Geographic Units Research Articles

Creating a robust coordinated data and policy framework for addressing substance use issues in the United States

The ongoing opioid epidemic has been met with the inadequate use of data-informed approaches to respond to the crisis. Although data relevant to opioid and substance use do exist and have been utilized for research in the literature and practice, they have not been prepared for cross-sector coordination and for providing practical intelligence to inform policy planning directly. In this article, we share our views on how data can better serve the purposes of informing policy and planning to maximize population health and safety benefits. Based on our experience in advising state policymakers on developing settlement allocation strategies based on empirical data, we discuss several issues in the data, including coverage, specificity in drug types, time relevance, geographic units, and access, which may hinder data-informed policymaking. Following these discussions, we envision a coordinated data and policy framework as an ideal case to ensure access to meaningful and timely data and harness the full potential of the data to inform policy to combat the continuing epidemic.

Coronarographie et angioplastie coronaire ambulatoire ; pourquoi, comment, pour quels patients ?

There is great heterogeneity in lengths of stay in interventional cardiology but the number of outpatient procedures is increasing. The expected benefits of an outpatient procedure are numerous and non-inferiority of this strategy has been demonstrated. Proper selection of patients eligible for this treatment is essential to minimize the risks of unplanned hospitalization and early complications. It is based on clinical, medico-social and organizational criteria. Perfect management of the care pathway based on an organizational unit and a geographical unit is essential.

Sense of community, life satisfaction and psychological distress among seniors – HUNT4 study, Norway

Abstract Background Strong sense of community with one’s neighbors may benefit psychosocial well-being. Moreover, local communities with high level of belonging and social cohesion can have a positive impact on public mental health. This study aims to investigate associations between sense of community and life satisfaction - at both an individual and local community level in a Norwegian county. Methods Data from Trøndelag Health Study 4 (HUNT4: 2017-2019) on 22 939 participants were linked to geographic data of residence from Statistics Norway. The basic statistical unit in Norwegian, known as “grunnkrets,” serves to identify participants’ local communities. These units are subdivisions of municipalities, covering geographically contiguous areas that typically include a few hundred inhabitants. This population-based study enables investigation of variation in sense of community, trust, life satisfaction and psychological distress on individual and community level. Results Seniors (median age 70.3 years) with strong sense of community with their neighbors had more than three times higher odds (95%CI 3.08-3.93) of high level of life satisfaction and less than 0.3 times lower odds (95%CI 0.26-0.33) of psychological distress symptoms vs those with low sense of community, adjusted to sociodemographic variables. The average sense of community varied between local communities (N = 284). At this local geographical unit, strong sense of community was associated with high level of trust, enjoying the neighborhood and high life satisfaction. Conclusions The study can inform public health policy to consider development of social infrastructures that bolster sense of community among seniors within neighborhoods. Policy and action plans targeting to increase the overall sense of community in neighborhoods may contribute to create mental health-promoting and age-friendly local communities in Norway. Key messages • At individual level, sense of community with neighbors are associated with life satisfaction and psychological distress among Norwegian seniors. • The average sense of community varies between geographic local areas and are associated with level of trust, enjoying the neighborhood, average life satisfaction, and psychological distress.

Engagement of adults with congenital heart disease in research: overcoming barriers relating to social deprivation

