The Johns Hopkins Center for a Livable Future (CLF) obtained the food permit list from the Baltimore City Health Department in August 2011, which includes all sites that sell food, such as stores, restaurants and temporary locations such as farmers' market stands and street carts. The restaurants were grouped into three categories, including full service restaurants, fast food chains and carryouts. Carryout and fast food chain restaurants were extracted from the restaurant layer and spatially joined with the 2010 Community Statistical Area (CSA) data layer, provided by BNIA-JFI. The prepared foods density, per 1,000 people, was calculated for each CSA using the CSA's population and the total number of carryout and fast food restaurants, including vendors selling prepared foods in public markets, in each CSA. Source: Johns Hopkins University, Center for a Livable FutureYears Available: 2011, 2013, 2018, 2019

Chart and table of population level and growth rate for the Baltimore metro area from 1950 to 2025. United Nations population projections are also included through the year 2035.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Baltimore Ecosystem Study LTER (BES) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

This indicator reflects the number of business establishments that possess a Class A (Off Sale package goods no on-premises consumption - 6 days, 6:00 a.m.- Midnight. No Sunday sales except Sundays between Thanksgiving Day and New Year's Day upon issuance of a special license for each Sunday) or BD7 (tavern) business license that allows them to sell beer, wine, or liquor. Other liquor licenses to restaurants or on-premise consumption were not included in this analysis. This number is provided by 1,000 residents to allow for comparison across neighborhoods. Source: Baltimore City Liquor Board Years Available: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2021, 2022

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Baltimore Ecosystem Study LTER (BES) contains percent urban population measurements in percent units and were aggregated to a yearly timescale.

This EnviroAtlas dataset estimates the intersection density of walkable roads within a 750 meter radius of any given 10 meter pixel in the community. Intersections are defined as any point where 3 or more roads meet and density is calculated using kernel density, where closer intersections are weighted higher than further intersections. Intersection density is highly correlated with walking for transportation. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

A dataset listing Maryland counties by population for 2024.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) 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 shapefile 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 2020 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.

After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." Urbanized areas (UAs) contain 50,000 or more people. Each urban area is identified by a 5-character numeric census code that may contain leading zeroes.Date: 1/1/2016 (2010 Census boundary)Update: none planned, 2020 boundaries will be added separatelySource: Census TIGER/Line. More information on Census geography can be found at https://www.census.gov/geo/maps-data/data/tiger-line.html.

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 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 and beyond, 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.

Soil_Samples_BACI Available only by request on a case by case basis. Contact rthe author, David Nowak, at dnowak@fs.fed.us Tags Biophysical Resources, Land, Social Institutions, Health, BES, Soil, Lead, Sample, UFORE Summary Samples were taken to relate soil data to vegetation data obtained for the Urban Forestry Effects Model (UFORE). Description The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces. Credits Rich Pouyat, USDA Forest Service Use limitations Not for profit use only Extent West -76.711030 East -76.530612 North 39.371355 South 39.200686 Scale Range There is no scale range for this item. The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.

Soil_Samples_BACI

   Tags

   Biophysical Resources, Land, Social Institutions, Health, BES, Soil, Lead, Sample, UFORE




   Summary


   Samples were taken to relate soil data to vegetation data obtained for the Urban Forestry Effects Model (UFORE).


   Description


   The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.


   Credits


   Rich Pouyat, USDA Forest Service


   Use limitations


   Not for profit use only


   Extent



   West -76.711030  East -76.530612 

   North 39.371355  South 39.200686 




   Scale Range

   There is no scale range for this item.



   The data is soil concentrations and characteristics of the following: land use, bulk density, sand, silt, clay, pH, organic matter, nitrogen, Al, P, S, Ti, Cr, Mn, Fe, Co, Ni, Cu Zn, Mo, Pb, Cd, Na, Mg, K, Ca, and V. Soils were sampled in 125 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.

