Numerous visualisation methods have been proposed, including Origin-Destination maps to represent movement patterns gathered from social media; however, visual clutter remains a persistent issue due to complex data dimensionality. Besides, most Origin-Destination maps fail to illustrate the temporal dimension of social network phenomena within the geographical environment. To tackle this issue, we propose the visualisation method for geo-located Facebook social-media data while emphasising the time aspect. Based on the citizen-generated data for the European Union (EU), we estimated the EU citizens’ residing or travelling across the EU member states as a means of current and previous destinations to reveal the extent of the hypothetical human migration. The proposed methodology consists of Origin-Destination maps implemented within the time geography framework as a model to support the process of analysis for decision-making. The generated visualisation allows comprehension of the scale of human movement distribution internally within the EU from a space–time perspective.

Data is collected from Baidu Map (http://qianxi.baidu.com/). The migration scale index data, produced by the map service company of Baidu through the GPS location in mobile phone, record the number of migrants between any two cities. It visualises the migration flows in particular time periods. Although, it cannot capture all migrants, it is useful to conduct comparisons among different cities.

The data includes original child ladder migration data, intermediate, and output files to help produce flow map time series. Postgresql dump file includes the all birth events with years and locations. Migration data includes the medium products and the versions of the data.

Family trees contain information on individuals such as birth and death places and years, and kinship ties, e.g., parent-child, spouse, and sibling relationships. Such information makes it possible to construct population-scale trees and study population dynamics and migration over many generations and far into the past. Despite the recent advances, existing spatial and temporal abstraction techniques for space-time flow data have limitations due to the lack of knowledge about the effects of temporal partitioning on flow patterns and their visualization. In this study, we extract state-to-state migration patterns over a period between 1789 and 1924 from a set of cleaned, geocoded and connected family trees from Rootsweb.com. We use the child ladder approach, one that captures changes in family locations by comparing birthplaces and birthyears of consecutive siblings. Our study has two major contributions. First, we introduce a methodology to reveal patterns and trends for analyzing and mapping of migration across space and time using a family tree dataset. Specifically, we evaluate a series of temporal partitioning methods to capture how changes in temporal partitioning influence the results of patterns and trends. Second, we visualize longitudinal population mobility in the US using time-series flow maps. This is one of the first studies to uncover dynamic migration patterns on a larger spatial and temporal scale, than the more typical micro studies of individual movement. Our findings are reflective of the migration patterns of European descendants in the U.S., while native Americans, Blacks, Mexican populations are not represented in the data. [KC1]

[KC1]Need to discuss about this more in limitations, and maybe put in in the abstract and/or introduction. Since this is a methodological paper to map migration from trees, I don’t think we need to add this in the title.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Chatham County, GA (DISCONTINUED) (NETMIGNACS013051) from 2009 to 2020 about Chatham County, GA; Savannah; migration; flow; GA; Net; 5-year; and population.

A map and ranking of the highest-volume interstate moves such as California to Texas and New York to Florida.

Graph and download economic data for Net County-to-County Migration Flow (5-year estimate) for Carroll County, MD (DISCONTINUED) (NETMIGNACS024013) from 2009 to 2020 about Carroll County, MD; Baltimore; migration; flow; MD; Net; 5-year; and population.

This package contains data, filters and visualizations from Nilsson and Dokter et al. (2019).

Files

radar_metadata.csv: Metadata for the 84 European radars considered for this study. Includes radar code (odim_code = country + odim_code_3char and alternative radar code vp_radar), radar site location (location, latitude, longitude), radar site elevation (site_altitude_asl in meters above sea level) and radar altitude range used in this study (min_height_cut_asl and max_height_cut_asl in meters above sea level).

vp.zip: Vertical profiles of birds (vp) data, processed from the radar volume data following procedures described by Dokter et al. (2011), using the vol2bird algorithm in the R package bioRad. Zip file includes vp data for the 84 European radars considered for this study from September 19 to October 9, 2016 (21 days). This time period is characterized by strong passerine migration throughout Europe. Files are organized in radar (= odim_code), date and hour directories and follow the ODIM bird profile format specification. Data can be read with the R package bioRad.

vp_processing_settings.yaml: Data selection setting for this study, based on data quality criteria. File lists for each radar the altitudes to include (include_heights), time periods to exclude (exclude_datetimes) and reasons for exclusion (comments). 70 of the 84 radars were retained after filtering.

vp_processed_70_radars_20160919_20161009.csv: Processed vp data for 70 radars. Is the result of processing vp.zip with vp_processing_settings.yaml and radar_metadata.csv using vp-processing (Desmet & Nilsson 2018). Note: includes all timestamps: day and night & those marked for exclusion (marked in exclusion_reason). This data file forms the basis for analysis in the study.