Abstract Background In adult congenital heart disease (ACHD), social deprivation is associated with adverse health outcomes, such as increased inpatient admissions and major adverse cardiovascular events (1). Research studies on this topic often rely on patients engaging with surveys, and the characteristics of non-respondents in relation to social deprivation remain unknown. Purpose We assessed the association between social deprivation and response to a research survey in the ACHD population. Methods We included all individuals in the UK who were invited to participate in the APPROACH-IS II study (2), which assessed patient-reported outcomes in ACHD. A validated index of relative deprivation was derived from household postcodes, based on the 2019 Index of Multiple Deprivation (IMD) scores for geographic units in England (3). A lower IMD decile indicates a greater degree of deprivation. We compared patient engagement rates between 2 centres utilising different recruitment approaches: a Southern centre (South) relying on recruitment during mainly telephone or video consultations, and a Northern centre (North) recruiting patients during face-to-face clinic appointment, with the help of an ACHD clinical and research nurse specialist. Results In total, 593 patients were asked to take part in APPROACH-IS II in the UK: 324 patients from the Southern centre and 269 patients from the Northern centre, of whom 130(40.1%) and 209(77.7%) responded, respectively (p<0.0001). In the South, patients approached were homogeneously spread amongst IMD deciles (6.0[4.0-9.0]) and respondents were significantly less deprived (higher IMD decile) than non-respondents (7.0[5.0-9.0] vs 6.0[4.0-8.0], p=0.02). Indeed, the response rate in areas of higher deprivation (IMD decile<=4) was significantly lower than in the least deprived areas (IMD >=8) (31.3% vs 47.0%, p=0.03). In the North, the distribution of deprivation in patients was skewed towards lower IMD deciles, with a significantly higher proportion of patients from areas of higher deprivation (Figure A, 26 vs 99(30.6%) in the South, p<0.0001). There was no difference in IMD decile between respondents and non-respondents in the North (Figure B, 5.0[2.0-7.0] vs 4.0[2.0-7.0], p=0.26). When comparing patients living in the most deprived areas (IMD <=4), there was a large and significant difference in response rate between centres (p<0.0001). Conclusions In our population, deprivation appears to have a significant impact on the likelihood of patients engaging with clinical research. An enhanced approach, involving face-to-face contact and the involvement of a dedicated clinical nurse specialist, can improve recruitment to research studies and abolish the effect of socioeconomic status on patient engagement.Figure 1A and B

Value Assessment and Prediction of Regulating Ecosystem Services in Hainan Tropical Rainforest National Park, China

Ecosystem services serve as a bridge between the ecological environment and human society. The quantitative analysis and forecasting of ecosystem services can provide references for regional eco-environmental assessments and land-use planning for the future. In this study, taking Hainan Tropical Rainforest National Park (HTRNP) as an example, the value of regulating ecosystem services (RESs) in 2020 was assessed via ArcGIS 10.1 and the InVEST 3.5 model, and the per-unit value of RESs was calculated for different LULC types. In addition, in accordance with the Overall Planning for HTRNP and the objective of optimizing RESs, the value of RESs in short-term (to 2030) and long-term (to 2050) scenarios was forecast via a linear programming model. The results are as follows: (1) The RES value of HTRNP in 2020 was CNY 2090.67 × 108, with climate regulation accounting for the largest proportion; the spatial distribution of RESs in the eastern and central areas was higher than that in the western area, but different indicators of RESs differed in their spatial patterns in varied geographic units. (2) The natural forest ecosystem in HTRNP accounts for 76.94% of the total area but 84.82% of the total value of RESs. The per-unit value is ranked from highest to lowest as follows: montane rainforests > wetlands > lowland rainforests > lowland secondary rainforests > tropical coniferous forests > deciduous monsoon rainforests > tropical cloud forests > shrub forests > timber forests > economic forests > rubber forests > grasslands > farmlands > settlements. (3) In the short-term scenario, the value of RESs is CNY 2216.64 × 108, an increase of CNY 118.97 × 108 compared to 2020, with an increase rate of 5.67%. In the long-term scenario, the value of RESs is CNY 2472.48 × 108, an increase of CNY 374.81 × 108 compared to 2020, with an increase rate of 17.87%. The results reveal the significance of ecosystem services in the national park and can inform more targeted and scientifically sound decision-making in the future.