Disclaimer: These data are updated by the author and are not an official product of the Federal Reserve Bank of Cleveland.This project provides two sets of migration estimates for the major US metro areas. The first series measures net migration of people to and from the urban neighborhoods of the metro areas. The second series covers all neighborhoods but breaks down net migration to other regions by four region types: (1) high-cost metros, (2) affordable, large metros, (3) midsized metros, and (4) small metros and rural areas. These series were introduced in a Cleveland Fed District Data Brief entitled “Urban and Regional Migration Estimates: Will Your City Recover from the Pandemic?"The migration estimates in this project are created with data from the Federal Reserve Bank of New York/Equifax Consumer Credit Panel (CCP). The CCP is a 5 percent random sample of the credit histories maintained by Equifax. The CCP reports the census block of residence for over 10 million individuals each quarter. Each month, Equifax receives individuals’ addresses, along with reports of debt balances and payments, from creditors (mortgage lenders, credit card issuers, student loan servicers, etc.). An algorithm maintained by Equifax considers all of the addresses reported for an individual and identifies the individual’s most likely current address. Equifax anonymizes the data before they are added to the CCP, removing names, addresses, and Social Security numbers (SSNs). In lieu of mailing addresses, the census block of the address is added to the CCP. Equifax creates a unique, anonymous identifier to enable researchers to build individuals’ panels. The panel nature of the data allows us to observe when someone has migrated and is living in a census block different from the one they lived in at the end of the preceding quarter. For more details about the CCP and its use in measuring migration, see Lee and Van der Klaauw (2010) and DeWaard, Johnson and Whitaker (2019). DefinitionsMetropolitan areaThe metropolitan areas in these data are combined statistical areas. This is the most aggregate definition of metro areas, and it combines Washington DC with Baltimore, San Jose with San Francisco, Akron with Cleveland, etc. Metro areas are combinations of counties that are tightly linked by worker commutes and other economic activity. All counties outside of metropolitan areas are tracked as parts of a rural commuting zone (CZ). CZs are also groups of counties linked by commuting, but CZ definitions cover all counties, both metropolitan and non-metropolitan. High-cost metropolitan areasHigh-cost metro areas are those where the median list price for a house was more than $200 per square foot on average between April 2017 and April 2022. These areas include San Francisco-San Jose, New York, San Diego, Los Angeles, Seattle, Boston, Miami, Sacramento, Denver, Salt Lake City, Portland, and Washington-Baltimore. Other Types of RegionsMetro areas with populations above 2 million and house price averages below $200 per square foot are categorized as affordable, large metros. Metro areas with populations between 500,000 and 2 million are categorized as mid-sized metros, regardless of house prices. All remaining counties are in the small metro and rural category.To obtain a metro area's total net migration, sum the four net migration values for the the four types of regions.Urban neighborhoodCensus tracts are designated as urban if they have a population density above 7,000 people per square mile. High density neighborhoods can support walkable retail districts and high-frequency public transportation. They are more likely to have the “street life” that people associate with living in an urban rather than a suburban area. The threshold of 7,000 people per square mile was selected because it was the average density in the largest US cities in the 1930 census. Before World War II, workplaces, shopping, schools and parks had to be accessible on foot. Tracts are also designated as urban if more than half of their housing units were built before WWII and they have a population density above 2,000 people per square mile. The lower population density threshold for the pre-war neighborhoods recognizes that many urban tracts have lost population since the 1960s. While the street grids usually remain, the area also needs su

