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.

Context

Found this dataset while searching for immigrant movement to different countries. It is a UN dept of Social and Economic Affairs. Refer to migflows2015documentation.pdf to getter deeper and clear understanding of data. Pdf in data explorer.

Content

The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data available from 45 countries. Skip the first 20 rows to access the tabular content.

Acknowledgements

Thanks to UN department of Social and Economic Affairs. All right reserved by the department. For viewing original site click here .

Have Fun. Would love to see lot of beautiful visualizations.

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.

Migration Summary (2011-2020) Infographic to be embedded in 2022 BBTN Migration Story Map. Data for maps and tables was retrieved from: Internal Revenue Service, Statistics of Income Division Migration Data, 2011 - 2020.

Migration data serves various purposes, including urban planning, resource allocation, public health, economic analysis, and policymaking. It aids in understanding population trends, labor market dynamics, refugee resettlement, demographic shifts, and social integration. Additionally, it informs international relations, humanitarian aid efforts, climate change adaptation strategies, and academic research in fields such as sociology, economics, geography, and public policy.

This dataset contains 10,000 synthetic records simulating the migratory behavior of various bird species across global regions. Each entry represents a single bird tagged with a tracking device and includes detailed information such as flight distance, speed, altitude, weather conditions, tagging information, and migration outcomes.

The data was entirely synthetically generated using randomized yet realistic values based on known ranges from ornithological studies. It is ideal for practicing data analysis and visualization techniques without privacy concerns or real-world data access restrictions. Because it’s artificial, the dataset can be freely used in education, portfolio projects, demo dashboards, machine learning pipelines, or business intelligence training.

With over 40 columns, this dataset supports a wide array of analysis types. Analysts can explore questions like “Do certain species migrate in larger flocks?”, “How does weather impact nesting success?”, or “What conditions lead to migration interruptions?”. Users can also perform geospatial mapping of start and end locations, cluster birds by behavior, or build time series models based on migration months and environmental factors.

For data visualization, tools like Power BI, Python (Matplotlib/Seaborn/Plotly), or Excel can be used to create insightful dashboards and interactive charts.

Join the Fabric Community DataViz Contest | May 2025: https://community.fabric.microsoft.com/t5/Power-BI-Community-Blog/%EF%B8%8F-Fabric-Community-DataViz-Contest-May-2025/ba-p/4668560

Description

This dataset provides a comprehensive record of missing migrants and their tragic journeys towards international destinations , collected by the Missing Migrants Project, an initiative implemented by the International Organization for Migration (IOM) since 2014. The dataset documents deaths and disappearances, shedding light on the challenges migrants face during their journeys. Please note that due to the complexities of data collection, the figures presented are likely an undercount. The dataset serves as a tribute to the individuals who lost their lives, as well as the families and communities impacted by their absence.

Key Features:

• Incident Type: Type of migration incident
• Incident Year: Year when the incident occurred
• Reported Month: Month when the incident was reported
• Region of Origin: Geographical region where the migrants originated
• Region of Incident: Geographical region where the incident occurred
• Country of Origin: Country from which the migrants originated
• Number of Dead: Number of confirmed deceased migrants
• Minimum Estimated Number of Missing: Minimum estimated count of missing migrants
• Total Number of Dead and Missing: Total count of both deceased and missing migrants
• Number of Survivors: Number of migrants who survived the incident
• Number of Females: Number of female migrants involved
• Number of Males: Number of male migrants involved
• Number of Children: Number of children migrants involved
• Cause of Death: Cause of death for the migrants
• Migration Route: Route taken by migrants during their journey (if available)
• Location of Death: Approximate location where the incident occurred
• Information Source: Source of information about the incident
• Coordinates: Geographical coordinates of the incident location
• UNSD Geographical Grouping: Geographical grouping according to the United Nations Statistics Division

Potential Use Cases:

• Migration Patterns Analysis: Explore trends and patterns in migration incidents to understand the most affected regions and routes.
• Gender and Age Analysis: Investigate the demographics of migrants to identify gender and age-related vulnerabilities.
• Survival and Mortality Analysis: Analyze survival rates and causes of death to highlight risks and challenges migrants face.
• Temporal Analysis: Examine incidents over time to identify any temporal patterns or changes.
• Geospatial Analysis: Utilize geographical coordinates to map migration routes and incident locations.

