPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)PROJECTION: Geographic, WGS 84UNITS: Estimated persons per grid squareMAPPING APPROACH: Random ForestFORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)FILENAMES: Example - ARM_ppp_v2c_2010_UNadj.tif = Armenia (ARM) population per pixel (ppp) modelling version 2c (v2c) map for 2010 (2010) adjusted to match UN national estimates (UNadj).DATE OF PRODUCTION: May 2016Also included: (i) Metadata html file, (ii) Population datasets produced using original census year data, (iii).kmz Google Earth file.

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

CANUE staff developed the Green Roads data set by combining street network files from Open Street Map 9OSM) (downloaded Nov 29, 2020) and annual average normalized difference vegetation index (NDVI) data from LandSat 8 circa 2016 from Google Earth Engine. OSM roads categorized as primary, secondary, tertiary, tertiary link, residential, unclassified and unknown were extracted from OSM, combined into a single file and clipped to urban areas. Urban areas were defined as all dissemination blocks classified as small population centres (population 1,000 to 29,999), medium population centres (population 30,000 to 99,999) or large population centres (population 100,000 or greater) in the 2016 Census. The urban roads layer was used to extract all LandSat 8 pixels with NDVI data (30m resolution). All extracted pixels with an NDVI value of 0.3 or greater, indicating green vegetation, were converted into points. Finally, the total number or points and the average NDVI value was calculated within buffers of 250m, 500m, 750m and 1000m of DMTI single-link postal codes from 2016.

This data package contains data that were used to support conclusions drawn in “Seed origin and warming constrain lodgepole pine recruitment, slowing the pace of population range shifts”, by Conlisk et al. 2018. Experimental data collected at field sites within the Alpine Treeline Warming Experiment (ATWE) on Niwot Ridge, Colorado, USA were used to formulate models packaged within “Model_archive” in the zipped folder “Conlisk_etal_GCB2018_model_archive20201202.zip”. “Model_archive” contains four comma-separated values (.csv) files used to create Figures 4 and 6 in the published journal, and five folders that contain population model files. These files can be opened with either a text-editor software, or Microsoft Excel. Models are stored as text files (.txt), and .pch, .sch, and .fch files can also be opened as text files using any simple text-editor software. However, please note that all the aforementioned files are specific to the RAMAS’ population modeling software suite, and you will need the software in order to be able to run the models. Analyses for this publication utilized Metapop. Experimental seedling and microclimate data are available in files “PICOseedlings20160408rev20210127” and “Climate_Table_d0_v20160607EEC_rev20210803”, respectively. Both files are available in .csv and .xlsx formats, and zipped in their respective folders. Data files with the same name are identical; file formats are provided for accessibility. “PICOseedlings…” contain lodgepole pine seedling data that were used for analysis and model parameterization, while “Climate_Table...” files contain delta values presented in Table 1 of the publication. The respective files can be opened using any simple text-editor software, and Microsoft Excel. “StatsPICO06072016rev12212020.R” is an R script file containing code used for statistical analyses reported in the final manuscript; R version 3.3.3 was used. The file can be opened by R, RStudio, and simple text-editor software. Some calculations were not used in the publication, and are indicated in the file. As above, “gcb13840-sup-0001-supinfo” is archived here in two forms: Microsoft Word (.docx) and PDF. The file contents are identical, and different file formats are provided for greater flexibility in file access. The .docx file can be opened by Microsoft Word, and the PDF can be opened by Adobe Acrobat Reader, or any application compatible with PDF files. Geospatial data delineating the three study sites are also included in this archive: .kml files can be opened by Google Earth or Google Maps, and shapefiles (.shp) can be opened by geospatial information systems applications compatible with shapefiles, such as ESRI’s ArcGIS suite, and QGIS. ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- Understanding how climate warming will affect the demographic rates of different ecotypes is critical to predicting shifts in species distributions. Here we present results from a common garden climate change experiment in which we measured seedling recruitment of lodgepole pine (Pinus contorta), a widespread North American conifer that is also planted globally. Seeds from a low-elevation provenance had greater recruitment to their third year (by 323%) than seeds from a high-elevation provenance across sites within and above its native elevation range, and across climate manipulations. Heating reduced recruitment (by 49%) to the third year of both low- and high-elevation seed sources across the elevation gradient, while watering alleviated some of the negative effects of heating (108% increase in watered plots). Demographic models based on recruitment data from the climate manipulations and long-term observations of adult populations revealed that heating could effectively halt modeled upslope range expansion except when combined with watering. Simulating fire and rapid post-fire forest recovery at lower elevations accelerated lodgepole pine expansion into the alpine, but did not alter final abundance rankings among climate scenarios. Regardless of climate scenario, greater recruitment of low-elevation seeds compensated for longer dispersal distances to treeline, assuming colonization was allowed to proceed over multiple centuries. Our results show that ecotypes from lower elevations within a species’ range could enhance recruitment and facilitate upslope range shifts with climate change.