Priority Planting locations by block group for Baltimore City using the index developed by Nowak et al. (unpublished) of the USDA Forest Service Northeastern Experiment Station. The criteria used to make the index were: - Population density: the greater the population density, the greater the priority for tree planting - Tree stocking levels: the lower the tree stocking level (the percent of available greenspace (tree, grass, and soil cover areas) that is occupied by tree canopies), the greater the priority for tree planting - Tree cover per capita: the lower the amount of tree canopy cover per capita (m2/capita), the greater the priority for tree planting Each criteria was standardized on a scale of 0 to 1 with 1 representing the census block with the highest value in relation to priority of tree planting (i.e., the census block with highest population density, lowest stocking density or lowest tree cover per capita were standardized to a rating of 1). Individual scores were combined based on the following formula to produce an overall priority index value between 0 and 100: I = (PD * 40) + (TS * 30) + (TPC * 30) Where I = index value, PD is standardized population density, TS is standardized tree stocking, and TPC is standardized tree cover per capita. Based on this index, Planting priority maps were produced. Standardized value for population density was calculated as PD = (PD_n - PD_m) / PD_r, where PD is the value (0-1), PD_n is the value for the census block (population / km2), PD_m is the minimum value for all census blocks, and PD_r is the range of values among all census blocks (maximum value - minimum value). Standardized value for tree stocking was calculated as TS = (100 - ((T/(T+G)) * 100)) / 100, where TS is the value (0-1), T is percent tree cover, and G is percent grass cover. Standardized value for tree cover per capita was calculated as TPC = 1 - [(TPC_n - TPC_m) / TPC_r], where TPC is the value (0-1), TPC_n is the value for the census block (m2/capita), TPC_m is the minimum value for all census blocks, and TPC_r is the range of values among all census blocks (maximum value - minimum value).

Transfer of Development Rights (TDR) and Purchase of Development Rights (PDR) are two tools used to protect lands from development. "Transfer of Development Rights (TDR) is a voluntary, incentive-based program that allows landowners to sell development rights from their land to a developer or other interested party who then can use these rights to increase the density of development at another designated location. While the seller of development rights still owns the land and can continue using it, an easement is placed on the property that prevents further development. A TDR program protects land resources at the same time providing additional income to both the landowner and the holder of the development rights." Source: https://www.uwsp.edu/cnr-ap/clue/Documents/PlanImplementation/Transfer_of_Development_Rights.pdf The Purchase of Development Rights (PDR) is a voluntary program in which a land trust or a local, county or state agency buys the development rights on a parcel of land, primarily agricultural. PDRs "permanently extinguish all preexisting development potential of a particular property and are not used to offset development elsewhere in the county. Other than very limited rights reserved to the original grantor and their immediate family, no further commercial or residential subdivision is allowed. The grantor of the easement and all subsequent owners of the property retain full fee simple ownership of the land, but are bound by the terms of the Deed of Easement" in perpetuity. Source: https://www.harfordcountymd.gov/368/HALPP These data are intended for general guidance and use only.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://geodata.md.gov/imap/rest/services/Environment/MD_ProtectedLands/FeatureServer/9

The aim of this research was to examine the spatial and temporal variation in export control points of nitrogen on residential lawns (locations prone to mobilizing nitrogen during a rain event) and to examine if previously measured hydrobiogeochemical properties were predictive of N mobilization in lawns. This data set contains measurements of saturated infiltration rates, sorptivity, soil moisture, soil organic matter, bulk density, pH, soil nitrate, soil ammonium, N2O, N2 and CO2 fluxes from soil cores, nitrogen mineralization rates and fluxes of N in runoff and leachate from fertilized and unfertilized residential and institutional lawns. Study lawns were located at homes of people who agreed to volunteer their lawn for the study from a door knocking campaign. Four sampling houses were located in an exurban neighborhood in Baisman Run. Five sampling houses were located in a suburban neighborhood in Dead Run. Two sampling locations on institutional lawns were located at University of Maryland Baltimore County. At the exurban study houses and institutional lawns sites, we identified one hillslope to conduct sampling on. At the Dead Run houses we identified one hillslope on the front yard and one in the backyard as there were distinct locations that were not present in the exurban neighborhood. Locations within the yards for sampling were selected based on sampling conducted in October 2017. Locations were grouped into four categories based on have either high or low potential denitrification rates and high or low saturated infiltration rates (n=48). These locations were also distributed across yard types (exurban, suburban or institutional), fertilizer treatments, and hillslope location (top or bottom of hillslope). At each sampling location we ran a Cornell Sprinkle Infiltrometer to generate an experimental rainfall during which we collected runoff and leachate to quantity N flux. We also measure sorptivity and saturated infiltration rates. Volumetric water content was measured before and after infiltrometer runs with a Field Scout TDR 300 with 7.5 cm rods. In addition, at each sampling location we took two soil cores to 10 cm depth to measure gaseous N flux and soil N processes. Soil cores were stored on ice in the field, and then stored at 4°C in the lab until processed for variables mentioned above. Sampling was conducted across four seasons (April 2018, September 2018, November 2018 and March 2019) to capture seasonal variability including the timing of fertilizer applications.