Varying size and shape of spatial units

When an analysis over a specific geographic area is performed, the way that area is divided into regions can affect the outcome of the analysis. Results obtained based on different geographic units can be conflicting. This issue is known as the Modifiable Areal Unit Problem (MAUP). The objective of this paper is to understand the extent to which the regional setting influences the results of an analysis with spatially aggregated variables, with a focus on agglomeration effects, in the case of Germany. Relying on a sample of manufacturing firms over 7 years we estimate a fixed effects model to explain the firm-specific total factor productivity in dependence of region-based agglomeration variables. We simulate 1000 regional settings of Germany on three scales and overtake thereby some characteristics of the administrative units, which are used as the baseline. We infer that the spatial scale and shape matter in the case of Germany.

Analysis of recurrent event data with spatial random effects using a Bayesian approach.

Recurrent event data, which represent the occurrence of repeated incidences, are common in observational studies. Furthermore, collecting possible spatial correlations in health and environmental data is likely to provide more information for risk prediction. This article proposes a comprehensive proportional intensity model considering spatial random effects for recurrent event data using a Bayesian approach. The spatial information for areal data (where the spatial location is known up to a geographic unit such as a county) and georeferenced data (where the location is exactly observed) is examined. A traditional constant baseline intensity function, as well as a flexible piecewise constant baseline intensity function, are both under consideration. To estimate the parameters, a Markov chain Monte Carlo method with the Metropolis-Hastings algorithm and the adaptive Metropolis algorithm are applied. To assess the performance of model fitting, the deviance information criterion and log pseudo marginal likelihood are proposed. Overall, simulation studies demonstrate that the proposed model is significantly better than models that do not consider spatial effects if spatial correlations exist. Finally, our approach is implemented using a dataset related to the recurrence of cardiovascular diseases, which incorporates spatial information.

Developing a Geo-Spatial Database and Standard Queries for Election Management by Stakeholders within Jos South, Nigeria

One of the major set-backs of the 2023 general elections in Nigeria as highlighted by YIAGA Africa (2024) was the failure of the body saddled with election management which is Independent Electoral Commission (INEC), to deploy Geo-spatial Database (GSDB) as a repository in the efficient management processes which enable all election stakeholder participate seamlessly and interactively. The modus of oparandy for the geo-spatial database was hierarchical by dis-aggregating GSDB to the lowest geographical unit. The recent court verdict on re-delineation of electoral wards in Rivers State did not go down well with the political pundits prior to the forth coming local government election in the state. It surffices to say that, INEC from the on-set never had a geo-spatial database from which delineation and update of electoral wards, polling units can be carried out This will further build confidence and integrity, then reducing litigations that characterized the 2023 elections. The GSDB used was predicated on file geo-database of ArcGIS, where both spatial and attribute data of the study were stored; and standard query languages that are pertinent to elections were issued at prompt which generated maps and texts for decision making by all stakeholders.

Presence of Ochratoxin A Residues in Blood Serum of Slaughtered Pigs in Greece.

This study aimed to assess the presence of ochratoxin A (OTA) residues in the blood serum of slaughtered pigs in Greece. Samples were obtained from 1695 healthy slaughtered pigs originating from 113 different farms located in 21 geographic regional units in 8 different geographic regions of Greece and were analyzed using an immunosorbent assay (ELISA) and high-performance liquid chromatography with fluorescence detector (HPLC-FD). OTA contamination assessment showed that 782 (46.1%) and 1233 (72.7%) samples were OTA-positive, with a concentration range of 0.20-5.38 μg/L and 0.15-5.96 µg/L according to ELISA and HPLC-FD analysis, respectively. Also, 88 (77.9%) and 108 (95.6%) of farms were found to be OTA-positive by ELISA and HPLC-FD analysis, respectively. The highest OTA serum positivity rate (>98%) and toxin level (5.96 µg/L) determined by HPLC-FD were observed in the Thessaly region, whereas a high prevalence of up to 100% (range 75-100%) was found on farms in the Crete Island region. The detection of OTA in the serum of slaughtered pigs in different regions in Greece poses a risk for animal and human health and highlights the need for constant OTA monitoring in the swine industry and pork meat production facilities.

