The population density picture of Boston is generally a story of two Bostons: the high density central and northern neighborhoods, and the low density southern neighborhoods.The highest density areas of Boston are particularly concentrated in Brighton, Allston, and the Fenway area, areas of the city with large numbers of college students and young adults. There is also high population density in areas such as the Back Bay, the South End, Charlestown, the North End, and South Boston. These are all relatively small areas geographically, but have housing stock conducive to population density (e.g. multi-family dwelling units, row housing, large apartment buildings). The southern neighborhoods, specifically Hyde Park and West Roxbury, have significant numbers of people living in them, but lots sizes tend to be much larger. These areas of the city also tend to have more single family dwelling units. In that, there are fewer people per square mile than places north in the city. Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, areas of highest density exceed 30,000 persons per square kilometer. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.How to make this map for your city

2020 Census data for the city of Boston, Boston neighborhoods, census tracts, block groups, and voting districts. In the 2020 Census, the U.S. Census Bureau divided Boston into 207 census tracts (~4,000 residents) made up of 581 smaller block groups. The Boston Planning and Development Agency uses the 2020 tracts to approximate Boston neighborhoods. The 2020 Census Redistricting data also identify Boston’s voting districts.

For analysis of Boston’s 2020 Census data including graphs and maps by the BPDA Research Division and Office of Digital Cartography and GIS, see 2020 Census Research Publications

For a complete official data dictionary, please go to 2020 Census State Redistricting Data (Public Law 94-171) Summary File, Chapter 6. Data Dictionary. 2020 Census State Redistricting Data (Public Law 94-171) Summary File

2020 Census Tracts In Boston

2020 Census Block Groups In Boston

Boston Neighborhood Boundaries Approximated By 2020 Census Tracts

Boston Voting District Boundaries

Chart and table of population level and growth rate for the Boston metro area from 1950 to 2025.

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Feature layer generated from running the Plan Routes solution.

Measurements of currents and other oceanographic properties were made to assess the impact of sewage discharge from the proposed outfall site. cdm_data_type=TimeSeries cdm_timeseries_variables=station_id COMPOSITE=0 contributor_name=B. Butman contributor_role=principalInvestigator Conventions=COARDS, CF-1.6, ACDD-1.3 COORD_SYSTEM=GEOGRAPHICAL CREATION_DATE=08-Aug-2000 11:04:33 DATA_CMNT=Microcat Boston Subsurface Mooring DATA_ORIGIN=USGS/WHFC DATA_TYPE=TIME date_metadata_modified=2017-04-11T22:15:00Z DELTA_T=225 DEPTH_CONST=0 DRIFTER=0 Easternmost_Easting=-70.78231 EXPERIMENT=MWRA experiment_name=MBAY_LT featureType=TimeSeries FILL_FLAG=0 geospatial_bounds=POINT(-70.78231048583984 42.377838134765625) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=42.37784 geospatial_lat_min=42.37784 geospatial_lat_resolution=0 geospatial_lat_units=degrees_north geospatial_lon_max=-70.78231 geospatial_lon_min=-70.78231 geospatial_lon_resolution=0 geospatial_lon_units=degrees_east geospatial_vertical_positive=up geospatial_vertical_resolution=0 geospatial_vertical_units=meters grid_mapping_epsg_code=EPSG:4326 grid_mapping_inverse_flattening=298.257223563 grid_mapping_long_name=http://www.opengis.net/def/crs/EPSG/0/4326 grid_mapping_name=latitude_longitude grid_mapping_semi_major_axis=6378137.0 history=Fri Nov 1 20:19:41 2019: ncatted -a project,global,a,c,, CMG_Portal MBAY_LT/5901mc-a.nc Time fixed in dolly.m by increasing time -1 hours from record 1 thru 56447.: :Trimmed using truncate.m to select records in the range 59 to 56505. :Converted from ascii to EPIC by asc2epic.m 2017-04-11T22:15:00Z - pyaxiom - File created using pyaxiom id=5901mc-a infoUrl=https://cmgds.marine.usgs.gov/ocean-time-series/ inst_depth=6.0 INST_TYPE=Seabird Microcat institution=Coastal and Marine Hazards and Resources Program, U.S. Geological Survey keywords_vocabulary=GCMD Science Keywords latitude=42.37784 longitude=-70.78231 magnetic_variation=-16.0 MOORING=590 moortype=subsurface naming_authority=gov.usgs.cmgp ncei_template_version=NCEI_NetCDF_TimeSeries_Orthogonal_Template_v2.0 NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) NCProperties=version=1|netcdflibversion=4.4.1.1|hdf5libversion=1.8.17 Northernmost_Northing=42.37784 original_filename=5901mc-a.nc original_folder=MBAY_LT POS_CONST=0 project=U.S. Geological Survey Oceanographic Time-Series Data, CMG_Portal project_summary=Measurements of currents and other oceanographic properties were made to assess the impact of sewage discharge from the proposed outfall site. project_title=Long-term observations in Massachusetts Bay; Site A-Boston Harbor scipi=Brad Butman source=USGS sourceUrl=(local files) Southernmost_Northing=42.37784 standard_name_vocabulary=CF-1.6 start_time=21-Sep-1999 14:37:33 stop_time=15-Feb-2000 14:30:03 time_coverage_duration=PT12700350S time_coverage_end=2000-02-15T14:30:03Z time_coverage_start=1999-09-21T14:37:33Z VAR_DESC=T:C:S:STH water_depth=34.0 WATER_MASS=? Westernmost_Easting=-70.78231