Abstract: One-meter soil cores were taken to evaluate soil texture, bulk density, carbon and nitrogen pools, microbial biomass carbon and nitrogen content, microbial respiration, potential net nitrogen mineralization, potential net nitrification and inorganic nitrogen pools in 32 residential home lawns that differed by previous land use and age, but had similar soil types. These were compared to soils from 8 forested reference sites. Purpose: Soil cores were obtained from residential and forest sites in the Baltimore, MD USA metropolitan area. The residential sites were mostly within the Gwynns Falls Watershed (-76.012008W, -77.314183E, 39.724847N, 38.708367S and approximately 17 km2) Lawns on residential sites were dominated by a variety of cool season turfgrasses. Forest soil cores were taken from permanent forest plots of the Baltimore Ecosystem Study (BES) LTER (Groffman et al. 2006). These remnant forests are over 100 years old with soils that were comparable in type and texture to those underlying the residential study sites. Soils from all sites were from the Manor series (coarse-loamy, micaceous, mesic Typic Dystrudepts), which are well-drained upland soils with loamy textures and bedrock at 5 to 10 feet below the soil surface. To aid the site selection process we used neighborhoods in the Baltimore City metropolitan area that have been mapped using HERCULES, a high resolution land cover classification system designed to assist in the study of human-ecological systems (Cadenasso et al. 2007). Using HERCULES and additional data sources, we identified residential sites that were similar except for single factors that we hypothesized to be important predictors of ecosystem dynamics. These factors included land use history (agriculture and forest, n = 10 and n = 22), housing density (low and medium/high, n = 9 and n = 23), and housing age (4 to 58 yrs old, n = 32). Housing age was acquired from the Maryland Property View database. Prior land use was determined based on land use change maps developed by integrating aerial photos from 1938, 1957, 1971, and 1999 into a geographic information system. Once a list of residential parcels meeting the predefined criteria were identified, we sent mailings to property owners chosen at random from each of the factor groups with the goal of recruiting 40 property owners for a 3 year study (of which this work is a part). We had recruited 32 property owners at the time that soil cores were obtained. Data have been published in Raciti et al. (2011a, 2011b) References Cadenasso, M. L., S. T. A. Pickett, and K. Schwarz. 2007. Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification. Frontiers in Ecology and the Environment 5:80-88. https://doi.org/10.1890/1540-9295(2007)5[80:SHIUER]2.0.CO;2 Groffman, P. M., R. V. Pouyat, M. L. Cadenasso, W. C. Zipperer, K. Szlavecz, I. D. Yesilonis, L. E. Band, and G. S. Brush. 2006. Land use context and natural soil controls on plant community composition and soil nitrogen and carbon dynamics in urban and rural forests. Forest Ecology and Management 236:177-192. https://doi.org/10.1016/j.foreco.2006.09.002 Raciti, S. M., Groffman, P. M., Jenkins, J. C., Pouyat, R. V., Fahey, T. J., Pickett, S. T. A., & Cadenasso, M. L. (2011a). Nitrate production and availability in residential soils. Ecological Applications, 21(7), 2357–2366. https://doi.org/10.1890/10-2009.1 Raciti, S.M., Groffman, P.M., Jenkins, J.C. et al. (2011b). Accumulation of Carbon and Nitrogen in Residential Soils with Different Land-Use Histories. Ecosystems 14, 287–297. https://doi.org/10.1007/s10021-010-9409-3