Transfer learning reconstructs submarine topography for global mid-ocean ridges

Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which further limits ocean research at the global scale. Here, we divide the global mid-ocean ridge system into 2805 tiles and reconstruct their high-resolution topography by using a transfer learning approach with freely available low-resolution digital elevation models (DEMs) and limited high-resolution DEMs. A high-frequency terrain feature-based deep residual network is proposed to generate high-resolution global mid-ocean ridge DEMs. In this network, topographic knowledge related to mid-ocean ridges is integrated and quantified to improve the learning efficiency and reconstruction quality of the network. A series of verifications and evaluations demonstrate the reliability of reconstructed topographies for submarine topography research. We observe that reconstructed topography can achieve good environmental understanding and information acquisition in the global mid-ocean ridge range. We find that the complexity of the previous terrain environment is underestimated by 26.63% in terms of the slope gradient and by 14.95% in terms of terrain relief, while a 101.10% information improvement can be obtained for the reconstructed topography. The reconstructed topography indicates that diverse and intricate topographical environments of mid-ocean ridges exist among different ocean regions. The proposed transfer learning method for reconstructing high-resolution mid-ocean ridge topographies is valuable and can be utilized for reconstructing information in regions that are difficult to observe directly and lack sufficient data.

Abstract C009: An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities

Abstract Introduction: Obesity has been associated with aggressive prostate cancer (PCa). Environments that promote obesity, known as obesogenic environments, may also contribute to PCa progression at the population level. However, studies exploring these factors are limited. The purpose of this study was to examine the association between neighborhood obesogenic factors with PCa mortality rates in Chicago. Methods: We used an ecological study design with Chicago Community Areas (n=77) serving as the geographical area unit of analysis. All community area-level data were obtained from the Chicago Health Atlas public data portal. For each area, two obesogenic factors were examined: food environment and park access. The number of healthy and unhealthy grocery stores was used to characterize the food environment. The retail food establishments data was retrieved for the year 2013. The stores were classified into chain grocery stores, non-chain grocery stores, liquor stores, and convenience stores which included dollar tsores, corner stores and gas stations. Chain and non-chain grocery stores were grouped as healthy food access, and other (convenience store, dollar store and liquor store) as unhealthy food access. The area of public parks per 100,000 community area population was used as a measure of park access. An exclusive park area for each community was calculated by intersecting the community area and park area shape files from the year 2016. The outcome variable was community area-level PCa mortality rate age-adjusted to the community area male population. To normalize the data, all included variables were standardized to zero mean and unit standard deviation. Linear regression models analyzed the association of the standardized measures of obesogenic environment (individually and simultaneously) with age-adjusted rates using the Statistical Analysis Software 9.4. Results: The mean number of healthy (0 - 11) and unhealthy (0 - 10) food stores in each community area were 3.1 and 2.6 respectively and mean park area (14.0 - 4096.5 acres) available in each community area was 453.5 acres per 100,000 population. The mean (0 – 29.6) PCa mortality was 9.8 per 100,000 population. Healthy food access was inversely associated with PCa mortality rates, where for every for one 1-standard deviation increase in healthy food access standard deviation, there is a 0.40 unit decrease in community area PCa mortality rate standard deviation (p=0.002) while accounting for the other obesogenic factors (unhealthy food access and park access). Conclusion: The findings of this study suggest that the neighborhood access to healthy food may be a determinant of neighborhood PCa mortality rates. Ecological studies are helpful to understand the impact at the geographical level. Methodological challenges and opportunity in ecological analyses, such as addressing bias and multivariable control of confounding, will be discussed. Citation Format: Ravneet Kaur, Jiehuan Sun, Nebiyou Y. Tilahun, Vincent L. Freeman. An exploratory ecologic analysis of the association of neighborhood obesogenic factors with prostate cancer mortality in Chicago, Illinois: Challenges and opportunities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C009.

