Applied Geography (Sevenoaks, England)[JOURNAL]
Sun
108 papers
Modeling Accessibility of Screening and Treatment Facilities for Older Adults using Transportation Networks.
Zhang Q, Northridge ME, Jin Z … +1 more , Metcalf SS
Appl Geogr · 2018 Apr · PMID 29556112 · Full text
Increased lifespans and population growth have resulted in an older U.S. society that must reckon with the complex oral health needs that arise as adults age. Understanding accessibility to screening and treatment facili...
Increased lifespans and population growth have resulted in an older U.S. society that must reckon with the complex oral health needs that arise as adults age. Understanding accessibility to screening and treatment facilities for older adults is necessary in order to provide them with preventive and restorative services. This study uses an agent-based model to examine the accessibility of screening and treatment facilities via transportation networks for older adults living in the neighborhoods of northern Manhattan, New York City. Older adults are simulated as socioeconomically distinct agents who move along a GIS-based transportation network using transportation modes that mediate their access to screening and treatment facilities. This simulation model includes four types of mobile agents as a simplifying assumption: walk, by car, by bus, or by van (i.e., a form of transportation assistance for older adults). These mobile agents follow particular routes: older adults who travel by car, bus, and van follow street roads, whereas pedestrians follow walkways. The model enables the user to focus on one neighborhood at a time for analysis. The spatial dimension of an older adult's accessibility to screening and treatment facilities is simulated through the travel costs (indicated by travel time or distance) incurred in the GIS-based model environment, where lower travel costs to screening and treatment facilities imply better access. This model provides a framework for representing health-seeking behavior that is contextualized by a transportation network in a GIS environment.
Looking through a different lens: Examining the inequality-mortality association in U.S. counties using spatial panel models.
Yang TC, Matthews SA, Park K
Appl Geogr · 2017 Sep · PMID 28936015 · Full text
Two areas still need further examination in the ecological study of inequality and mortality. First, the evidence for the relationship between income inequality and mortality remains inconclusive, particularly when the a...
Two areas still need further examination in the ecological study of inequality and mortality. First, the evidence for the relationship between income inequality and mortality remains inconclusive, particularly when the analytic unit is small (e.g., county in the U.S.). Second, most previous studies are cross-sectional and are unable to address the recent diverging patterns whereby mortality has decreased and income inequality increased. This study aims to contribute to both topic areas by studying the relationship between inequality and mortality via a spatiotemporal approach that simultaneously considers the spatial structure and the temporal trends of inequality and mortality using county panel data between 1990 and 2010 for the conterminous U.S. Using both spatial panel random effect model and spatial panel fixed effect models, we found that (a) income inequality was not a significant factor for mortality after taking into account the spatiotemporal structure and the most salient factors for mortality (e.g., socioeconomic status); (b) the spatial panel fixed effect model indicated that income inequality was negatively associated with mortality over the time, a relationship mirroring the diverging patterns; and (c) the significant spatial and temporal fixed effects suggested that both dimensions are critical factors in understanding the inequality-mortality relationship in the U.S. Our findings extend support to the argument that income inequality does not affect mortality and suggest that the cross-sectional findings may be a consequence of ignoring the temporal trends.
Spatial spillover and the socio-ecological determinants of diabetes-related mortality across US counties.
Turi KN, Grigsby-Toussaint DS
Appl Geogr · 2017 Aug · PMID 36238660 · Full text
The spatial structure of diabetes-related mortality in US counties is evident from previous studies. However, it is not clear if spatial variation in diabetes-related mortality is associated with spatial variation in soc...