The LSOA atlas provides a summary of demographic and related data for each Lower Super Output Area in Greater London. The average population of an LSOA in London in 2010 was 1,722 compared with 8,346 for an MSOA and 13,078 for a ward.

The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, diversity, households, health, housing, crime, benefits, land use, deprivation, schools, and employment.

Due to significant population change in some areas, not all 2011 LSOA boundaries are the same as previous LSOA boundaries that had been used from 2001. A lot of data is still only available using the 2001 boundaries therefore two Atlases have been created - one using the current LSOA boundaries (2011) and one using the previous boundaries (2001).

If you need to find an LSOA and you know the postcode of the area, the ONS NESS search page has a tool for this.

The LSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5).

CURRENT LSOA BOUNDARIES (2011)

NOTE: There is comparatively less data for the new boundaries compared with the old boundaries

PREVIOUS LSOA BOUNDARIES (2001)

For 2011 Census data used in the 2001 Boundaries Atlas: For simplicity, where two or more areas have been merged, the figures for these areas have been divided by the number of LSOAs that used to make that area up. Therefore, these data are not official ONS statisitcs, but presented here as indicative to display trends.

NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard.

IMPORTANT: Due to the large amount of data and areas, the LSOA Atlas may take up to a minute to fully load. Once loaded, the report will work more efficiently by using the filter tool and selecting one borough at a time. Displaying every LSOA in London will slow down the data reload.

Tips:

1. - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data.

2. - To view data just for one borough, use the filter tool.

3. - The legend settings can be altered by clicking on the pencil icon next to the LSOA tick box within the map legend.

4. - The areas can be ranked in order by clicking at the top of the indicator column of the data table.

Beware of large file size for 2001 Boundary Atlas (58MB) alternatively download Zip file (21MB).

Themes included in the atlases are Census 2011 population, Mid-year Estimates by age, Population Density, Households, Household Composition, Ethnic Group, Language, Religion, Country of Birth, Tenure, Number of dwellings, Vacant Dwellings, Dwellings by Council Tax Band, Crime (numbers), Crime (rates), Economic Activity, Qualifications, House Prices, Workplace employment numbers, Claimant Count, Employment and Support Allowance, Benefits claimants, State Pension, Pension Credit, Incapacity Benefit/ SDA, Disability Living Allowance, Income Support, Financial vulnerability, Health and Disability, Land use, Air Emissions, Energy consumption, Car or Van access, Accessibility by Public Transport/walk, Road Casualties, Child Benefit, Child Poverty, Lone Parent Families, Out-of-Work families, Fuel Poverty, Free School Meals, Pupil Absence, Early Years Foundation Stage, Key Stage 1, Key Stage 2, GCSE, Level 3 (e.g A/AS level), The Indices of Deprivation 2010, Economic Deprivation Index, and The IMD 2010 Underlying Indicators.

The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster.

These profiles were created using the most up to date information available at the time of collection (Spring 2014).

You may also be interested in MSOA Atlas and Ward Atlas.