Abstract A015: Small area-estimation for low mammography screening: Geographic and racial disparities in mammography screening, 2015 – 2022

Abstract Background: Mammography screening is associated with at least a 20% reduction in breast cancer mortality. However, there are racial/ethnic differences in breast cancer mortality. For example, non-Hispanic Black (NHB) women are significantly more likely to die of breast cancer than non-Hispanic White (NHW) women. Few studies have reported small-area estimates for mammography screening among the entire US population; differences by race were either not examined or resulted in mixed findings. The American Cancer Society (ACS) and the United States Preventive Services Taskforce (USPSTF) recommend regular mammography screening from age 40 through 74 years. The current study aims to identify areas of up-to-date mammography screening and to elucidate whether mammography screening varied by geographical location, race, or ethnicity. Methods: Mammography screening within the past 12 months was retrieved from the 2015-2022 Behavioral Risk Factor Surveillance System data. The study population consisted of adult women aged 40 and older residing within the United States. 2020 US census tract population estimates were used to generate gender (all women), race/ethnicity (NHB, NHW, non-Hispanic Asian, and Hispanic/Latina), income, and age group weights for each US census tract. Synthetic estimates of mammography screening uptake at the census tract were conducted using our novel PARIS model (Probability of mammography screening within the past year, given age, race/ethnicity, income, and state) using a multilevel logistic regression model. Regional differences in mammography screening by US Census Bureau regions and divisions were examined. Results: Up-to-date mammography among all women in the sample was 55.98% (SD = 2.07; F (3, 72429) = 2886.81, p < .0001). Mammography screening examined by race indicated the greatest screening rates among non-Hispanic Black women (M = 60.78, SD = 3.11) and least among Asian women (M = 48.68, SD = 3.15). Among four US Census Bureau regions, the West region had the lowest rates of mammography screening among women (M = 54.74, SD = 1.85) while the South region had the highest rates (M = 56.55, SD = 2.18; F(3, 72429) = 2886.81, p < .0001). After further parsing geographical units, the West Pacific region, among the 9 US Census Bureau divisions, had the lowest mammography screening (M = 54.53, SD = 1.81) while the East South Central division had the highest rates of mammography screening (M = 56.94, SD = 2.10; F(8, 72429) = 1360.60, p < .0001).Conclusion: Using novel geospatial methods, the current study elucidated significant differences in mammography screening by race and place. These results suggest that Black women and women living in the South have higher up-to-date mammography screening compared to other women. Thus, the existing inequities in breast cancer outcomes may be explained by differences in the cancer care continuum (delays in follow-up care, treatment, and breast cancer survival). Further examination of the interplay between screening uptake, late-stage diagnosis, and death is needed. Citation Format: Sydney P. Howard, Justin X. Moore, Elinita Pollard, Ryan Johnson, Lovoria Williams, Adebola Adegboyega, Jie Chen, Jorge Cortes, Aimee James. Small area-estimation for low mammography screening: Geographic and racial disparities in mammography screening, 2015 – 2022 [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A015.

How to build vibrant communities by utilizing functional zones? A community-detection-based approach for revealing the association between land use and community vibrancy

Investigating the association between land use and urban vibrancy is crucial for sustainable urban development. To that end, according to the four conditions proposed by Jacobs (1962), suitable units should be identified before investigating this association. In this study, we consider spatial-temporal human activity patterns and propose a novel community-detection approach integrating graph neural network (GNN) and gated recurrent unit (GRU) to identify suitable geographic units. Then, the impact of land-use-relevance variables on community vibrancy is measured by the geographically and temporally weighted regression (GTWR) model. Using the cell phone signaling data from Beijing, we initially identify 66 communities, primarily comprising intra-travel (the origin and destination are in the same community) and a small proportion of inter-community travel, showing distinct vibrancy patterns and different land use characteristics. Then, we develop a GTWR model to reveal the associations between land use and community vibrancy, revealing that land-use-based variables, such as mixed land use and densities of points of interest (POIs), have spatial-temporal-heterogeneous impacts on community vibrancy, implying the heterogeneity among the development focus of communities, such as population density and functional zone structure. Consequently, we propose heterogeneous development strategies for communities, which could offer valuable suggestions for enhancing urban vibrancy.