The spatial structure of diabetes-related mortality in US counties is evident from previous studies. However, it is not clear if spatial variation in diabetes-related mortality is associated with spatial variation in socioecological factors. We analyze the spatial spillover effect of changes in socioeconomic gradients (education, employment, household income), retail food environments, and access to health care, on diabetes-related mortality rates across the United States. Seven-year aggregates of multiple cause mortality data from the CDC WONDER compressed mortality database were merged with several sources of county-level data to examine mortality clusters, factors associated with the clusters, and spatial spillover effects in 3109 continuous US counties. The results suggest that high diabetes-related mortality cluster counties are located throughout the Southern Plains, Southeastern, and Appalachian regions. Lower socioeconomic status, a high density of fast food restaurants, a lack of access to grocery stores, a high proportion of Blacks, and low physical activity characterize high diabetes-related mortality rates clusters. The impacts from improvements in socioeconomic gradients and the retail food environment in neighboring counties spill over, and reduce the diabetes-related mortality rate in a particular county. This result implies that improvements in socioeconomic status and access to healthy food would significantly reduce diabetes-related mortality rates in contiguous US counties.
How to allocate limited healthcare resources: Lessons from the introduction of antiretroviral therapy in rural Mozambique.
Dodson ZM, Agadjanian V, Driessen J
Appl Geogr · 2017 Jan · PMID 28596630 · Full text
Proper allocation of limited healthcare resources is a challenging task for policymakers in developing countries. Allocation of and access to these resources typically varies based on how need is defined, thus determinin...
Proper allocation of limited healthcare resources is a challenging task for policymakers in developing countries. Allocation of and access to these resources typically varies based on how need is defined, thus determining how individuals access and acquire healthcare. Using the introduction of antiretroviral therapy in southern Mozambique as an example, we examine alternative definitions of need for rural populations and how they might impact the allocation of this vital health service. Our results show that how need is defined matters when allocating limited healthcare resources and the use of need-based metrics can help ensure more optimal distribution of services.
Leveraging geotagged Twitter data to examine neighborhood happiness, diet, and physical activity.
Nguyen QC, Kath S, Meng HW … +5 more , Li D, Smith KR, VanDerslice JA, Wen M, Li F
Appl Geogr · 2016 Aug · PMID 28533568 · Full text
OBJECTIVES: Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York. METHODS: We util...
OBJECTIVES: Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York. METHODS: We utilize 2.8 million tweets collected between February-August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics. RESULTS: For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths. CONCLUSIONS: Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities-information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.
Environmental Inequality and Pollution Advantage among Immigrants in the United States.
Bakhtsiyarava M, Nawrotzki RJ
Appl Geogr · 2017 Apr · PMID 28484286 · Full text
Environmental inequality scholarship has paid little attention to the disproportional exposure of immigrants in the United States (U.S.) to unfavorable environmental conditions. This study investigates whether new intern...
Environmental inequality scholarship has paid little attention to the disproportional exposure of immigrants in the United States (U.S.) to unfavorable environmental conditions. This study investigates whether new international migrants in the U.S. are exposed to environmental hazards and how this pattern varies among immigrant subpopulations (e.g., Hispanics, Asian, European). We combine sociodemographic information from the American Community Survey with toxicity-weighted chemical concentrations (Toxics Release Inventory) to model the relationship between toxin exposure and the relative population of recent immigrants across Public Use Microdata Areas (PUMAs, n=2,054) during 2005-2011. Results from spatial panel models show that immigrants tend to be less exposed to toxins, suggesting resilience instead of vulnerability. This pattern was pronounced among immigrants from Europe and Latin America (excluding Mexico). However, our results revealed that Mexican immigrants are disproportionately exposed to environmental hazards in wealthy regions.
Mapping Resource Selection Functions in Wildlife Studies: Concerns and Recommendations.
Morris LR, Proffitt KM, Blackburn JK
Appl Geogr · 2016 Nov · PMID 29887652 · Full text
Predicting the spatial distribution of animals is an important and widely used tool with applications in wildlife management, conservation, and population health. Wildlife telemetry technology coupled with the availabili...