The LSOA atlas provides a summary of demographic and related data for each Lower Super Output Area in Greater London. The average population of an LSOA in London in 2010 was 1,722 compared with 8,346 for an MSOA and 13,078 for a ward. The profiles are designed to provide an overview of the population in these small areas by combining a range of data on the population, diversity, households, health, housing, crime, benefits, land use, deprivation, schools, and employment. Due to significant population change in some areas, not all 2011 LSOA boundaries are the same as previous LSOA boundaries that had been used from 2001. A lot of data is still only available using the 2001 boundaries therefore two Atlases have been created - one using the current LSOA boundaries (2011) and one using the previous boundaries (2001). If you need to find an LSOA and you know the postcode of the area, the ONS NESS search page has a tool for this. The LSOA Atlas is available as an XLS as well as being presented using InstantAtlas mapping software. This is a useful tool for displaying a large amount of data for numerous geographies, in one place (requires HTML 5). CURRENT LSOA BOUNDARIES (2011) NOTE: There is comparatively less data for the new boundaries compared with the old boundaries PREVIOUS LSOA BOUNDARIES (2001) For 2011 Census data used in the 2001 Boundaries Atlas: For simplicity, where two or more areas have been merged, the figures for these areas have been divided by the number of LSOAs that used to make that area up. Therefore, these data are not official ONS statisitcs, but presented here as indicative to display trends. NB. It is currently not possible to export the map as a picture due to a software issue with the Google Maps background. We advise you to print screen to copy an image to the clipboard. IMPORTANT: Due to the large amount of data and areas, the LSOA Atlas may take up to a minute to fully load. Once loaded, the report will work more efficiently by using the filter tool and selecting one borough at a time. Displaying every LSOA in London will slow down the data reload. Tips: - Select a new indicator from the Data box on the left. Select the theme, then indicator and then year to show the data. - To view data just for one borough, use the filter tool. - The legend settings can be altered by clicking on the pencil icon next to the LSOA tick box within the map legend. - The areas can be ranked in order by clicking at the top of the indicator column of the data table. Beware of large file size for 2001 Boundary Atlas (58MB) alternatively download Zip file (21MB). Themes included in the atlases are Census 2011 population, Mid-year Estimates by age, Population Density, Households, Household Composition, Ethnic Group, Language, Religion, Country of Birth, Tenure, Number of dwellings, Vacant Dwellings, Dwellings by Council Tax Band, Crime (numbers), Crime (rates), Economic Activity, Qualifications, House Prices, Workplace employment numbers, Claimant Count, Employment and Support Allowance, Benefits claimants, State Pension, Pension Credit, Incapacity Benefit/ SDA, Disability Living Allowance, Income Support, Financial vulnerability, Health and Disability, Land use, Air Emissions, Energy consumption, Car or Van access, Accessibility by Public Transport/walk, Road Casualties, Child Benefit, Child Poverty, Lone Parent Families, Out-of-Work families, Fuel Poverty, Free School Meals, Pupil Absence, Early Years Foundation Stage, Key Stage 1, Key Stage 2, GCSE, Level 3 (e.g A/AS level), The Indices of Deprivation 2010, Economic Deprivation Index, and The IMD 2010 Underlying Indicators. The London boroughs are: City of London, Barking and Dagenham, Barnet, Bexley, Brent, Bromley, Camden, Croydon, Ealing, Enfield, Greenwich, Hackney, Hammersmith and Fulham, Haringey, Harrow, Havering, Hillingdon, Hounslow, Islington, Kensington and Chelsea, Kingston upon Thames, Lambeth, Lewisham, Merton, Newham, Redbridge, Richmond upon Thames, Southwark, Sutton, Tower Hamlets, Waltham Forest, Wandsworth, Westminster. These profiles were created using the most up to date information available at the time of collection (Spring 2014). You may also be interested in MSOA Atlas and Ward Atlas.

This data set was acquired with a Video Camera on the ROV Jason II during Melville expedition MGLN07MV conducted in 2006 (Chief Scientist: Dr. Charles Fisher). These data files are of Google Earth (KML/KMZ) format include photos and vehicle navigation information. Data were acquired as part of the project(s): Collaborative Research: Site evaluations and background studies of interactions among fluid chemistry, physiology, and community ecology for Ridge 2000 Lau Basin Integrated Studies and Bacterial population structure: evaluating gene flow in the symbionts of deep-sea mussels, and funding was provided by NSF grant(s): OCE02-40896, OCE02-40982, OCE02-40985, OCE02-41250, and OCE04-53901.