The 500-meter long-term winter wheat grain protein content dataset for China from multi-source data

In China, the exigency for precise wheat grain protein content (GPC) data rises with growing food consumption demands and global market competition. However, due to the lack of extensive, prolonged high-resolution benchmark data, previous GPC studies have primarily focused on experimental fields, small geographic units, and limited temporal scopes. Additionally, the diverse geographical terrain in China exacerbates the challenges of large-scale GPC estimation. To address this challenge and the data gap, the first 500-meter spatial resolution, long-term winter wheat dataset covering major planting regions in China (CNWheatGPC-500) was created by integrating multi-source data from ERA5 and MODIS. The results demonstrate that the GPC estimation model based on hierarchical linear model significantly outperformed other conventional models. The validation dataset exhibited an R2 of 0.45 and an RMSE of 0.96%. In cross-validation, the RMSE values ranged from 0.90% in Gansu to 1.32% in Anhui. For leave-one-year-out cross-validation, the RMSE values ranged from 0.77% to 1.11%. CNWheatGPC-500 offers valuable insights for enhancing wheat production, quality control, and agricultural decision-making.

Morphometric Analysis and Prioritization of Sub-Watersheds Located in Heterogeneous Geographical Units—Case Study: The Buzău River Basin

The present study evaluates selected morphometric characteristics of 39 direct tributaries of the Buzău River (area of 4112 km2) to prioritize development actions in these sub-watersheds, which are distributed in three geographical regions within the full Buzău River watershed: the Carpathian, Subcarpathian, and plains regions. By combining a variable number of morphometric parameters, using various mathematical models, we can evaluate the state of balance or imbalance in a watershed and identify sub-watersheds vulnerable to natural processes. Prioritization was achieved using composite (Cp) values by comparing two scenarios. In the first scenario, 11 derived morphometric parameters were used, and in the second scenario, another 7 parameters were added. The obtained values were grouped into five classes (very high, high, medium, low, and very low). Due to the heterogeneity of relief units among geographical regions, there are classification differences for sub-watersheds among those regions. Watersheds classed as very high priority for intervention actions are located in the Carpathian (SW1, SW2, SW15, SW16) and Subcarpathian areas (SW24, SW30), which are characterized by high relief energy and pronounced slope instability, which leads to an increase in the risk of flooding and land degradation. For comparison, all sub-watersheds in the plains region, are classified as very low or low priority. Since the arithmetic mean is sensitive to extreme values, its use in the calculation of composite (Cp) values causes the values to be “eclipsed” and the sub-watersheds to be placed in different prioritization classes (SW18 and SW34), depending on the analyzed scenario, due to the large number of parameters and classified sub-watersheds.

Geographic Spines in the 2020 Census Disclosure Avoidance System

The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geographic units, which is known as the geographic spine. After post-processing these noisy measurements, DAS outputs a formally private database with fields indicating location in the standard census geographic spine, which is defined by the United States as a whole, states, counties, census tracts, block groups, and census blocks. This paper describes how the geographic spine used internally within DAS to define the initial noisy measurements impacts accuracy of the output database. Specifically, tabulations for geographic areas tend to be most accurate for geographic areas that both 1) can be derived by aggregating together geographic units above the block geographic level of the internal spine, and 2) are closer to the geographic units of the internal spine. After describing the accuracy tradeoffs relevant to the choice of internal DAS geographic spine, we provide the settings used to define the 2020 Census production DAS runs.