If you find this dataset valuable, your support through votes is highly appreciated! ❤️ Thank you 🙂

Information on user control functionality and terminology used in web interface in parentheses. Hyperlinks to online visualizations are provided.

Author: P Ofstedal, educator, Minnesota Alliance for Geographic EducationGrade/Audience: high schoolResource type: lessonSubject topic(s): migration, population, mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.

Standard 3. Places have physical characteristics (such as climate, topography and vegetation) and human characteristics (such as culture, population, political and economic systems).

Standard 5. The characteristics, distribution and migration of human populations on the earth’s surface influence human systems (cultural, economic and political systems).Objectives: Students will be able to:

1. Analyze migration patterns into and out of their county and explain why these patterns exist.
2. Analyze the age of local in- and out-migrants and explain why these patterns exist.Summary: Students will investigate current in-migration and out-migration patterns in their county through two map websites.

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

The United Nations Department of Social affairs and Economic data website contains annual data on the flows of international immigrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. The current version presents data pertaining to 45 countries. This dataset focus on the Australian immigration data and is a part of International migration flows to and from selected countries - The 2015 revision.

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.

Debates about migration are often in the news. People quote numbers about how many people are entering and leaving different countries. Governments need to plan and manage public resources based on how their own populations are changing.

Informed discussions and effective policymaking rely on good migration data. But how much do we really know about migration, and where do estimates come from?

In this article, I look at how countries and international agencies define different forms of migration, how they estimate the number of people moving in and out of countries, and how accurate these estimates are.

Migrants without legal status make up a small portion of the overall immigrant population. Most high-income countries and some middle-income ones have a solid understanding of how many immigrants live there. Tracking the exact flows of people moving in and out is trickier, but governments can reliably monitor long-term trends to understand the bigger picture.

Who is considered an international migrant? In the United Nations statistics, an international migrant is defined as “a person who moves to a country other than that of his or her usual residence for at least a year, so that the country of destination effectively becomes his or her new country of usual residence”.1

For example, an Argentinian person who spends nine months studying in the United States wouldn’t count as a long-term immigrant in the US. But an Argentinian person who moves to the US for two years would. Even if someone gains citizenship in their new country, they are still considered an immigrant in migration statistics.

The same applies in reverse for emigrants: someone leaving their home country for more than a year is considered a long-term emigrant for the country they’ve left. This does not change if they acquire citizenship in another country. Some national governments may have definitions that differ from the UN recommendations.

What about illegal migration? “Illegal migration” refers to the movement of people outside the legal rules for entering or leaving a country. There isn’t a single agreed-upon definition, but it generally involves people who breach immigration laws. Some refer to this as irregular or unauthorized migration.

There are three types of migrants who don’t have a legal immigration status. First, those who cross borders without the right legal permissions. Second, those who enter a country legally but stay after their visa or permission expires. Third, some migrants have legal permission to stay but work in violation of employment restrictions — for example, students who work more hours than their visa allows.

Tracking illegal migration is difficult. In regions with free movement, like the European Union, it’s particularly challenging. For example, someone could move from Germany to France, live there without registering, and go uncounted in official migration records.2 The rise of remote work has made it easier for people to live in different countries without registering as employees or taxpayers.

The project leads for the collection of these data were Shelly Blair (CDFW) and Jerrod Merrell (University of Nevada Reno). Mule deer (52 adult females) from the Pacific herd were captured and equipped with store-onboard GPS collars (Vectronic Plus Vertex Survey Iridium), transmitting data from 2015-2020. Pacific mule deer are found on the western slope of the Sierra Nevada in eastern California and exhibit largely traditional seasonal migration strategies. This population migrates from a multitude of lower elevation areas in the foothills of El Dorado National Forest in winter westward into higher elevation summer ranges. Migrants vary in their movements from shorter (6 km) to longer (41 km) distances.GPS locations were fixed between 1-13 hour intervals in the dataset. To improve the quality of the dataset, the GPS data were filtered prior to analysis to remove locations which were 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 43 migrating deer, including 149 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 7.79 days and 26.72 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. Corridors and stopovers were best visualized using a fixed motion variance of 500 per sequence. Winter range was processed with a fixed motion variance of 1000. All products were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours. Winter range analyses were based on data from 32 individual deer and 54 wintering sequences. Winter range designations for this herd may expand with a la

