VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

Under the direction of University College London (UCL), this international, multidisciplinary project assessed the feasibility of using non-destructive digital imaging technology to make texts visible in images of papyrus in Ancient Egyptian mummy case cartonnages for open research and analysis. Our pilot project has led to an understanding of which imaging modalities are worth pursuing in future research projects. The massive finding of papyri in Egypt between the end of the 19th and the beginning of the 20th century has dramatically increased our knowledge of the ancient world. The recovering of new texts has brought to light classical and biblical literature, and everyday writing of people that have changed the way we interpret antiquity. Papyri were and still are found in two main ways: in situ, i.e. where they were left by the ancients, or recycled for fabricating other objects such as mummy masks and coverings, book binding and other kinds of what scholars broadly define as 'cartonnage.' Papyri were also used sometimes to stuffing animal mummies. In the past, the awareness that such ancient objects could be filled with manuscripts has led papyrologists to destroy cartonnage, mummy masks and other material for retrieving their contents. With the passing of decades, specialists' recognition of the problems connected with such practice has increased, and new, less invasive techniques have been developed in order to avoid the destruction of important historical evidence. The decision to eventually dismount cartonnage involves careful evaluations of the pros and cons and of the methods to be followed. Besides papyrologists, conservators and other specialists, the practice of dissolving cartonnage in order to retrieve papyri has been employed by dealers and collectors seeing the opportunity to multiply their earnings or simply looking for manuscripts without recognizing the issues involved with the destruction of ancient artefacts. In these cases, the damage produced to our cultural heritage is even greater since little if any attention to the methods employed and to the recording of the process is paid. The application of advanced imaging techniques has the potential to dramatically improve our study of papyri encapsulated in ancient artefacts and will potentially solve the problem of invasive, destructive approaches to the remains of our ancient past. This exploratory, pilot project, working with a range of international partners and collections between November 2015 and December 2017, tested the feasibility of non-destructive imaging of multi-layered Papyrus found in Egyptian mummy cartonnages. Our research has shown that no current single imaging technique can identify both iron and carbon based inks at depths within cartonnage. If we are to detect and ultimately read text within cartonnage, a multimodal imaging approach is required, but this will necessarily be limited by cost, access to imaging systems, and the portability of both the system and the cartonnage. We are currently in the process of publishing lessons-learned on findings and imaging methodologies for further research, including on affordances and limitations of specific imaging approaches, and how they can be used in tandem to recover extant text within layers of cartonnage. This data is hosted by UCL Research Data Repository for public access and use. All images are licensed for use under Creative Commons 0 1.0 Universal License.

This data set comprises a core content set of digital images, analytical data and technical reports on the imaging and analysis of mummy mask cartonnage and modern surrogates. These are intended for access by researchers, scholars, students and the general public. The data set contains the following folders organized by imaging method:

Documentation.7z contains documentation, metadata, photographs and reports for each modality (151MB). Data_FiberOpticReflectanceSpectroscopy.7z is Fiber Optic Reflectance Spectroscopy Data from testing conducted by Equipoise Imaging (30MB) Data_OpticalCoherenceTomography.7z is Optical Coherence Tomography Data from imaging conducted in the Duke University Eye Center and Department of Biomedical Engineering. (619MB) Data_Terahertz.7z is Terahertz Data from experimental imaging at the University of Western Australia (1MB) Data_Xray.7z contains XRF data from the SLAC Stanford Synchrotron Radiation Lightsource in California and "Micro-CT ALS Berkeley" data from the Lawrence Livermore National Laboratory Advanced Light Source in California. (21.3GB). ImageData_RBT.7z - Multispectral imaging data from RB Toth Associates at Duke University and University of California at Berkeley, with processed images of US and UCL images. (31 GB.) UCBsn_LC.7z - Data from multispectral imaging at the University of California at Berkeley s.n. cartonnage fragment by the Library of Congress before and after x-ray of the fragment for damage assessment (2.1GB) UCL_Digital_Humanities.7z - Data from multispectral imaging of the UCL Phantom surrogates and Petrie Museum cartonnage UC806037i in the UCL Centre for Digital Humanities, London. (22.6GB) UManchester_JohnRylands: Data from multispectral imaging of both sides of cartonnage Greek P458 P458 at the University of Manchester John Rylands Library. (5.5GB)

README files with more specific information are included with the data set from each imaging modality. This data was first shared online in July 2017. It was moved to its current location and assigned a doi in November 2022.