People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data

Abstract

In 1998, the National Research Council published People and Pixels: Linking Remote Sensing and Social Science. The volume focused on emerging research linking changes in human populations and land use/land cover to shed light on issues of sustainability, human livelihoods, and conservation, and led to practical innovations in agricultural planning, hazard impact analysis, and drought monitoring. Since then, new research opportunities have emerged thanks to the growing variety of remotely sensed data sources, an increasing array of georeferenced social science data, including data from mobile devices, and access to powerful computation cyberinfrastructure. In this article, we outline the key extensions of the People and Pixels foundation since 1998 and highlight several breakthroughs in research on human–environment interactions. We also identify pressing research problems—disaster, famine, drought, war, poverty, climate change—and explore how interdisciplinary approaches integrating people and pixels are being used to address them.

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Notes

  1. 1.

    The POPGRID Data Collaborative, a consortium of the major gridded population and settlement data producers, was established to aid users in making these assessments. The POPGRID website (www.popgrid.org) includes data documentation, tables comparing data products, and a web map service that facilitates visualization and comparison across data products.

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Acknowledgments

This paper is the result of a 2018 Population-Environment Research Network (PERN) cyberseminar for which the co-authors served as organizers and invited experts.

Funding

The authors would like to acknowledge the support under NASA contract NNG13HQ04C for the continued operation of the Socioeconomic Data and Applications Center (SEDAC), which underwrites PERN; the Minnesota Population Center (P2C HD041023), funded through a grant from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD); and the Office of Naval Research (ONR) award no. N00014-16-1-2543 (PSU no. 171570) and US Army Corps of Engineers ERDC-GRL award no. W9126G-18-2-0037 (PSU no. 209549).

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Correspondence to Tracy A. Kugler.

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Appendix

Appendix

IPUMS Terra

IPUMS Terra aims to foster more widespread use of linked population and environmental data by taking the heavy lifting of data integration and transformation off the hands of individual researchers. IPUMS Terra utilizes location-based integration to transform among three data structures: individual-level population microdata, area-level data describing places defined by geographic boundaries, and pixel-based raster data. The IPUMS Terra data access system provides a workflow through which users first select the data structure in which they would like to receive their output data. They then select data of interest from one or more of the source data structures and make basic decisions about how transformations are conducted. The system processes the requested data, handling any necessary transformations, and delivers a customized, integrated dataset in the user’s chosen data structure.

The transformations performed by IPUMS Terra depend on the output data structure and the types of input data selected (Kugler et al. 2015; Ruggles et al. 2015). Users may choose to receive area-level output data (describing administrative units) and include both census-based aggregate population data and raster data on land cover or climate as inputs. IPUMS Terra then transforms the raster data by calculating summary measures (e.g., % area of forest cover, mean annual precipitation) for the administrative units described in the aggregate population data. Area-level population and environmental data can also be attached as contextual variables to individual microdata records, based on the administrative unit in which the person lives. Conversely, IPUMS Terra can distribute values from area-level population data across the pixels located within each administrative unit to produce raster output.

The data available through IPUMS Terra encompass a range of population and environmental characteristics. IPUMS Terra’s population data are primarily drawn from IPUMS International (Minnesota Population Center 2018), which provides over one billion microdata records from 94 countries, with multiple census years available for many countries. IPUMS Terra has also pre-tabulated a series of area-level variables from the microdata. On the pixel side of the data collection, IPUMS Terra includes data on land cover (European Commission, Joint Research Centre 2003; Friedl et al. 2010), harvested area and yield of 175 individual crops (Monfreda et al. 2008; Ramankutty et al. 2008), long-term climate baselines (Hijmans et al. 2005), and time-series climate data (Harris et al. 2014). Transformations between population microdata, area-level data, and raster data are supported by IPUMS Terra’s collection of geographic administrative unit boundary data. For most countries, IPUMS Terra provides first- and second-level administrative unit boundaries.

Giovanni and AppEEARS

Giovanni and AppEEARS are tools developed by NASA to facilitate remote sensing data discovery, extraction, processing, and visualization. Giovanni’s focus is largely on remotely sensed atmospheric data of relatively low spatial resolution and very high temporal resolution, including hourly datasets. The tool uses server-side processing to process requests “on the fly,” and produces map and graph visualizations. AppEEARS’ focus is largely on remotely sensed terrestrial data of moderate spatial and temporal resolution (30 m to 1 km; daily time steps or monthly composites). AppEEARS delivers results within minutes or hours (depending on the degree of processing) by email notification. Both tools enable analysis of time trends—Giovanni for one pixel or averaged over a larger area comprising many pixels, and AppEEARS for multiple points or polygons. Additional tools and use cases of Giovanni and AppEEARS are described in Adamo and de Sherbinin (2018).

Giovanni’s remotely sensed data assets are concentrated primarily in the areas of atmospheric composition, atmospheric dynamics, global precipitation, hydrology, and solar irradiance. More than 1,850 variables are available, searchable via a keyword and faceted search. Example variables of potential interest to social and health scientists include precipitation, flooding, air temperature, PM2.5 particulate pollution, and UV exposure. Mapping options include time averaging, animation, precipitation accumulation, time-averaged overlay of two datasets, and user-defined climatology. For time-series plots, options include area averaged, differences, and seasonal data (e.g., July–August for daily max temperatures over multiple years). Visualization features include animation, interactive scatterplots, contouring, and scaling (linear or log).