Headers are:

radar_id: odim_code of the radar

datetime: timestamp

HGHT: lower altitude of altitude bin (m above sea level)

u: bird ground speed towards east (m/s)

v: bird ground speed towards north (m/s)

dens: bird density (birds/km3)

dd: bird flight direction (degrees from north)

ff: bird ground speed (m/s)

DBZH: reflectivity factor (dBZ) in horizontal polarisation

mtr: migration traffic rate (birds/km/h)

day_night: timestamp occurs during day or night (based on sunrise/sunset)

date_of_sunset: date at sunset, with night timestamps between midnight and sunrise belonging to the previous date

exclusion_reason: reason timestamp is excluded in vp_processing_settings.yaml (if applicable). Excluded timestamps have NA values for u, v, dens, dd, ff, DBZH, and mtr.

vp_flowviz.csv: Input data for visualizations. Is the result of processing vp_processed_70_radars_20160919_20161009.csv using vp-to-flowviz.Rmd in vp-processing (Desmet & Nilsson 2018). Aggregates data in hourly bins for 200-2000m (altitude_band = 1) and above (altitude_band = 2). Only altitude band 1 is used in visualizations.

flowviz.mov: Screencast of vp_flowviz.csv visualized with Bird migration flow visualization v2 (Desmet et al. 2016, Shamoun-Baranes et al. 2016). The visualization extrapolates the migration over the entire sampling range (cropped in the screencast due to technical limitations and thus excluding the Bulgarian radar), not taking topography or water bodies into account, and shows the ground speed (length of arrows) and direction of migration over time. Note that density is not shown: low density movements can therefore appear as strong as high density movements when ground speeds are similar.

cartoviz.mov: Screencast of vp_flowviz.csv visualized as an interactive map with CARTO. Visualization shows migration density (size of circles) and mean direction (colour) over time. The interactive map is available at https://inbo.carto.com/u/lifewatch/builder/8685140f-8d8c-4d06-9e1e-25d051d43748/embed.

Patterning of functional blood vessel networks is achieved by pruning of superfluous connections. The cellular and molecular principles of vessel regression are poorly understood. Here we show that regression is mediated by dynamic and polarized migration of endothelial cells, representing anastomosis in reverse. Establishing and analyzing the first axial polarity map of all endothelial cells in a remodeling vascular network, we propose that balanced movement of cells maintains the primitive plexus under low shear conditions in a metastable dynamic state. We predict that flow-induced polarized migration of endothelial cells breaks symmetry and leads to stabilization of high flow/shear segments and regression of adjacent low flow/shear segments.

The project leads for the collection of most of this data were Heiko Wittmer, Christopher Wilmers, Bogdan Cristescu, Pete Figura, David Casady, and Julie Garcia. Mule deer (82 adult females) from the Siskiyou herd were captured and equipped with GPS collars (Survey Globalstar, Vectronic Aerospace, Germany; Vertex Plus Iridium, Vectronic Aerospace, Germany), transmitting data from 2015-2020. The Siskiyou herd migrates from winter ranges primarily north and east of Mount Shasta (i.e., Shasta Valley, Red Rock Valley, Sheep Camp Butte, Sardine Flat, Long Prairie, and Little Hot Spring Valley) to sprawling summer ranges scattered between Mount Shasta in the west and the Burnt Lava Flow Geological Area to the east. A small percentage of the herd were residents. GPS locations were fixed between 1-2 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 67 migrating deer, including 167 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 12.09 days and 41.33 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Due to often produced BBMM variance rates greater than 8000, separate models using BBMMs and fixed motion variances of 1000 were produced per migration sequence and visually compared for the entire dataset, with best models being combined prior to population-level analyses (62 percent of sequences selected with BMMM). Winter range analyses were based on data from 66 individual deer and 111 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer, greater than or equal to 4 deer (10 percent of the sample), and greater than or equal to 7 deer (20 percent of the sample) representing migration corridors, medium use corridors, and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2 were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50th percentile contour of the winter range utilization distribution.