The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program. You can use the information to better understand your level of flood risk and type of flooding.The NFHL is made from effective flood maps and Letters of Map Change (LOMC) delivered to communities. NFHL digital data covers over 90 percent of the U.S. population. New and revised data is being added continuously. If you need information for areas not covered by the NFHL data, there may be other FEMA products which provide coverage for those areas.In the NFHL Viewer, you can use the address search or map navigation to locate an area of interest and the NFHL Print Tool to download and print a full Flood Insurance Rate Map (FIRM) or FIRMette (a smaller, printable version of a FIRM) where modernized data exists. Technical GIS users can also utilize a series of dedicated GIS web services that allow the NFHL database to be incorporated into websites and GIS applications. For more information on available services, go to the NFHL GIS Services User Guide.You can also use the address search on the FEMA Flood Map Service Center (MSC) to view the NFHL data or download a FIRMette. Using the “Search All Products” on the MSC, you can download the NFHL data for a County or State in a GIS file format. This data can be used in most GIS applications to perform spatial analyses and for integration into custom maps and reports. To do so, you will need GIS or mapping software that can read data in shapefile format.FEMA also offers a download of a KMZ (keyhole markup file zipped) file, which overlays the data in Google Earth™. For more information on using the data in Google Earth™, please see Using the National Flood Hazard Layer Web Map Service (WMS) in Google Earth™.

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights.

SW Map package: for mobile use, this enables users to visualize the site maps and boundaries on their own mobile. Together with the relevant files, users can also find a manual showing step by step how to copy files from their own computer to SW Map running on another portable device.

KMZ file: for desktop use, this enables users to visualize the site maps and boundaries on Google Earth. By adding or removing layers, it is possible to visualize each location assessed by NPM Baseline 10. These files are available on HDX.

Historical UAV imagery of Rohingya settlements in Cox Bazar in GIS, KML Google Earth, Mbtiles (SW Maps), format. Updates of imagery will be added on top of the list.

NPM has also produced individual packages by camps: - Please click [here] {https://data.humdata.org/dataset/iom-bangladesh-npm-drone-imagery-and-gis-package-by-camp-sept-oct-2018} to access the data by camp as of September 2018. - Please click here to access the data by camp as of August 2018. - Please click here to access the data by camp as of July 2018. - Please click here to access the data by camp as of June 2018. - Please click here to access the data by camp as of May 2018. - Please click here to access the data by camp as of April 2018.

All majhee blocks shapefiles are also available at the following link:

• Please click here to access the most current majhee block shapefiles, as well as all historical versions.

The Water Resources Agency's disaster emergency response team of the Ministry of Economic Affairs further combines real-time data such as rainfall, water level, and reservoir information with long-term disaster response experience and computer technology to provide reservoir alerts for the public and relevant units. This helps the public understand the risk of home flooding, prepare early, and reduce the occurrence of disasters. This dataset is linked to a Keyhole Markup Language (KML) file list. This format is a markup language based on the XML (eXtensible Markup Language) syntax standard, developed and maintained by Keyhole, a subsidiary of Google, to express geographic annotations. Documents written in the KML language are KML files, which use the XML file format and are used in Google Earth related software (Google Earth, Google Map, Google Maps for mobile...) to display geographic data (including points, lines, polygons, polyhedra, and models...). Many GIS-related systems now also use this format for the exchange of geographic data, and the fields and codes of this data are all in UTF-8.

Dear Scientist! This folder contains data collected due to conducting study: "Analysis of the route safety of abnormal vehicle from the perspective of traffic parameters and infrastructure characteristics with the use of web technologies and machine learning" funded by National Science Centre Poland (MINIATURA-5 grant reference 2021/05/X/ST8/01669). The structure of files is arising from the aims of the study and numerous of sources needed to tailor suitable data possible to use as an input layer for neural network. You can find a folders and files containing: road parameters data, Google Maps data, geocoding data for roads, population density data for roads, road incidents data, weather data for actual traffic of roads. More information in README file put in the main folder. Good luck with your study! Igor Betkier, PhD

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. –––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive.As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

The Ministry of Economic Affairs' Water Resources Agency's Disaster Emergency Response Team, utilizing long-term disaster response experience, further combines real-time data such as rainfall, water levels, and reservoir levels, through computer technology to provide water level alerts to the public and relevant units. This helps people understand the risk of home flooding, prepare early, and reduce the occurrence of disasters. This dataset is linked to a Keyhole Markup Language (KML) file list, which is a markup language based on the eXtensible Markup Language (XML) syntax standard, developed and maintained by Google's Keyhole company for expressing geospatial annotations. Documents written in the KML language are referred to as KML files and are used in Google Earth-related software (Google Earth, Google Map, Google Maps for mobile, etc.) for displaying geospatial data. Many GIS-related systems now also use this format for geospatial data exchange, and the KML of this data uses UTF-8 encoding.