Predicting the spatial distribution of animals is an important and widely used tool with applications in wildlife management, conservation, and population health. Wildlife telemetry technology coupled with the availability of spatial data and GIS software have facilitated advancements in species distribution modeling. There are also challenges related to these advancements including the accurate and appropriate implementation of species distribution modeling methodology. Resource Selection Function (RSF) modeling is a commonly used approach for understanding species distributions and habitat usage, and mapping the RSF results can enhance study findings and make them more accessible to researchers and wildlife managers. Currently, there is no consensus in the literature on the most appropriate method for mapping RSF results, methods are frequently not described, and mapping approaches are not always related to accuracy metrics. We conducted a systematic review of the RSF literature to summarize the methods used to map RSF outputs, discuss the relationship between mapping approaches and accuracy metrics, performed a case study on the implications of employing different mapping methods, and provide recommendations as to appropriate mapping techniques for RSF studies. We found extensive variability in methodology for mapping RSF results. Our case study revealed that the most commonly used approaches for mapping RSF results led to notable differences in the visual interpretation of RSF results, and there is a concerning disconnect between accuracy metrics and mapping methods. We make 5 recommendations for researchers mapping the results of RSF studies, which are focused on carefully selecting and describing the method used to map RSF studies, and relating mapping approaches to accuracy metrics.
An approach for estimating vaccination coverage for communities using school-level data and population mobility information.
Delamater PL, Leslie TF, Yang YT … +1 more , Jacobsen KH
Appl Geogr · 2016 Jun · PMID 31327881 · Full text
Childhood vaccination data are made available at a school level in some U.S. states. These data can be geocoded and may be considered as having a high spatial resolution. However, a school only represents the destination...
Childhood vaccination data are made available at a school level in some U.S. states. These data can be geocoded and may be considered as having a high spatial resolution. However, a school only represents the destination location for the set of students that actually reside and interact within some larger areal region, creating a spatial mismatch. Public school districts are often used to represent these regions, but fail to account for private schools and school of choice programs. We offer a new approach to estimate childhood vaccination coverage rates at a community level by integrating school level data with population commuting information. The resulting mobility-adjusted vaccine coverage estimates resolve the spatial mismatch problem and are more aligned with the geographic scale at which public health policies are implemented. We illustrate the utility of our approach using a case study on diphtheria, tetanus, and pertussis (DTP) vaccination coverage for kindergarten students in California. The modeled community-level DTP coverage estimates yield a statewide coverage of 92.37%, which is highly similar to the 92.44% coverage rate calculated from the original school-level data.
Scale effects in food environment research: Implications from assessing socioeconomic dimensions of supermarket accessibility in an eight-county region of South Carolina.
Barnes TL, Colabianchi N, Hibbert JD … +3 more , Porter DE, Lawson AB, Liese AD
Appl Geogr · 2016 Mar · PMID 27022204 · Full text
Choice of neighborhood scale affects associations between environmental attributes and health-related outcomes. This phenomenon, a part of the modifiable areal unit problem, has been described fully in geography but not...
Choice of neighborhood scale affects associations between environmental attributes and health-related outcomes. This phenomenon, a part of the modifiable areal unit problem, has been described fully in geography but not as it relates to food environment research. Using two administrative-based geographic boundaries (census tracts and block groups), supermarket geographic measures (density, cumulative opportunity and distance to nearest) were created to examine differences by scale and associations between three common U.S. Census-based socioeconomic status (SES) characteristics (median household income, percentage of population living below poverty and percentage of population with at least a high school education) and a summary neighborhood SES z-score in an eight-county region of South Carolina. General linear mixed-models were used. Overall, both supermarket density and cumulative opportunity were higher when using census tract boundaries compared to block groups. In analytic models, higher median household income was significantly associated with lower neighborhood supermarket density and lower cumulative opportunity using either the census tract or block group boundaries, and neighborhood poverty was positively associated with supermarket density and cumulative opportunity. Both median household income and percent high school education were positively associated with distance to nearest supermarket using either boundary definition, whereas neighborhood poverty had an inverse association. Findings from this study support the premise that supermarket measures can differ by choice of geographic scale and can influence associations between measures. Researchers should consider the most appropriate geographic scale carefully when conducting food environment studies.
Mapping eastern equine encephalitis virus risk for white-tailed deer in Michigan.
Downs JA, Hyzer G, Marion E … +3 more , Smith ZJ, Kelen PV, Unnasch TR
Appl Geogr · 2015 Oct · PMID 26494931 · Full text
Eastern equine encephalitis (EEE) is a mosquito-borne viral disease that is often fatal to humans and horses. Some species including white-tailed deer and passerine birds can survive infection with the EEE virus (EEEV) a...