The Channel Migration Potential (CHAMP) layer contains stream networks of Western Washington (and much of Western Oregon) with associated data and information important for assessing channel migration activity. It also features information on channel characteristics such as stream flow and physical dimensions. This data layer’s main feature is a classification of channel migration potential based on channel confinement and erosion potential. The layer was derived from existing statewide geospatial datasets and classified according to channel migration measurements by the High Resolution Change Detection (HRCD) project for the Puget Sound Region (Washington Department of Fish and Wildlife, 2014). While the layer identifies the potential for channel migration, it does not predict channel migration rates. Thus, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The tool helps plan and prioritize floodplain management actions such as Channel Migration Zone mapping, erosion risk reduction, and floodplain restoration. The background, use, and development of the CHAMP layer are fully described in Ecology Publication 15-06-003 (full report citation and URL below). That report also describes visual assessment techniques that should be used along with the CHAMP layer to assess channel migration potential. Legg, N.T. and Olson, P.L., 2015, Screening Tools for Identifying Migrating Stream Channels in Western Washington: Geospatial Data Layers and Visual Assessments: Washington State Department of Ecology Publication 15-06-003, 40 p. https://fortress.wa.gov/ecy/publications/SummaryPages/1506003.htmlThe tool developers would like to thank the following people for their contribution to this work: • Brian D. Collins (University of Washington) • Jerry Franklin (Washington Department of Ecology) • Christina Kellum (Washington Department of Ecology) • Matt Muller (Washington Department of Fish and Wildlife) • Hugh Shipman (Washington Department of Ecology) • Terry Swanson (Washington Department of Ecology) This project has been funded wholly or in part by the United States Environmental Protection Agency under Puget Sound Ecosystem Restoration and Protection Cooperative Agreement Grant PC-00J27601 with Washington Department of Ecology. The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.Generally, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The data resolution does not allow one to predict channel migration. The classification identifies stream segments for further examination, and those that likely require limited attention or analysis. The potential uncertainty involved in the classification approach is a reason for the visual assessment techniques (described below in Ecology Publication 15-06-003) being described along with the CHAMP data layer.

The Passage Assessment Database (PAD) geospatial file contains locations of known and potential barriers to salmonid migration in California streams with additional information about each record. The PAD is an ongoing map-based inventory of known and potential barriers to anadromous fish in California, compiled and maintained through a cooperative interagency agreement. The PAD compiles currently available fish passage information from many different sources, allows past and future barrier assessments to be standardized and stored in one place, and enables the analysis of cumulative effects of passage barriers in the context of overall watershed health. The database is set up to capture basic information about each potential barrier. It is designed to be flexible. As the database grows, other modules may be added to increase data detail and complexity. For the PAD to be useful as a restoration tool, the data within the PAD need to accurately depict the on-the ground reality of fish passage constraints. This requires the PAD to retrieve new barrier data and updates to existing sites and to have verified and vetted the information it receives. In 2013, new PAD data standards were designed to standardize this process, and refine the data in PAD making the data more robust. The standard is available online at: https://nrmsecure.dfg.ca.gov/FileHandler.ashx?DocumentID=78802. The new standards have been implemented for all new records since 2013. In the future, the new standards will be implemented for all existing records. If after reading the metadata, additional details about the PAD project are needed, please visit the CalFish website at www.calfish.org/PAD or refer to the PAD Methodology document at http://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=19037. To send comments about data issues, corrections, edits or to map a new barrier location not yet reported in the PAD, please use the PAD Online Data Review Application: https://map.dfg.ca.gov/pad/ or send an email to: Anne.Elston@wildlife.ca.gov. Preferred citation: California Department of Fish and Wildlife, Passage Assessment Database, May 2016 Version.

Components of international migratory increase, quarterly: immigrants, emigrants, returning emigrants, net temporary emigrants, net non-permanent residents.

