- Population
The population density of Middle River, MD was 3,259 in 2018. The population density of Reisterstown, MD was 5,216 in 2018.
Population Density
Population Density is computed by dividing the total population by Land Area Per Square Mile.
Above charts are based on data from the U.S. Census American Community Survey | ODN Dataset | API -
Geographic and Population Datasets Involving Reisterstown, MD or Middle River, MD
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MD COVID-19 - Cases per 100K population, by jurisdiction
opendata.maryland.gov | Last Updated 2024-09-17T14:36:53.000Z<b>Note:</b> Starting April 27, 2023 updates change from daily to weekly. <b>Summary</b> The rate of confirmed COVID-19 cases among Marylanders per 100,000 people in each Maryland jurisdiction. <b>Description</b> The MD COVID-19 cases per 100K population, by jurisdiction layer is the rate of confirmed daily COVID-19 cases among Marylanders per 100,000 people in each Maryland jurisdiction. This rate is a 7-day average, calculated using the CasesByCounty layer and the 2019 estimated county populations (Maryland Department of Planning). Any negative value may be attributed to changes in reporting by jurisdiction. <b>Terms of Use</b> The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
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MD COVID-19 - Cases per 100K population, statewide
opendata.maryland.gov | Last Updated 2024-09-17T14:36:31.000Z<b>Note:</b> Starting April 27, 2023 updates change from daily to weekly. <b>Summary</b> The rate of confirmed COVID-19 cases among Marylanders per 100,000 people statewide. <b>Description</b> The MD COVID-19 cases per 100K population, statewide layer is the rate of confirmed daily COVID-19 cases among Marylanders per 100,000 people statewide. This rate is a 7-day average, calculated using the sum of the CasesByCounty layer and the 2019 estimated county populations (Maryland Department of Planning). <b>Terms of Use</b> The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
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MD COVID-19 - Vaccination Percent Age Group Population
opendata.maryland.gov | Last Updated 2023-04-27T15:26:50.000ZRegarding all Vaccination Data The date of Last Update is 4/21/2023. Additionally on 4/27/2023 several COVID-19 datasets were retired and no longer included in public COVID-19 data dissemination. See this link for more information https://imap.maryland.gov/pages/covid-data <b>Summary</b> The cumulative number of COVID-19 vaccinations percent age group population: 16-17; 18-49; 50-64; 65 Plus. <b>Description</b> COVID-19 - Vaccination Percent Age Group Population data layer is a collection of COVID-19 vaccinations that have been reported each day into ImmuNet. COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county. <b>Terms of Use</b> The Spatial Data, and the information therein, (collectively the Data) is provided as is without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata. This map is for planning purposes only. MEMA does not guarantee the accuracy of any forecast or predictive elements.
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Chesapeake Bay Pollution Loads - Nitrogen
opendata.maryland.gov | Last Updated 2018-09-27T13:20:11.000ZNitrogen pollution from contributing sources in Bay watershed, pounds per year. 1985, 2007, and 2009 - 2012 progress; 2017 and 2025 targets. Data source: EPA Phase 5.3.2 Watershed Model.
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MD COVID-19 - Total Population Tested by County
opendata.maryland.gov | Last Updated 2022-08-23T10:34:42.000Z<b>Summary</b> This layer is deprecated (Last updated 3/14/2022). The total number of residents who have been administered at least one COVID-19 test in each Maryland jurisdiction. <b>Description</b> Data represent the number of Maryland residents, both in number and by percent of the population, who have been tested for COVID-19 at least once each Maryland jurisdiction. <b>Terms of Use</b> The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
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Chesapeake Bay Pollution Loads - Phosphorus
opendata.maryland.gov | Last Updated 2018-06-25T12:27:15.000ZPhosphorus pollution from contributing sources in Bay watershed, pounds per year. 1985, 2007, and 2009 - 2012 progress; 2017 and 2025 targets. Data source: EPA Phase 5.3.2 Watershed Model.
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Chesapeake Bay Pollution Loads - Sediment
opendata.maryland.gov | Last Updated 2018-06-25T12:26:56.000ZSediment pollution from contributing sources in Bay watershed, tons per year. 1985, 2007, and 2009 - 2012 progress; 2017 and 2025 target loads. Target loads for sediment were not broken out to a scale finer than major basin. Data source: EPA Phase 5.3.2 Watershed Model.