Eastern equine encephalitis (EEE) is a mosquito-borne viral disease that is often fatal to humans and horses. Some species including white-tailed deer and passerine birds can survive infection with the EEE virus (EEEV) and develop antibodies that can be detected using laboratory techniques. In this way, collected serum samples from free ranging white-tailed deer can be used to monitor the presence of the virus in ecosystems. This study developed and tested a risk index model designed to predict EEEV activity in white-tailed deer in a three-county area of Michigan. The model evaluates EEEV risk on a continuous scale from 0.0 (no measurable risk) to 1.0 (highest possible risk). High risk habitats are identified as those preferred by white-tailed deer that are also located in close proximity to an abundance of wetlands and lowland forests, which support disease vectors and hosts. The model was developed based on relevant literature and was tested with known locations of infected deer that showed neurological symptoms. The risk index model accurately predicted the known locations, with the mean value for those sites equal to the 94 percentile of values in the study area. The risk map produced by the model could be used refine future EEEV monitoring efforts that use serum samples from free-ranging white-tailed deer to monitor viral activity. Alternatively, it could be used focus educational efforts targeted toward deer hunters that may have elevated risks of infection.
GEOGRAPHICALLY-WEIGHTED REGRESSION ANALYSIS OF PERCENTAGE OF LATE-STAGE PROSTATE CANCER DIAGNOSIS IN FLORIDA.
Goovaerts P, Xiao H, Adunlin G … +4 more , Ali A, Tan F, Gwede CK, Huang Y
Appl Geogr · 2015 Aug · PMID 26257450 · Full text
This study assessed spatial context and the local impacts of putative factors on the proportion of prostate cancer diagnosed at late-stages in Florida during the period 2001-2007. A logistic regression was performed aspa...
This study assessed spatial context and the local impacts of putative factors on the proportion of prostate cancer diagnosed at late-stages in Florida during the period 2001-2007. A logistic regression was performed aspatially and by geographically-weighted regression (GWR) at the nodes of a 5 km spacing grid overlaid over Florida and using all the cancer cases within a radius of 125 km of each node. Variables associated significantly with high percentages of late-stage prostate cancer included having comorbidities, smoking, being Black and living in census tracts with farmhouses. Having private or public insurance, being married or diagnosed in a for-profit facility, as well as living in census tracts with high household income reduced significantly this likelihood. Geographically-weighted regression allowed the identification of areas where the local odds ratio is significantly different from the ratio estimated using aspatial regression (State-level). For example, the local odds ratios for the comorbidity covariates were significantly smaller than the State-level odds ratio in Tallahassee and Pensacola, while they were significantly larger in Palm Beach. This emphasizes the need for local strategies and cancer control interventions to reduce the percentage of prostate cancer diagnosed at late-stages and ultimately eliminate health disparities.
Multilevel built environment features and individual odds of overweight and obesity in Utah.
Xu Y, Wen M, Wang F
Appl Geogr · 2015 Jun · PMID 26251559 · Full text
Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007, 2009 and 2011 in Utah, this research uses multilevel modeling (MLM) to examine the associations between neighborhood built environmen...
Based on the data from the Behavioral Risk Factor Surveillance System (BRFSS) in 2007, 2009 and 2011 in Utah, this research uses multilevel modeling (MLM) to examine the associations between neighborhood built environments and individual odds of overweight and obesity after controlling for individual risk factors. The BRFSS data include information on 21,961 individuals geocoded to zip code areas. Individual variables include BMI (body mass index) and socio-demographic attributes such as age, gender, race, marital status, education attainment, employment status, and whether an individual smokes. Neighborhood built environment factors measured at both zip code and county levels include street connectivity, walk score, distance to parks, and food environment. Two additional neighborhood variables, namely the poverty rate and urbanicity, are also included as control variables. MLM results show that at the zip code level, poverty rate and distance to parks are significant and negative covariates of the odds of overweight and obesity; and at the county level, food environment is the sole significant factor with stronger fast food presence linked to higher odds of overweight and obesity. These findings suggest that obesity risk factors lie in multiple neighborhood levels and built environment features need to be defined at a neighborhood size relevant to residents' activity space.