Modelling population density over time: how spatial distance matters. Regional Studies. This study provides an empirical application of the Bayesian approach for modelling the evolution of population density distribution across time. It focuses on the case of Massachusetts by tracking changes in the importance of spatial distance from Boston concerning citizens’ choices of residence according to data for 1880–90 and 1930–2010. By adopting a Bayesian strategy, results show that Boston reinforced its attractiveness until the 1960s, when the city's accessibility no longer represented the unique determinant of population density distribution. Referring to selected historical evidence, a few possible interpretations are presented to endorse these results.

As part of the Boston University-led, Urban Long-Term Research Area - Exploratory Award (ULTRA-Ex), we established 135 circular, 15 m radius biometric plots extending across two Boston urban-to-rural gradients (Boston MA to Petersham MA and Boston MA to Worcester MA). The plots were stratified based on neighborhood (1 km2 surrounding area) characteristics for population density, impervious surface area fraction, and land cover. Within each plot we measured aboveground live and dead biomass, species characteristics, ground cover characteristics, and soil properties.

The Communities at Sea maps use Vessel Trip Report location point data as input to create density polygons representing visitation frequency ("fisherdays"). The data show total labor including crew time and the time spent in transit to and from fishing locations. They do not show other variables such as vessel value or number of pounds landed. The results can be interpreted as maps of "community presence." This layer shows data for the small bottom trawl fishing gear group for Boston, MA from 2011-2015.

This dataverse repository contains data from May to November of 2014 at fifteen locations across Metropolitan Boston for (1) throughfall nitrogen, (2) fossil fuel carbon dioxide emissions, (3) human population density, (4) land cover class, (5) ISA, (6) soil solution nitrogen and soil nitrogen cycling rates (mineralization and nitrification) and (7) soil respiration. Details of the methodology are provided in the following publications. Decina SM, PH Templer, LR Hutyra, CK Gately, P Rao. 2017. Variability, drivers, and effects of atmospheric nitrogen inputs across an urban area: emerging patterns among human activities, the atmosphere and soils. Science of the Total Environment 609:1524-1534. https://doi.org/10.1016/j.scitotenv.2017.07.166 Decina S, LR Hutyra, CK Gately, JM Getson, AB Reinmann, AG Short Gianotti, and PH Templer. 2016. Soil respiration contributes significantly to urban carbon fluxes. Environmental Pollution 212:433-439. https://doi.org/10.1016/j.envpol.2016.01.012