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MTA Transit Oriented Development (TOD) Data
opendata.maryland.gov | Last Updated 2024-03-25T15:38:10.000Z*** DISCLAIMER - This web page is a public resource of general information. The Maryland Mass Transit Administration (MTA) makes no warranty, representation, or guarantee as to the content, sequence, accuracy, timeliness, or completeness of any of the spatial data or database information provided herein. MTA and partner state, local, and other agencies shall assume no liability for errors, omissions, or inaccuracies in the information provided regardless of how caused; or any decision made or action taken or not taken by any person relying on any information or data furnished within. *** This dataset assesses rail station potential for different forms of transit oriented development (TOD). A key driver of increased transit ridership in Maryland, TOD capitalizes on existing rapid transit infrastructure. The online tool focuses on the MTA’s existing MARC Commuter Rail, Metro Subway, and Central Light Rail lines and includes information specific to each station. The goal of this dataset is to give MTA planning staff, developers, local governments, and transit riders a picture of how each MTA rail station could attract TOD investment. In order to make this assessment, MTA staff gathered data on characteristics that are likely to influence TOD potential. The station-specific data is organized into 6 different categories referring to transit activity; station facilities; parking provision and utilization; bicycle and pedestrian access; and local zoning and land availability around each station. As a publicly shared resource, this dataset can be used by local communities to identify and prioritize area improvements in coordination with the MTA that can help attract investment around rail stations. You can view an interactive version of this dataset at geodata.md.gov/tod. ** Ridership is calculated the following ways: Metro Rail ridership is based on Metro gate exit counts. Light Rail ridership is estimated using a statistical sampling process in line with FTA established guidelines, and approved by the FTA. MARC ridership is calculated using two (2) independent methods: Monthly Line level ridership is estimated using a statistical sampling process in line with FTA established guidelines, and approved by the FTA. This method of ridership calculation is used by the MTA for official reporting purposes to State level and Federal level reporting. Station level ridership is estimated by using person counts completed by the third party vendor. This method of calculation has not been verified by the FTA for statistical reporting and is used for scheduling purposes only. However, because of the granularity of detail, this information is useful for TOD applications. *Please note that the monthly level ridership and the station level ridership are calculated using two (2) independent methods that are not interchangeable and should not be compared for analysis purposes.
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SWGIHubCorShp
opendata.maryland.gov | Last Updated 2024-04-10T19:35:09.000ZMaryland's green infrastructure is a network of undeveloped lands that provide the bulk of the state's natural support system. These data map hub and corridor elements within the green infrastructure. The Green Infrastructure Assessment was developed to provide decision support for Maryland's Department of Natural Resources land conservation programs. Ecosystem services, such as cleaning the air, filtering water, storing and cycling nutrients, conserving soils, regulating climate, and maintaining hydrologic function, are all provided by the existing expanses of forests, wetlands, and other natural lands. These ecologically valuable lands also provide marketable goods and services, like forest products, fish and wildlife, and recreation. The Green Infrastructure serves as vital habitat for wild species and contributes in many ways to the health and quality of life for Maryland residents. To identify and prioritize Maryland's green infrastructure, we developed a tool called the Green Infrastructure Assessment (GIA). The GIA was based on principles of landscape ecology and conservation biology, and provides a consistent approach to evaluating land conservation and restoration efforts in Maryland. It specifically attempts to recognize: a variety of natural resource values (as opposed to a single species of wildlife, for example), how a given place fits into a larger system, the ecological importance of natural open space in rural and developed areas, the importance of coordinating local, state and even interstate planning, and the need for a regional or landscape-level view for wildlife conservation. The GIA identified two types of important resource lands - "hubs" and "corridors." Hubs typically large contiguous areas, separated by major roads and/or human land uses, that contain one or more of the following: Large blocks of contiguous interior forest (containing at least 250 acres, plus a transition zone of 300 feet) Large wetland complexes, with at least 250 acres of unmodified wetlands; Important animal and plant habitats of at least 100 acres, including rare, threatened, and endangered species locations, unique ecological communities, and migratory bird habitats; relatively pristine stream and river segments (which, when considered with adjacent forests and wetlands, are at least 100 acres) that support trout, mussels, and other sensitive aquatic organisms; and existing protected natural resource lands which contain one or more of the above (for example, state parks and forests, National Wildlife Refuges, locally owned reservoir properties, major stream valley parks, and Nature Conservancy preserves). In the GIA model, the above features were identified from Geographic Information Systems (GIS) spatial data that covered the entire state. Developed areas and major roads were excluded, areas less than 100 contiguous acres were dropped, adjacent forest and wetland were added to the remaining hubs, and the edges were smoothed. The average size of all hubs in the state is approximately 2200 acres. Corridors are linear features connecting hubs together to help animals and plant propagules to move between hubs. Corridors were identified using many sets of data, including land cover, roads, streams, slope, flood plains, aquatic resource data, and fish blockages. Generally speaking, corridors connect hubs of similar type (hubs containing forests are connected to one another; while those consisting primarily of wetlands are connected to others containing wetlands). Corridors generally follow the best ecological or "most natural" routes between hubs. Typically these are streams with wide riparian buffers and healthy fish communities. Other good wildlife corridors include ridge lines or forested valleys. Developed areas, major roads, and other unsuitable features were avoided.