As part of a Channel Migration Zone Study, GeoEngineers, Inc. was contracted by Pierce County, Public Works Surface Water Management formerly “Water Programs” Division to create a series of shapefiles including the SPC_Migration_Potential_Areas.shp. Pierce_Migration_Potential_Areas.shp combines the include severe, moderate or low migration potential areas. GeoEngineers, Inc. completed migration potential studies of the White, Puyallup and Carbon Rivers (completed 2003 adopted 2005), South Prairie Creek (completed 2005 adopted 2017) and Upper Nisqually River (completed 2007 adopted 2017). These were accepted by SWM and Adopted by County Council.The MPA delineation involved identifying severe, moderate and low migration potential areas within the delineated CMZ. The MPA delineation approach is similar to that employed in our CMZ analysis; that future rates and character of migration will be similar to those of the past for similar water discharges, sediment influx, and debris entrainment conditions. This analysis was also based on the absence of levees, revetments and other confining structures. The width of each MPA was measured, based on delineation criteria developed specifically for this project, and then adjusted to accommodate geomorphic conditions not accounted for in the maximum migration rates. Criteria developed for mapping severe, moderate and low MPA are provided in the following paragraphs: Severe MPA includes the area lying inside the HCOT, and an area immediately outside the HCOT boundary equivalent to a distance the channel could travel in a specified period. The extent of the Severe Migration Potential Area outside the HCOT boundary is determined by two criteria. The first criterion is the distance the outside channel edge could travel in 10 years of steady lateral migration away from the HCOT boundary (Maximum lateral migration rates multiplied by a ten- year period). The second is defined by the distance the outside channel edge could travel in storm single event (i.e. maximum overnight rate) from the current channel position (2002). The landward most boundary of the two criteria defines the Severe Migration Potential Area.Moderate MPA includes areas adjacent to the outside edge of the severe migration potential area. The width of the moderate migration potential area is determined by the distance the outside channel edge could travel in five years (for South Prairie Creek 10 years) of steady lateral migration beyond the outside edge of the severe migration potential area. The CMZ boundary will serve as the outside edge of the moderate migration potential boundary at sites where the distance between the severe migration potential boundary and the CMZ boundary represents less the five years (for South Prairie Creek 10 years)of steady lateral migration. Moderate migration potential areas are not included at sites where the outside edge of the severe migration potential area is determined by the location of the CMZ boundary. The rate of migration used in the calculation is the maximum average rate of migration for each geomorphic reach (measured as described above). In some places the width of the Moderate Migration Potential Area may be modified based on geologic interpretation, professional judgment. Low MPA includes areas adjacent to the outside edge of the moderate migration potential area. The extent of the Low Migration Potential Area beyond the moderate migration potential boundary will be determined by CMZ boundary, as determined by our CMZ evaluation. Low migration potential areas will not be included at sites where the outside edge of either a severe or moderate migration potential area is determined by the location of the CMZ boundary. The most common adjustments typically involved widening the moderate MPA to include ancient abandoned channel deemed capable of arresting main stem flow in an avulsion event. Other common Moderate MPA adjustments involved increasing or decreasing the base width to accommodate the following conditions; The presence of native erosion resistant bank materials, such as the Osceola Mudflow or local downstream or oblique direction of meander bend migration.

Please read metadata for additional information (https://matterhorn.co.pierce.wa.us/GISmetadata/pdbswm_regulated_cmz_floodway_all.html). Any data download constitutes acceptance of the Terms of Use (https://matterhorn.co.pierce.wa.us/Disclaimer/PierceCountyGISDataTermsofUs (pdf).

Steelhead (Oncorhynchus mykiss) and other Pacific salmon are threatened by unsustainable levels of harvest, genetic introgression from hatchery stocks and degradation or loss of freshwater habitat. Projected climate change is expected to further stress salmon through increases in stream temperatures and altered stream flows. We demonstrate a spatially explicit method for assessing salmon vulnerability to projected climatic changes (scenario for the years 20302059), applied here to steelhead salmon across the entire Pacific Northwest (PNW). We considered steelhead exposure to increased temperatures and more extreme high and low flows during four of their primary freshwater life stages: adult migration, spawning, incubation and rearing. Steelhead sensitivity to climate change was estimated on the basis of their regulatory status and the condition of their habitat. We assessed combinations of exposure and sensitivity to suggest actions that may be most effective for reducing steelhead vulnerability to climate change. Our relative ranking of locations suggested that steelhead exposure to increases in temperature will be most widespread in the southern Pacific Northwest, whereas exposure to substantial flow changes will be most widespread in the interior and northern Pacific Northwest. There were few locations where we projected that steelhead had both relatively low exposure and sensitivity to climate change. Synthesis and applications. There are few areas where habitat protection alone is likely to be sufficient to conserve steelhead under the scenario of climate change considered here. Instead, our results suggest the need for coordinated, landscape-scale actions that both increase salmon resilience and ameliorate climate change impacts, such as restoring connectivity of floodplains and high-elevation habitats. Stream flow and temperature gridded data for PNW.

In order to better describe both ancient and contemporary migratory flows associated with the North Atlantic abyssal fauna as part of the EC H2020 iAtlantic project, this dataset provides a collection of connectivity maps for several engineer species from hydrothermal springs on the Mid-Atlantic Ridge, from mussels of cold seeps on both sides of the Atlantic and, from deep-water corals in the NE Atlantic. These maps and the associated migrant flow matrices are derived from several demogenetic model analyses (dadi, moments and divmigrate) using multi-locus genotype data derived from a sub-sampling of the genomes of these target species. For each species, the dataset includes a series of shapefiles with a pdf map, the file of the geographic coordinates of the studied localities with a flow matrix, as well as the file of the multi-locus genotypes used to carry out the genetic analysis of the populations and establish the migratory flows.

Abstract

This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. The metadata was not provided by the data supplier and has been compiled by the programme based on known details.