Ecosystem health assessment of the largest lake wetland in the Yellow River basin using an improved vigor-organization-resilience-services model

Wuliangsu Lake is the largest lake wetland in the Yellow River basin, it is the Northern Sand Belt of China, which is essential to maintaining ecological security in northern China and the areas downstream. With the increasing environmental problems, the basin has become a sensitive and complex geographical unit, and a better understanding of the level of ecosystem health in the basin is helpful for policymakers in developing measures for ecological restoration and eco-space management. In this study, we proposed an improved VORS model containing ecosystem vigor, organization, resilience and services to assess the ecosystem health in the Wuliangsu Lake Basin in 2000, 2011, and 2022. To improve spatial precision, the study area was partitioned into grids measuring 2 km × 2 km each. The results indicated that: (1) the ecosystem health showed a slight increase trend, which indicated by the increase level grids reached 94 % in 2000–2022 change. (2) The reduction in vegetation cover decreased the ecosystem vigor, which was indicated by the weak level increased from 22 % in 2000 to 73 % in 2022. Ecosystem structure becomes more complex enhanced the ecosystem organization, the value increased from 0.3 to 0.6 in 2000 to 0.6–0.8 in 2022. Ecosystem resilience slightly declined in 2011 and 2022, ecosystem services were relatively stable but were dominated by weak levels. (3) The majority land use type in the study area is farmland, and the ecosystem vigor decreases as the area of grassland decreases to farmland in these 22 years. The ecosystem’s structure was enhanced and the land use structure was progressively brought into balance. Changes in all four parameters of ecosystem health assessment are related to land use, and changes in ecosystem health are likewise related to land use changes. The improved VORS model constructed for the Wuliangsu Lake Basin objectively reflect the basin’s state of ecosystem health, and provide a reference for ecological restoration and basin regional planning.

Exploring the landscape of system dynamics archetypes: A systematic review

AbstractSystem dynamic archetypes reflect common problematic behaviours that occur in the real world. The high level of abstraction embodied in these archetypes is both promising and daunting. Identifying relevant archetypes for real‐world problems is not a straightforward task. In this work, we aim to provide a comprehensive background for understanding current knowledge on the application of system archetypes. The methodology is based on a systematic literature review. Most of the works contribute with prescriptive models that address problems in specific areas. These models generally tackle complex problems where social, environmental and political issues converge. The unit of analysis encompasses geographical units, organizations and artefacts. The steps followed in the development are described with different level of detail. Stakeholders' participation is typically integrated into the development process. Some reported problems are that archetypes could induce a hypothesis that does not include all relevant factors; some participants may have difficulty in understanding the models; confidence in results, difficulty of having data to configure all the variables; and the cost burden for small enterprises.

Where should we go - Estimating travel times for modelling accessibility to 24-hour emergency departments in Canada

Estimating travel time to 24-hour emergency services is an important component to modelling accessibility of health services, particularly for rural areas. However, methods used to estimate travel time vary significantly, are not representative of the residential population, and are not openly validated. This makes the assessment of travel-based accessibility metrics between studies incomparable. To address this issue and develop a standardized measurement of emergency service access, this study utilized small geographic units (Dissemination Areas – DA) and geographical boundaries representative of municipal equivalents (Census Subdivision – CSD). Estimated travel times between the centroid of an inhabited DA to each 24-hr emergency department was computed with population-weighted travel times generated for each CSD. This dataset provides a nationally consistent measurement of proximity to emergency services accounting for travel pathing and population distribution. This methodology can be extended to generate estimated shortest travel routes for other healthcare resources or develop actual travel routes based on individuals’ experiences with the healthcare system.

GLocal: A global development dataset of subnational administrative areas

The purpose of the GLocal dataset is to enable research in international development that requires both global scope and local precision. Leveraging modern geospatial analysis tools, we process a diverse array of sources to provide researchers with a growing set of economic, demographic, ecological and socio-political variables for geographic units relevant to public policy. We provide separate data files for different levels of administrative and periodic aggregation, along with ad-hoc files with more detailed information on specific topics. In this data descriptor paper, we discuss both our data processing methodologies and validation pipelines, and provide a short case study to illustrate the research potential of the dataset. We also introduce a simple web app, glocal.streamlit.app, which offers a user-friendly interface for exploring and visualizing the dataset. Given the growing number of public and granular sources of relevance for international development research, we hope to continue adding features and expand the GLocal dataset in the future.