AppEEARS provides access to more than 100 datasets from Terra & Aqua MODIS, the Shuttle Radar Topography Mission (SRTM v3), Web Enabled Landsat Data (WELD), Gridded population of the World (GPW), and NASA data products derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. Selected variables of interest to social scientists include land surface temperature, land cover type, population counts and densities, elevation, and vegetation indices. A key feature of AppEEARS is the ability to subset large geospatial datasets and to extract data for specific locations or regions around the world. Data can be extracted using an area sample via vector polygons (bounding box, Shapefile, or GeoJSON) or a point sample with geographic coordinates. In addition to data values, the user also receives quality values. The results preview includes interactive visualizations, decoded quality information, and summary statistics.

Google Earth Engine

In addition to tools developed by the research community, Google is leveraging their extensive hardware platform and computing resources to manage and process increasingly large volumes of remotely sensed data. Google Earth Engine (GEE) serves as a free platform (free for noncommercial use) where users can easily visualize and download a relatively wide range of spatial data (Gorelick et al. 2017). GEE hosts satellite and other geospatial data gathered from a range of sources including Landsat, Sentinel, some MODIS data, Climate Hazards Infrared Precipitation with Stations (CHIRPS), and many other datasets. Satellite images are processed daily and GEE provides data for some areas covering up to 40 years. Users can specify their locations and temporal scale of interest and download data for analyses in other spatial or statistical analyses packages.

Beyond hosting satellite and geospatial data, GEE also supports simple geospatial analysis through its own Explorer product and more complex analyses through an application programming interface (API). The API allows researchers and developers to leverage both the GEE data collection and Google’s cloud-based computing resources. The combination of data and processing power makes the development of modeled environmental products at high resolution and global extent feasible (Hansen et al. 2013; Pekel et al. 2016). For example, the Hansen et al. forest change data, based on 650,000 30-m resolution Landsat scenes, would have taken 15 years to process on a single computer, but was completed on GEE in a matter of days (University of Maryland 2013). The API also enables near real-time and interactive tools for detecting and monitoring land cover change (Bey et al. 2016; Hansen et al. 2016).

Software for handling spatial data

In the 20 years since the publication of People and Pixels, growth in the availability of high spatial and temporal resolution remotely sensed data has coincided with growth in both software designed to work with spatial data and the ability of general purpose software to handle spatial data. As of the late 1990s, remotely sensed and other spatial data were manipulated and analyzed in specialized software packages, such as Esri’s ArcInfo, Erdas Imagine, ENVI, and GRASS, on stand-alone computers or servers. These packages, with the exception of GRASS, are commercial products with attendant cost considerations.

The original spatial software packages have continued to evolve, expand, and become more accessible to nonspecialists. Open-source geospatial software has also expanded greatly, leading to the development of organizations like the Open Source Geospatial Foundation (www.osgeo.org) that, in turn, foster further growth. As a result, awareness and use of geographic information systems (GIS) and other spatial software now extend across a broad range of disciplines and educational levels. For example, GIS is increasingly used in secondary schools in subjects ranging from natural sciences to history (Kerski et al. 2013).

In addition to specialized spatial software, scholars increasingly use general-purpose, open-source software, such as R, Python, and SQL, to process, analyze, and integrate remotely sensed data. The availability of support for spatial data in these general-purpose tools enables researchers to conduct both spatial and nonspatial analysis seamlessly in a single environment. R and Python are free, widely used, open-source languages that may be modified and enhanced by any programmer in the world. R is a programming language and environment focused mainly on statistical analysis and graphing. The R community has developed spatially oriented packages supporting geographic data structures, geospatial processing operations, geostatistics, and geovisualization. These packages allow researchers to integrate remotely sensed and other spatial data into analytical workflows, which may include statistical modeling or other statistical techniques, within a single software ecosystem (Lovelace et al. 2018). R packages for handling spatial data and analysis, like sp (supporting spatial data) and more recently sf (supporting “simple features”) raster, rgdal, GISTools, spdep, gstat, and many others, have helped to make R an ideal platform to merge, manipulate, and display spatial data. Python is an open-source, general-purpose, object-oriented programming language used in a wide variety of domains. Software developers have written Python libraries dedicated to geographic data, including remotely sensed data. These libraries support spatial data input/output, geoprocessing, visualization, and spatial statistics and modeling (Rey 2017). Support for spatial data and spatial queries has also been incorporated into relational databases, both commercial (e.g., SQL Server, Oracle) and open source (e.g., PostGIS).

Spatial analysis methods

This paper has primarily focused on the data landscape and the conceptual linkages and challenges related to contemporary “people and pixels” analyses. Beyond the scope of this project are the many discussions related to spatial statistics and other statistical or mathematical models and approaches that are used to analyze these data. As with any other spatial analysis work, researchers should be aware of the available methods and their capabilities and limitations for particular applications. Those interested in this area of research are advised to consider work by Schabenberger and Gotway (2017), Maxwell et al. (2018), Tong and Murray (2012), Long and Nelson (2013), and many others.

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Kugler, T.A., Grace, K., Wrathall, D.J. et al. People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data. Popul Environ 41, 209–234 (2019). https://doi.org/10.1007/s11111-019-00326-5

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Keywords

  • Remote sensing
  • Population data
  • Human dimensions of global change
  • Data integration
  • Mobile device data