A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints

Racial geography, mapping spatial distributions of different racial groups, is of keen interest in a multiracial society like the United States. A racial dot map is a method of visualizing racial geography, which depicts spatial distribution, population density, and racial mix in a single, easy-to-understand map. Because of the richness of information it carries, the dot map is an excellent tool for visual analysis of racial distribution. Presently-used racial dot maps are based on the Census data at the tract or the block level. In this paper, we present a method of constructing a more spatially-accurate racial dot map based on a sub-block-resolution population grid. The utility of our dot maps is further enhanced by placing dots on the map in random order regardless of the race they represent in order to achieve a more accurate depiction of local racial composition. We present a series of comparisons between dot maps based on tract, block, and grid data. The advantage of a grid-based dot map is evident from the visual comparison of all maps with an actual image of the mapped area. We make available the R code for constructing grid-based dot maps. We also make available 2010 grid-based racial dot maps for all counties in the conterminous United States.

Download Full-text

JUNE: open-source individual-based epidemiology simulation

10.1101/2020.12.15.20248246 ◽

2020 ◽

Author(s):

Frank Krauss ◽

Joseph Bullock ◽

Carolina Cuesta-Lazaro ◽

Arnau Quera-Bofarull ◽

Miguel Icaza-Lizaola ◽

...

Keyword(s):

Social Interactions ◽

Open Source ◽

Census Data ◽

Initial Model ◽

Mortality Statistics ◽

Virtual Population ◽

Social Mixing ◽

Open Source Framework ◽

Detailed Simulation

We introduce JUNE, an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity, and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. JUNE provides a suite of flexible parameterisations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper we apply JUNE to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

Download Full-text

J une : open-source individual-based epidemiology simulation

Royal Society Open Science ◽

10.1098/rsos.210506 ◽

2021 ◽

Vol 8 (7) ◽

pp. 210506

Author(s):

Joseph Aylett-Bullock ◽

Carolina Cuesta-Lazaro ◽

Arnau Quera-Bofarull ◽

Miguel Icaza-Lizaola ◽

Aidan Sedgewick ◽

...

Keyword(s):

Social Interactions ◽

Open Source ◽

Census Data ◽

Initial Model ◽

Mortality Statistics ◽

Virtual Population ◽

Social Mixing ◽

Open Source Framework ◽

Detailed Simulation

We introduce J une , an open-source framework for the detailed simulation of epidemics on the basis of social interactions in a virtual population constructed from geographically granular census data, reflecting age, sex, ethnicity and socio-economic indicators. Interactions between individuals are modelled in groups of various sizes and properties, such as households, schools and workplaces, and other social activities using social mixing matrices. J une provides a suite of flexible parametrizations that describe infectious diseases, how they are transmitted and affect contaminated individuals. In this paper, we apply J une to the specific case of modelling the spread of COVID-19 in England. We discuss the quality of initial model outputs which reproduce reported hospital admission and mortality statistics at national and regional levels as well as by age strata.

Download Full-text

AMEND: Open Source and Data-Driven Oversight of Water Quality in New England

Media and Communication ◽

10.17645/mac.v7i3.2136 ◽

2019 ◽

Vol 7 (3) ◽

pp. 91-103

Author(s):

Nathan Edward Sanders

Keyword(s):

Water Quality ◽

Open Source ◽

Census Data ◽

Environmental Data ◽

Federal State ◽

Online Data ◽

Web Resource ◽

Civic Organizations ◽

Sewer Overflows ◽

State And Local

The advent of government transparency through online data publication should provide a transformative benefit to the information gathering practices of civic organizations and environmental advocates. However, environmental agencies and other reporters often disseminate this critical data only in siloed repositories and in technically complex, inconsistent formats, limiting its impact. We have developed a new open source web resource, the Archive of Massachusetts ENvironmental Data or AMEND, which curates information relating to federal, state, and local environmental stewardship in Massachusetts, focused on water quality. We describe the construction of AMEND, its operation, and the datasets we have integrated to date. This tool supports the development and advocacy of policy positions with published analyses that are fully reproducible, versioned, and archived online. As a case study, we present the first publicly reported analysis of the distributional impact of combined sewer overflows on Environmental Justice (EJ) communities. Our analysis of the historical geospatial distribution of these sewer overflows and block-level US Census data on EJ indicators tracking race, income, and linguistic isolation demonstrates that vulnerable communities in Massachusetts are significantly overburdened by this form of pollution. We discuss applications of this analysis to the state-level legislative process in Massachusetts. We believe that this approach to increasing the accessibility of regulatory data, and the code underlying AMEND, can serve as a model for other civic organizations seeking to leverage data to build trust with and advocate to policymakers and the public.

Download Full-text

Racial dot maps based on dasymetrically modeled gridded population data

10.31235/osf.io/62mk3 ◽

2020 ◽

Author(s):

Anna Dmowska ◽

Tomasz Stepinski

Keyword(s):

United States ◽

Visual Analysis ◽

Census Data ◽

Population Data ◽

Random Order ◽

The United States ◽

Racial Groups ◽

Grid Data ◽

Population Grid ◽

Grid Based

Racial geography - mapping spatial distributions of different racial groups is of keen interest in a multiracial society like the United States. Racial dot map is a method of visualizing racial geography which depicts spatial distribution, population density, and racial mix in a single, easy-to-understand map. Because of the richness of information it carries, the dot map is an excellent tool for visual analysis of racial distribution. Presently used racial dot maps are based on the census data at the tract or the block level. In this paper, we present a method of constructing a more spatially accurate racial dot map based on a sub-block resolution population grid. The utility of our dot maps is further enhanced by placing dots on the map in random order regardless of the race they represent in order to achieve a more accurate depiction of local racial composition. We present a series of comparisons between dot maps based on tract, block, and grid data. The advantage of a grid-based dot map is evident from the visual comparison of all maps with an actual image of the mapped area. We make available the R code for constructing grid-based dot maps. We also make available 2010 grid-based racial dot maps for all counties in the conterminous United States.

Download Full-text