Linking pesticides and human health: a geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure.
VoPham T, Wilson JP, Ruddell D … +6 more , Rashed T, Brooks MM, Yuan JM, Talbott EO, Chang CH, Weissfeld JL
Appl Geogr · 2015 Aug · PMID 28867851 · Full text
Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide appl...
Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.
Sensing the public's reaction to crime news using the 'Links Correspondence Method'.
Lampoltshammer TJ, Kounadi O, Sitko I … +1 more , Hawelka B
Appl Geogr · 2014 Aug · PMID 25843991 · Full text
Public media such as TV or newspapers, paired with crime statistics from the authority, raise awareness of crimes within society. However, in today's digital society, other sources rapidly gain importance as well. The In...
Public media such as TV or newspapers, paired with crime statistics from the authority, raise awareness of crimes within society. However, in today's digital society, other sources rapidly gain importance as well. The Internet and social networks act heavily as information distribution platforms. Therefore, this paper aims at exploring the influence of the social Web service Twitter as an information distribution platform for crime news. In order to detect messages with crime-related contents, the Links Correspondence Method (LCM) is introduced, which gathers and investigates Twitter messages related to crime articles via associated Web links. Detected crime tweets are analysed in regard to the distance between the location of an incident and the location of associated tweets, as well as regards demographic aspects of the corresponding crime news. The results show that there exists a spatial dependency regarding the activity space of a user (and the crime-related tweets of this user) and the actual location of the crime incident. Furthermore, the demographic analysis indicates that the type of a crime as well as the gender of the victim has great influence on whether the crime incident is spread via Twitter or not.
Patterns and causes of uncertainty in the American Community Survey.
Spielman SE, Folch D, Nagle N
Appl Geogr · 2014 Jan · PMID 25404783 · Full text
In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margi...
In 2010 the American Community Survey (ACS) replaced the long form of the United States decennial census. The ACS is now the principal source of high-resolution geographic information about the U.S. population. The margins of error on ACS census tract-level data are on average 75 percent larger than those of the corresponding 2000 long-form estimate. The practical implications of this increase is that data are sometimes so imprecise that they are difficult to use. This paper explains why the ACS tract and block group estimates have large margins of error. Statistical concepts are explained in plain English. ACS margins of error are attributed to specific methodological decisions made by the Census Bureau. These decisions are best seen as compromises that attempt to balance financial constraints against concerns about data quality, timeliness, and geographic precision. In addition, demographic and geographic patterns in ACS data quality are identified. These patterns are associated with demographic composition of census tracts. Understanding the fundamental causes of uncertainty in the survey suggests a number of geographic strategies for improving the usability and quality ACS.
A random forest approach for predicting the presence of intermediate host . presence in relation to landscape characteristics in western China.
Marston CG, Danson FM, Armitage RP … +5 more , Giraudoux P, Pleydell DR, Wang Q, Qui J, Craig PS
Appl Geogr · 2014 Dec · PMID 25386042 · Full text
Understanding distribution patterns of hosts implicated in the transmission of zoonotic disease remains a key goal of parasitology. Here, random forests are employed to model spatial patterns of the presence of the plate...
Understanding distribution patterns of hosts implicated in the transmission of zoonotic disease remains a key goal of parasitology. Here, random forests are employed to model spatial patterns of the presence of the plateau pika (.) small mammal intermediate host for the parasitic tapeworm which is responsible for a significant burden of human zoonoses in western China. Landsat ETM+ satellite imagery and digital elevation model data were utilized to generate quantified measures of environmental characteristics across a study area in Sichuan Province, China. Land cover maps were generated identifying the distribution of specific land cover types, with landscape metrics employed to describe the spatial organisation of land cover patches. Random forests were used to model spatial patterns of . presence, enabling the relative importance of the environmental characteristics in relation to . presence to be ranked. An index of habitat aggregation was identified as the most important variable in influencing . presence, with area of degraded grassland the most important land cover class variable. 71% of the variance in . presence was explained, with a 90.98% accuracy rate as determined by 'out-of-bag' error assessment. Identification of the environmental characteristics influencing . presence enables us to better understand distribution patterns of hosts implicated in the transmission of Em. The predictive mapping of this Em host enables the identification of human populations at increased risk of infection, enabling preventative strategies to be adopted.