These data are a part of the effort to map geologic substrates of the Stellwagen Bank National Marine Sanctuary region off Boston, Massachusetts. The overall goal is to develop high-resolution (1:25,000) interpretive maps, based on multibeam sonar data and seabed sampling, showing surficial geology and seabed sediment dynamics. The work was conducted in collaboration with the Stellwagen Bank National Marine Sanctuary, and the data collected will aid research on the ecology of fish and invertebrate species that inhabit the region. The Sanctuary's research vessel, R/V Auk, visited a total of 169 locations on Stellwagen Bank during three separate surveys from September 2020 to August 2021. During these surveys a customized Van Veen grab sampler (SEABOSS) equipped with a video camera and a CTD was deployed in drift mode to collect sediment for grain-size analysis, video imagery of the seabed, and measurements of water column properties including temperature, salinity, density, and fluorescence.

A salt marsh restoration plan for Thompson Island in Boston Harbor calls for the removal of a stone berm that prevents tidal flushing of an existing salt marsh/pond on the central east side of the island. Lack of daily tidal exchange has resulted in a stagnant water body with severe algae blooms and encroachment by the invasive plant species, common reed (Phragmites australis). The restoration plan includes archaeological investigation of the proposed construction/demolition area as well as the inter-tidal zone and marsh/pond bottom that may be subject to erosion once the blocked outlet is reopened. The construction area was tested with a series of ten, 10 m-interval shovel tests arranged in four transects. Sand and gravel predominated with a low density of late nineteenth and twentieth century waterworn ceramics, glass, brick and coal. The south and west marsh perimter was previously tested with 20 m-interval shovel tests by Barbara Luedtke in 1993 with only late nineteenth and twentieth century artifacts found. The remaining east and north tidal zone, therefore, was tested with nineteen, 20 m-interval shovel tests. These revealed sandy clay loam soils and a low density scatter of mostly waterworn late nineteenth and twentieth century artifacts. Two gray siltstone flakes and some shatter were identified in one test in [REDACTED]. Array tests revealed an additional gray-green chert flake. The marsh bottom was remotely imaged using a StrataBox subbottom profiler to search for potential submerged anomalies. Shallow conditions and gas-charged sediments obscurred some readings, but a paleo drainage channel was identified approximately 2 m below the marsh bottom. Although one small Native American activity locus was identified, it was recommended that the project proceed as planned.

Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. The accompanying datasets include the block- and tract-level data used to conduct the analysis. R and Python scripts for calculating morphometrics, conducting unsupervised classification, and conducting the descriptive statistics and regression analysis at the census block and census tract levels are also included.