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Liberia Teacher Training Program II 2013 EGRA Midline
datahub.usaid.gov | Last Updated 2024-07-12T09:46:39.000ZThe Liberia Teacher Training Program II (LTTP II) is a partnership between FHI 360 and RTI International to provide support to the central Ministry of Education (MOE). The overarching goal of LTTP II is to enhance pupils' learning in general, and reading proficiency in particular; establish a functional teacher professional development (PD) system; and strengthen the MOE's capacity to manage such a system. The LTTP II was originally designed to work in nine counties: Grand Gedeh, Grand Kru, Lofa, Maryland, Montserrado, Nimba, River Cess, River Gee, and Sinoe. In 2011 and 2012, because of changes in USAID policies, the number of counties was reduced to five (i.e., Bong, Lofa, Margibi, Montserrado, and Nimba), which USAID identifies as a development corridor containing a majority of the Liberian population. The LTTP II intervention drew on the EGRA Plus model to introduce similarly structured reading and math programs in grades 1, 2, and 3 to approximately 1,020 schools in four counties (i.e., Bong, Lofa, Montserrado, and Nimba) in a phased approach. Cohort 1, the first to receive support, had 792 schools. During the middle of the 2011/2012 school year, the reading program was introduced in all three grades in these schools. During the middle of the 2012/2013 school year, the mathematics program was introduced in all three grades. Cohort 2, consisting of approximately 330 schools, began participating in the programs reading and mathematics interventions during the 2013/2014 school year and continued during the 2014/2015 school year. Some changes, although not significant, were made to the intervention approach for supporting the Cohort 2 schools. Schools in the four LTTP II counties were randomly assigned to the Cohort 1 and Cohort 2 groupings. These schools were then grouped in clusters of 12 schools based on geographic proximity, which would allow the program to deliver the interventions more efficiently Cohort 1: Schools from the four target counties included in Cohort 1 served as the treatment group for the midterm assessment. These schools stopped receiving LTTP II support after the midterm assessment, but they participated in the endline assessment, as a way to determine whether the gains that were achieved during the treatment were sustained. Cohort 2: Schools included in Cohort 2 in the same four counties began to receive treatment after the midterm assessment, thus, during the final two years of the program. Cohort 2 schools served as a control to which the Cohort 1 results were compared. The performance of Cohort 2 schools were to be compared to that of Cohort 1. The biggest challenge that the program faced regarding the implementation in Cohort 2 schools was the school closings because of the Ebola crisis. Schools were closed between September 2014 and February 2015. Even after the official reopening date, with the gradual actual opening of schools that required LTTP II to wait until schools were safe to open, it took several months to distribute books to schools and to train teachers which in turn severely affected the implementation of the treatment. External Cohort: A randomly selected sample of schools outside the four target counties served as another comparator, especially after Cohort 2 began receiving treatment alongside Cohort 1. Except for a small number of schools associated with the RTTIs, schools outside the four target counties did not participate in the program during the lifetime of LTTP II. This data file contains the 2013 EGRA midline.