Livability for all? Conceptual limits and practical implications.
Ruth M, Franklin RS
Appl Geogr · 2014 May · PMID 25339785 · Full text
Livability has risen, alongside sustainability, as a guiding principle for planning and policy. Promoted as the more tangible of the two concepts, livability shapes public perception and infrastructure investments in cit...
Livability has risen, alongside sustainability, as a guiding principle for planning and policy. Promoted as the more tangible of the two concepts, livability shapes public perception and infrastructure investments in cities, as well as competition among cities for the attention of the public, investment communities, and potentially fickle and mobile human capital. This paper takes stock of the current discourse on livability, identifies two central elements that have yet to shape the assessments of livability and policies to promote it, and explores strategies for research and practice to transform the livability concept, and with it the places in which the lives and livelihoods of people unfold.
Spatial conservation planning framework for assessing conservation opportunities in the Atlantic Forest of Brazil.
Giorgi AP, Rovzar C, Davis KS … +6 more , Fuller T, Buermann W, Saatchi S, Smith TB, Silveira LF, Gillespie TW
Appl Geogr · 2014 Sep · PMID 28210009 · Full text
Historic rates of habitat change and growing exploitation of natural resources threaten avian biodiversity in the Brazilian Atlantic Forest, a global biodiversity hotspot. We implemented a twostage framework for conserva...
Historic rates of habitat change and growing exploitation of natural resources threaten avian biodiversity in the Brazilian Atlantic Forest, a global biodiversity hotspot. We implemented a twostage framework for conservation planning in the Atlantic Forest. First, we used ecological niche modeling to predict the distributions of 23 endemic bird species using 19 climatic metrics and 12 spectral and radar remote sensing metrics. Second, we utilized the principle of complementarity to prioritize new sites to augment the Atlantic Forest's existing reserves. The best predictors of bird distributions were precipitation metrics (the seasonality of rainfall) and radar remote sensing metrics (QSCAT). The existing protected areas do not include 10% of the habitat of each of the 23 endemic species. We propose a more economical set of protected areas by reducing the extent to which new sites duplicate the biodiversity content of existing protected areas. There is a high concordance between the proposed conservation areas that we designed using computerized algorithms and Important Bird Areas prioritized by BirdLife International. Insofar as deforestation in the Atlantic Forest is similar to land conversion in other biodiversity hotspots, our methodology is applicable to conservation efforts elsewhere in the world.
Modelling spatial patterns of urban growth in Africa.
Linard C, Tatem AJ, Gilbert M
Appl Geogr · 2013 Oct · PMID 25152552 · Full text
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare...
The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5-10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.
Changing crops in response to climate: virtual Nang Rong, Thailand in an agent based simulation.
Malanson GP, Verdery AM, Walsh SJ … +8 more , Sawangdee Y, Heumann BW, McDaniel PM, Frizzelle BG, Williams NE, Yao X, Entwisle B, Rindfuss RR
Appl Geogr · 2014 Sep · PMID 25061240 · Full text
The effects of extended climatic variability on agricultural land use were explored for the type of system found in villages of northeastern Thailand. An agent based model developed for the Nang Rong district was used to...
The effects of extended climatic variability on agricultural land use were explored for the type of system found in villages of northeastern Thailand. An agent based model developed for the Nang Rong district was used to simulate land allotted to jasmine rice, heavy rice, cassava, and sugar cane. The land use choices in the model depended on likely economic outcomes, but included elements of bounded rationality in dependence on household demography. The socioeconomic dynamics are endogenous in the system, and climate changes were added as exogenous drivers. Villages changed their agricultural effort in many different ways. Most villages reduced the amount of land under cultivation, primarily with reduction in jasmine rice, but others did not. The variation in responses to climate change indicates potential sensitivity to initial conditions and path dependence for this type of system. The differences between our virtual villages and the real villages of the region indicate effects of bounded rationality and limits on model applications.
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