On 7 occasions over the course of 3 years (2011-2013) we conducted hypoxic static core incubations on sediments and water collected in Waquoit Bay Massachusetts (USA) from four stations: Childs River Estuary, Metoxit Point, South Basin, & Sage Lot Pond. The goal of this study was quantify sediment metabolism under water column hypoxia in a shallow, temperate estuarine system. As part of that study we analyzed samples from incubated cores to determine sediment density, porosity, percent carbon (%C), percent nitrogen (%N), and molar carbon to nitrogen (C:N) ratios. These samples were collected using plastic (polycarbonate) syringe sub-cores (60 mL) that we sectioned up to 4 cm in 1 cm sub-samples. We stored the sediments in plastic centrifuge tubes (50 mL) in a freezer until they were analyzed. All sampling materials were acid washed and ethanol rinsed prior to sediment collection. We used standard processing and analysis protocols to determine sediment porosity, density (Nielsen et al. 2000) and %C %N (Zimmermann et al. 1997). Percent C and N samples were determined using an elemental analyzer at the Boston University Stable Isotopes Laboratory. For more information please see articles where these data are published (Foster & Fulweiler 2014, Foster & Fulweiler 2019). Email questions and comments to: sqfoster@bu.eduSampling StationsCRE = Childs River Estuary (41° 34.805’ N 70°31.826’ W, 1.2 m deep, bottom water salinity 27.3-29.7 psu) MP = Metoxit Point (41° 34.134’ N 70° 31.272’ W, 2.2 m deep, bottom water salinity 29.6-31.3 psu) SB = South Basin (41° 33.404’ N 70° 31.442’ W, 1.8 m deep, bottom water salinity 30.6-31.3 psu)SLP = Sage Lot Pond (41° 33.270’ N 70° 30.584’ W, 1.2 m deep, bottom water salinity 28.9-30.4 psu)UnitsDepth Range = cmDensity = g/mLPorosity = sediment pore space to total volume ratioCarbon = percent carbon of total massNitrogen = percent nitrogen of total massC:N = carbon to nitrogen molar ratioAbbreviations & SymbolsDate = dd (day) - month - yy (year)Stn = Stationm.i. = measurement issuen.m. = not measuredAcknowledgmentsThere are numerous people who contributed to this project. We would like to thank the Waquoit Bay National Estuarine Research Reserve (WBNERR) for their continued multi-year support of our research. All sediment samples for this study were collected using WBNERR boats. We are particularly grateful to the following WBNERR employees who assisted with the fieldwork: MK Fox, A Lescher, J Mora, C Weidman. We would also like to thank several Fulweiler Lab members and Boston University Marine Program (BUMP) students for their assistance with fieldwork and sediment sub-coring in the lab: S Andrews, A Banks, S Buckley, K Czapla, S Donovan, D Forest, E Heiss, J Luthringer, M McCarthy, S Newell, MK Rogener, R Schweiker, K Yoshimura. In addition, S Donovan, S Duan, E Greenberg, R Lauto, and D Lewellyn, helped with sediment processing and density/porosity analysis. And R Michener in Boston University’s Stable Isotopes Laboratory analyzed sediment percent carbon and nitrogen. We also thank Boston University Earth and Environment Department for use of their facilities and their general academic and logistical research support.ReferencesFoster SQ, and RW Fulweiler. 2019. Estuarine sediments exhibit dynamic and variable biogeochemical responses to hypoxia. Journal of Geophysical Research: Biogeosciences, 124. https://doi.org/10.1029/2018JG004663Foster SQ and RW Fulweiler. 2014. Spatial and historic variability of benthic nitrogen cycling in an anthropogenically impacted estuary. Frontiers in Marine Science 1. https://doi.org/10.3389/fmars.2014.00056.Nielsen LP, V Brotas, P Viaroli, G Underwood, DB Nedwell, K Sundback, S Rysgaard, et al. 2000. Protocol handbook for NICE - Nitrogen Cycling in Estuaries: A Project under the EU reserach programme: Marine Science and Technology (MAST III). Edited by T Dalsgaard. National Environmental Research Institute, Silkborg, Denmark.Zimmermann CF, CW Keefe, and J Bashe. 1997. Determination of carbon and nitrogen in sediments and particulates of estuarine/coastal waters using elemental analysis. US Environmental Protection Agency, Method 440: 9.

