skip to main content

Title: Geocomputational infrastructure for population-environment data
Geocomputation is increasingly integrated with spatial data infrastructure to develop and deliver massive datasets and attendant analysis and visualization capacity to a wide range of users. IPUMS Terra is spatial data infrastructure that develops and uses geocomputational approaches to provide one of the largest collections of integrated population and environment data in the world. In this paper, we describe new efforts to fundamentally change the landscape of population-environment data by integrating, preserving, and disseminating vast amounts of aggregate census and agricultural census data. We are developing data manipulation tools and workflow management approaches to transform and standardize data as well as capture metadata. These developments in turn facilitate the processing, documenting, and intake of tens of thousands of data tables into IPUMS Terra, which then are shared with the scientific community and the broader public to advance understanding of the population and agricultural systems that are central to many complex human-environment systems.
; ; ; ;
Award ID(s):
Publication Date:
Journal Name:
Geocomputation 2019
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Modeling human activity dynamics is important for many application domains. However, there are problems inherent in modeling population information, since the number of people inside a given area can change dynamically over time. Here, a cyberGIS-enabled spatiotemporal population model is developed by combining Twitter data with urban infrastructure registry data to estimate human activity dynamics. This model is an object-class oriented space–time composite model, in which real-world phenomena are modeled as spatiotemporal objects, and people can move from one object to another over time. In this research, all spatiotemporal objects are aggregated into 14 spatiotemporal object classes, and all objects in a given space at different times can be projected down to a spatial plane to generate a common spatiotemporal map. A temporal weight matrix is derived from Twitter activity curves for each spatiotemporal object class and represents population dynamics for each object class at different hours of a day. Finally, model performance is evaluated by using a comparison to registered census data. This spatiotemporal human activity dynamics model was developed in a cyberGIS computing environment, which enables computational and data intensive problem solving. The results of this research can be used to support spatial decision-making in various applicationmore »areas such as disaster management where population dynamics plays an important role.

    « less
  2. The Kiribati 2019 Integrated Household Income and Expenditure Survey (Integrated HIES) embeds novel ecological and human health research into an ongoing social and economic survey infrastructure implemented by the Pacific Community in partnership with national governments. This study seeks to describe the health status of a large, nationally representative sample of a geographically and socially diverse I-Kiribati population through multiple clinical measurements and detailed socio-economic surveys, while also conducting supporting food systems research on ecological, social, and institutional drivers of change. The specific hypotheses within this research relate to access to seafood and the potential nutritional and health benefits of these foods. We conducted this research in 21 of the 23 inhabited islands of Kiribati, excluding the two inhabited islands—Kanton Islands in the Phoenix Islands group with a population of 41 persons (2020 census) and Banaba Island in the Gilbert Islands group with a population of 333 persons (2020 census)—and focusing exclusively on the remaining islands in the Gilbert and Line Islands groups. Within this sample, we focused our intensive human health and ecological research in 10 of the 21 selected islands to examine the relationship between ecological conditions, resource governance, food system dynamics, and dietary patterns. Ultimately, this researchmore »has created a baseline for future Integrated HIES assessments to simultaneously monitor change in ecological, social, economic, and human health conditions and how they co-vary over time.« less
  3. Agricultural systems are heterogeneous across temporal and spatial scales. Although much research has investigated farm size and economic output, the synergies and trade-offs across various agricultural and socioeconomic variables are unclear. This study applies a GIS-based approach to official Brazilian census data (Agricultural Censuses of 1995, 2006, and 2017) and surveys at the municipality level to (i) evaluate changes in the average soybean farm size across the country and (ii) compare agricultural and socioeconomic outcomes (i.e., soybean yield, agricultural production value, crop production diversity, and rural labor employment) relative to the average soybean farm size. Statistical tests (e.g., Kruskal–Wallis tests and Spearman’s correlation) were used to analyze variable outcomes in different classes of farm sizes and respective Agricultural Censuses. We found that agricultural and socioeconomic outcomes are spatially correlated with soybean farm size class. Therefore, based on the concepts of trade-offs and synergies, we show that municipalities with large soybean farm sizes had larger trade-offs (e.g., larger farm size was associated with lower crop diversity), while small and medium ones manifest greater synergies. These patterns are particularly strong for analysis using the Agricultural Census of 2017. Trade-off/synergy analysis across space and time is key for supporting long-term strategies aiming atmore »alleviating unemployment and providing sustainable food production, essential to achieve the UN Sustainable Development Goals.« less
  4. Abstract The discourse on resilience, currently at the forefront of research and implementation in a wide variety of fields, is confusing because of its multi-disciplinary/spatial/temporal nature. Resilience analysis is a discipline that allows the assessment and enhancement of the coping and recovery behaviors of systems when subjected to short-lived high-impact external shocks leading to partial or complete failure. This paper, meant for pedagogical teaching and research formulation, starts by providing an overview of different aspects of resilience in general and then focuses on communities and regions that are complex adaptive systems (CAS) involving multiple engineered infrastructures providing essential services to local inhabitants and adapted to available natural resources and social requirements. Next, for objective analysis and assessment, it is proposed that resilience be characterized by four different quantifiable sub-attributes. This paper then describes the standard technocentric manner in which different temporal phases during and in the aftermath of disasters are generally visualized and analyzed, and discusses how these relate to reliability and risk analyses. Subsequently, two prevalent types of frameworks are described and representative literature reviewed: (i) those that aim at improving general resilience via soft methods such as subjective means (interviews, narratives) and census data, and (ii) those thatmore »are meant to enhance specific resilience under certain threat scenarios using hard/objective methods such as data-driven analysis and performance-predictive modeling methods, akin to resource allocation problems in operations research. Finally, the need for research into an integrated framework is urged; one that could potentially combine the strengths of both approaches.« less
  5. Abstract Background

    The use of systems science methodologies to understand complex environmental and human health relationships is increasing. Requirements for advanced datasets, models, and expertise limit current application of these approaches by many environmental and public health practitioners.


    A conceptual system-of-systems model was applied for children in North Carolina counties that includes example indicators of children’s physical environment (home age, Brownfield sites, Superfund sites), social environment (caregiver’s income, education, insurance), and health (low birthweight, asthma, blood lead levels). The web-based Toxicological Prioritization Index (ToxPi) tool was used to normalize the data, rank the resulting vulnerability index, and visualize impacts from each indicator in a county. Hierarchical clustering was used to sort the 100 North Carolina counties into groups based on similar ToxPi model results. The ToxPi charts for each county were also superimposed over a map of percentage county population under age 5 to visualize spatial distribution of vulnerability clusters across the state.


    Data driven clustering for this systems model suggests 5 groups of counties. One group includes 6 counties with the highest vulnerability scores showing strong influences from all three categories of indicators (social environment, physical environment, and health). A second group contains 15 counties with high vulnerability scores drivenmore »by strong influences from home age in the physical environment and poverty in the social environment. A third group is driven by data on Superfund sites in the physical environment.


    This analysis demonstrated how systems science principles can be used to synthesize holistic insights for decision making using publicly available data and computational tools, focusing on a children’s environmental health example. Where more traditional reductionist approaches can elucidate individual relationships between environmental variables and health, the study of collective, system-wide interactions can enable insights into the factors that contribute to regional vulnerabilities and interventions that better address complex real-world conditions.

    « less