The main objectives of the Clarence-Moreton *SEEBASETM and GIS Project study were to provide the NSW Department of Primary Industries with an integrated regional interpretation of the basement composition, lithology, structure and depth of the Clarence-Moreton Basin in New South Wales. This included the construction of a depth to basement image (SEEBASE™) for the area.

The effects of the basement geology on the evolution of the Clarence-Moreton Basin (both onshore and offshore) and of its precursors, the Esk Trough and the Ipswich Basin have been investigated. Attention was focused on the formation and reactivation of the basin controlling structures. The evolution of these structures has been evaluated in the light of the different tectonic events that have affected the area.

Maturity and fluid flow migration maps derived from SEEBASETM grids indicate that the central axis of the onshore depocentres and parts of the offshore basin are mature for present-day oil generation. However, these maps probably underestimate the maturity along the eastern margin of the basin, due to significant uplift and erosion that occurred during the Cenomanian. Such areas are likely to be mature for hydrocarbon generation provided adequate source rocks are present at depth.

Available gravity and magnetic data have been reprocessed and enhanced with an extensive set of wavelength and amplitude filters. An ArcMap 9.0 GIS product has been constructed that includes all structural interpretations, as well as the potential field data.

The revised and expanded interpretation of the structure and basin architecture in the area of the Clarence-Moreton Basin provides an improved understanding of basin evolution in the region, which will contribute to the reduction of exploration risks in the area.

[Taken from executive summary of report cited in History]

This dataset has been provided to the BA Programme for use within the programme only. Third parties should contact the NSW Department of Industry. http://www.industry.nsw.gov.au/

Dataset History

No specific metadata file or history statement provided. See MR707_report.pdf in 'seebase&strucuralGisProject_cla-mor_nsw-dpi\Report' directory.

Dataset Citation

NSW Department of Primary Industries (2014) Clarence-Moreton SEEBASE & Structural GIS Project data.. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/b1690f8b-4025-45d2-96a0-6feb03ff3e52.

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

Hurricane Katrina of August, 2005, is remembered as one of the most destructive and influential storms in United States history. The densely populated city of New Orleans, one of many areas around the Gulf Coast to face catastrophic damage, endured extreme flooding and physical destruction when several levees and other flood prevention features guarding the city broke down. Many evacuated the city and fled to far corners of the country, and a large portion of these evacuees were unable to resettle in New Orleans after the storm. Dealing with the aftermath of Hurricane Katrina involved many immense challenges, but ten years later, one can see the effects of a decade of hard work in restoring this historic city. This series of maps tracks New Orleans through these ten years of change. The story map uses the Esri Story Map Series app, The story was produced by Esri in collaboration with the Smithsonian Institution. The story can also be found on the Smithsonian Website. Data for each map was taken from the following sources:Katrina Diaspora: 2006 American Community Survey 1-year Estimates, State-to-State Migration Flows, NHC, NOAA, NWS. Flooding: Terrestrial lidar datasets of New Orleans levee failures from Hurricane Katrina, August 29, 2005: U.S. Geological Survey Data Series, NASA Earth Observatory, NOAA National Geodetic Survey. Physical Damage: FEMA dataset collection following Hurricane Katrina and transferred to CNO/SHPOPopulation Shift: The Data Center analysis of data from U.S. Census 2000 Summary File 1 (SF1) and U.S. Census 2010 Summary File 1 (SF1)Steady Restoration: The Data Center analysis of Valassis Residential and Business Database Neighborhood Reference Map: City of New Orleans GIS Department For more information on Esri Story Map apps, visit storymaps.arcgis.com.

The data and documentation in this study is an innovation project that is a part of "PROTECT The Right to International Protection: A Pendulum between Globalization and Nativization?". This is an EU-funded research project launched on 1 February 2020. The impacts of the UN's Global Compacts on Refugees and Migration are studied, which are two non-binding frameworks promoting international cooperation and responsibility-sharing as key solutions to handle global refugee flows. By studying how the Compacts are received and implemented in different countries, and how they interact with existing legal frameworks and governance architectures, the Compacts' impact on refugees' right to international protection is investigated.

More specifically, this database maps the different policy tools of the external dimension of the Common European Asylum System (CEAS) and of the Asylum policy of the EU. The information gathered in this database concerns the policy toolbox relating to the external dimension of the EU’s asylum and migration policy. Said toolbox is also displayed on an interactive map available to the public by following the link: https://umap.openstreetmap.fr/en/map/eu-external-migration-policy-tools_584227#4/30.33/41.13. The data is collected from open secondary sources and legal texts.

Licensed under Creative Commons Attribution 4.0 International.