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

Measurements of currents and other oceanographic properties were made to assess the impact of sewage discharge from the proposed outfall site. cdm_data_type=TimeSeries cdm_timeseries_variables=station_id COMPOSITE=0 contributor_name=B. Butman contributor_role=principalInvestigator Conventions=COARDS, CF-1.6, ACDD-1.3 COORD_SYSTEM=GEOGRAPHICAL CREATION_DATE=31-Dec-2008 12:16:27 DATA_ORIGIN=USGS/WHFC DATA_TYPE=TIME date_metadata_modified=2017-04-11T22:10:00Z DELTA_T=5.0000 minutes DEPTH_CONST=0 DESCRIPT=BOSTON BASS MIDAS. NOT SCRUBBED. LOST POWER 3/22. SALINITY R DRIFTER=0 Easternmost_Easting=-70.78516 experiment_name=MBAY_LT featureType=TimeSeries FILL_FLAG=0 geospatial_bounds=POINT(-70.78516387939453 42.3745002746582) geospatial_bounds_crs=EPSG:4326 geospatial_lat_max=42.3745 geospatial_lat_min=42.3745 geospatial_lat_resolution=0 geospatial_lat_units=degrees_north geospatial_lon_max=-70.78516 geospatial_lon_min=-70.78516 geospatial_lon_resolution=0 geospatial_lon_units=degrees_east geospatial_vertical_positive=up geospatial_vertical_resolution=0 geospatial_vertical_units=meters grid_mapping_epsg_code=EPSG:4326 grid_mapping_inverse_flattening=298.257223563 grid_mapping_long_name=http://www.opengis.net/def/crs/EPSG/0/4326 grid_mapping_name=latitude_longitude grid_mapping_semi_major_axis=6378137.0 history=Fri Nov 1 20:18:22 2019: ncatted -a project,global,a,c,, CMG_Portal MBAY_LT/4131tct-a_d1.nc WATER_DEPTH related attributes corrected by adj_tripod_depth: Converted to EPIC Wed Nov 13 15:09:01 1996:Conductivity decreased by factor of 1000, jge, 25-Nov-1997: 2017-04-11T22:10:00Z - pyaxiom - File created using pyaxiom id=4131tct-a_d1 infoUrl=https://cmgds.marine.usgs.gov/ocean-time-series/ INST_TYPE=C-0844 institution=Coastal and Marine Hazards and Resources Program, U.S. Geological Survey keywords_vocabulary=GCMD Science Keywords latitude=42.3745 longitude=-70.785164 magnetic_variation_deg=-16.0 MOORING=413 naming_authority=gov.usgs.cmgp ncei_template_version=NCEI_NetCDF_TimeSeries_Orthogonal_Template_v2.0 NCO=netCDF Operators version 4.8.1 (Homepage = http://nco.sf.net, Code = https://github.com/nco/nco) NCProperties=version=1|netcdflibversion=4.4.1.1|hdf5libversion=1.8.17 Northernmost_Northing=42.3745 original_filename=4131tct-a_d1.nc original_folder=MBAY_LT POS_CONST=0 project=U.S. Geological Survey Oceanographic Time-Series Data, CMG_Portal project_summary=Measurements of currents and other oceanographic properties were made to assess the impact of sewage discharge from the proposed outfall site. project_title=Long-term observations in Massachusetts Bay; Site A-Boston Harbor source=USGS sourceUrl=(local files) Southernmost_Northing=42.3745 standard_name_vocabulary=CF-1.6 start_time=93- II -25 16.32.30 stop_time=93- III-22 14.07.30 time_coverage_duration=PT2151300S time_coverage_end=1993-03-22T14:07:30Z time_coverage_start=1993-02-25T16:32:30Z VAR_DESC=con :: sal WATER_DEPTH=32.725437 WATER_DEPTH_NOTE=water depth computed using mean pressure from 4071p-a1h.nc - atmosphere (1013.3): (m) WATER_MASS=? Westernmost_Easting=-70.78516

Aryandoust, A., van Vliet, O. & Patt, A. City-scale car traffic and parking density maps from Uber Movement travel time data. Scientific Data 6, 158 (2019). https://doi.org/10.1038/s41597-019-0159-6

A collection of historic traffic count data and guidelines for how to collect new data for Massachusetts Department of Transportation (MassDOT) projects.

a CES-D, Center for the Epidemiologic Studies Depression Scale; CRP, c-reactive protein; 25(OH)D, 25-hydroxyvitamin D; IL-6, interleukin 6; BMD, bone mineral density.b The category“with insomnia disorder” includes participants who reported having difficulty initiating sleep, difficulty maintaining sleep or early-morning awakenings most of the time, accompanied with non-restorative sleep most of the time.c Mean±SD (all such values).d Urinary cortisol (mg) is standardized by multiplying each measure by total urine volume and dividing by urinary creatinine excretion.e Geometric mean, 95% confidence interval in parentheses (all such values).* p

a Values in the table are β coefficients and 95% confidence interval in the regression model.b Adjusted for age (year), educational level, smoking status (never smoke, past smoker and current smoker), drinking status (not current drinker, current moderate drinker and current heavy drinker), body mass index (Kg/m2), physical activity score, presence of hypertension (y/n), diabetes (y/n), arthritis (y/n), plasma 25-hydroxyvitamin D concentration (ng/mL), snoring frequency (every/most night, a few nights a week, and occasionally/rare), osteoporosis medication use (y/n), CES-D score among men, and menopause status (y/n, women alone).Associations between insomnia symptoms and bone mineral density (BMD)a.