Soils were sampled in 126 plots located within the City of Baltimore in the summer of 2000. The plots were randomly stratified by Anderson Land Cover Classification System Level II, which included commercial, industrial, institutional, transportation right-of-ways, high and medium density residential (there were no low density residential areas identified within the city boundaries), golf course, park, urban open, forest, and wetland land-use types. The number of plots situated in each land-use type was weighted to their proportion of spatial area within the City. The resultant number of plots sampled for soil by land-use type was: commercial (n = 2); industrial (n = 3); institutional (n = 10); transportation right-of-ways (n = 7); high density residential (n = 19); medium density residential (n = 33); golf course (n = 3); riparian (n=2); park (n = 10); urban open (n = 10); and forest (n = 26) land-use types, respectively. The distribution of plots represents the proportion of area covered by impervious surfaces.

Participants Gary Fisher, U.S. Geological Survey (oversees stream flow monitoring) Ed Doheny, U.S. Geological Survey (oversees stream flow monitoring) Ken Belt, U.S. Forest Service (oversees weekly station checks) I. Collection of Flow Data at USGS Stream Gage Stations BES stream chemistry samples are collected at gaging stations built and maintained by the U.S Geological Survey and which are funded mainly by LTER funds. However a number of the stations are funded by other sources, including the USGS, Maryland Department of the Environment, Baltimore County DEPRM, and Baltimore City DPW. Stage at each gage site is measured to about 0.01 ft accuracy and is recorded at 15 minute (or shorter) intervals using either a float gage or pressure transducer. These stage records are converted to flow records using stage-discharge relationships (see below). Crest-stage recorders (floating cork gages) indicate high water marks and provide a backup source of high water in case recording gages are compromised in flooding conditions. Stilling wells are installed at each site, either in the channel or via piping to the channel, to provide quiescent locations for sensing of stage. General Approach to Discharge Measurement and Flow Ratings In a measurement of discharge (volumetric flow per unit time), a number of velocity measurements are taken across a transect located near the gage using a wading rod and propeller current meter (usually a mini (pygmy) velocity meter). Velocities (ft/sec) for each sub section are multiplied by their respective sub section areas (sq. ft.) and these discharges (cubic feet per second) are then summed to get a total discharge measurement for the section. Generally, an attempt is made to include enough velocity measurements across the transect such that no more than 10 % of the total discharge is contained in each sub section, although this may not be achievable for higher flows. Each total discharge measurement, along with the concurrent stage reading constitutes a point on a stage-discharge rating curve. Over time, discharge measurements are obtained for a range of stages so that a complete, accurate and current stage-discharge rating curve (graph) can be constructed. USGS field crews visit all sites about every six weeks to download data, check and calibrate equipment, and to do a low flow discharge measurements to check the stage-discharge rating for shifts, and to observe the channel section for potential changes in the high flow rating. A single USGS hydrologist is assigned to the BES stations to provide continuity and to maximize familiarity with the stations. These USGS visits are augmented by weekly visits by BES field crews who do a number of checks under the guidance of a Forest Service Hydrologist (see below, II. Stream Gage calibration & Flow Rating QAQC: Weekly Checks). High flow velocities cannot be measured in-stream, and are metered by USGS crews from bridges near the gaging station. Discharge measurements during runoff events are repeatedly conducted until the high flow portion of the rating is defined. Extremely high flow discharges that cannot be measured are estimated by a slope area curve method using cross section geometry, channel roughness and surveyed elevations of high points of the hydraulic grade line indicated by debris left by the receding limb of the hydrograph. Data are recorded at 15 minute intervals at the larger watersheds and at 5 minute intervals at small watersheds. Records are downloaded electronically and processed at the USGS office where adjustments are made for calibration problems, backwater corrections (due to debris and ice buildup), missing records, shifts in the rating, etc. Small changes in the low flow portion of the stage-discharge rating are handled using