Pan-European coastline-migration map at zoomable scale. The map is collated and harmonized from field-monitoring data and aerial photography provided by partners of EMODnet Geology. Where no such coastline-migration data were available, information from the EUROSION project is provided. For remaining gaps, please consult the coastline-migration map based on satellite data. The main attributes denote degree of landward (by erosion or submergence) or seaward (by accretion or emergence) change. In the visualization provided, three classes are distinguished: landward migration, stable coastline, seaward migration. The criterion for stable coastlines is ≤0.5 meter net change per year over a 10-year period. The current version was finalized in January 2021.

This document describes the dataset used for the interactive visualization.

Original data compiled via a survey with national experts (social policy and migration scholars) across EU27. Experts were asked to consult the national and international legislation regulating migrants’ access to welfare entitlements and provide objective information (as stipulated in the text of the law) regarding the specific eligibility conditions under which individuals can claim different types of benefits. The survey included standardized questions to ensure comparability across countries.

Codebook and xml along DDI standard.

West Africa’s vulnerability to climate change is influenced by a complex interplay of socio-economic and environmental factors, exacerbated by the region’s reliance on rain-fed agriculture. Climate variability, combined with rapid population growth, intensifies existing socio-economic challenges. Migration has become a key adaptive response to these challenges, enabling communities to diversify livelihoods and enhance resilience. However, spatial patterns of migration in response to climate risks are not fully understood. Thus, the study evaluates the applicability of the IPCC risk assessment framework to map and predict migration patterns in Ghana and Nigeria, with a focus on identifying areas of potential out-migration. By integrating geospatial environmental, socio-economic, and population data, the study highlights areas that have a higher likelihood of migration for the current baseline and near future (2050). Future climate is modeled using CMIP6 projections under the RCP4.5 scenario, while population projections providing insight into future exposure. The results from the baseline assessment are compared with actual migrant motivations, providing a ground-level perspective on migration drivers. In northern Ghana and Nigeria, elevated hazard, vulnerability, and exposure scores suggest a higher likelihood of migration due to the overall risk faced by the population. This pattern is projected to persist in the future. However, migrant responses indicate that environmental factors often play a secondary role, with vulnerability factors cited more frequently as migration drivers. The findings highlight the importance of developing localized adaptation strategies that address the specific needs of vulnerable areas. Additionally, management strategies that enhance community resilience and support sustainable migration pathways will be critical in addressing future climate-induced migration challenges.

Conservation of migratory birds requires improved understanding of the distribution of and threats to their migratory habitats and pathways. Wind energy development poses a potential threat, which may be reduced if facilities avoid or mitigate impacts in migration concentration areas. However, a current lack of information on the distribution of migratory concentration areas in the western U.S. impedes proactive planning. The Wyoming Natural Diversity Database (WYNDD) and The Nature Conservancy (TNC) developed deductive models of migratory bird concentration areas. Models were based on a synthesis of existing literature and expert knowledge concerning bird migration behavior and ecology, represented through GIS datasets, and validated using expert ratings and known occurrences. Our results were migration maps for four functional groups: raptors, wetland birds, riparian birds, and sparse grassland birds. Key factors included in migration models differed among the four groups, but included streams, topography, wind patterns, wetland size, forage availability, flyway location, proximity to streams, and vegetation type and structure. Experts rated all models as good or very good, and there was significant agreement between species occurrence data and the migration models for all groups except raptors. Our maps provide data to companies and agencies planning Wyoming wind developments. Our approach could be replicated elsewhere to fill critical data gaps and better inform conservation priorities and wind development planning.