Changes In Geographic Patterns Research Articles

Development and Assessment of a Social Media-Based Construct of Firearm Ownership: Computational Derivation and Benchmark Comparison.

Gun violence research is characterized by a dearth of data available for measuring key constructs. Social media data may offer a potential opportunity to significantly reduce that gap, but developing methods for deriving firearms-related constructs from social media data and understanding the measurement properties of such constructs are critical precursors to their broader use. This study aimed to develop a machine learning model of individual-level firearm ownership from social media data and assess the criterion validity of a state-level construct of ownership. We used survey responses to questions on firearm ownership linked with Twitter data to construct different machine learning models of firearm ownership. We externally validated these models using a set of firearm-related tweets hand-curated from the Twitter Streaming application programming interface and created state-level ownership estimates using a sample of users collected from the Twitter Decahose application programming interface. We assessed the criterion validity of state-level estimates by comparing their geographic variance to benchmark measures from the RAND State-Level Firearm Ownership Database. We found that the logistic regression classifier for gun ownership performs the best with an accuracy of 0.7 and an F1-score of 0.69. We also found a strong positive correlation between Twitter-based estimates of gun ownership and benchmark ownership estimates. For states meeting a threshold requirement of a minimum of 100 labeled Twitter users, the Pearson and Spearman correlation coefficients are 0.63 (P<.001) and 0.64 (P<.001), respectively. Our success in developing a machine learning model of firearm ownership at the individual level with limited training data as well as a state-level construct that achieves a high level of criterion validity underscores the potential of social media data for advancing gun violence research. The ownership construct is an important precursor for understanding the representativeness of and variability in outcomes that have been the focus of social media analyses in gun violence research to date, such as attitudes, opinions, policy stances, sentiments, and perspectives on gun violence and gun policy. The high criterion validity we achieved for state-level gun ownership suggests that social media data may be a useful complement to traditional sources of information on gun ownership such as survey and administrative data, especially for identifying early signals of changes in geographic patterns of gun ownership, given the immediacy of the availability of social media data, their continuous generation, and their responsiveness. These results also lend support to the possibility that other computationally derived, social media-based constructs may be derivable, which could lend additional insight into firearm behaviors that are currently not well understood. More work is needed to develop other firearms-related constructs and to assess their measurement properties.

Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates

BackgroundIt is well known that the burden caused by cancer can vary geographically, which may relate to differences in health, economics or lifestyle. However, to date, there was no comprehensive picture of how the cancer burden, measured by cancer incidence and survival, varied by small geographical area across Australia.MethodsThe Atlas consists of 2148 Statistical Areas level 2 across Australia defined by the Australian Statistical Geography Standard which provide the best compromise between small population and small area. Cancer burden was estimated for males, females, and persons separately, with 50 unique sex-specific (males, females, all persons) cancer types analysed. Incidence and relative survival were modelled with Bayesian spatial models using the Leroux prior which was carefully selected to provide adequate spatial smoothing while reflecting genuine geographic variation. Markov Chain Monte Carlo estimation was used because it facilitates quantifying the uncertainty of the posterior estimates numerically and visually.ResultsThe results of the statistical model and visualisation development were published through the release of the Australian Cancer Atlas (https://atlas.cancer.org.au) in September, 2018. The Australian Cancer Atlas provides the first freely available, digital, interactive picture of cancer incidence and survival at the small geographical level across Australia with a focus on incorporating uncertainty, while also providing the tools necessary for accurate estimation and appropriate interpretation and decision making.ConclusionsThe success of the Atlas will be measured by how widely it is used by key stakeholders to guide research and inform decision making. It is hoped that the Atlas and the methodology behind it motivates new research opportunities that lead to improvements in our understanding of the geographical patterns of cancer burden, possible causes or risk factors, and the reasons for differences in variation between cancer types, both within Australia and globally. Future versions of the Atlas are planned to include new data sources to include indicators such as cancer screening and treatment, and extensions to the statistical methods to incorporate changes in geographical patterns over time.

The role of humidity in determining scenarios of perceived temperature extremes in Europe

An increase of the 2 m temperature over Europe is expected within the current century. In order to consider health impacts, it is important to evaluate the combined effect of temperature and humidity on the human body. To achieve this, projections of a basic index—the humidex—representative of the perceived temperature, under different scenarios and periods, have been investigated. The simultaneous occurrence of observed extreme temperature events and perceived extreme temperature events is seldom found within the present climate, reinforcing the importance of investigating the combination of the two fields. A set of 10 km resolution regional climate simulations, provided within the EURO-CORDEX multi-model effort, demonstrates an ability in representing moderate to extreme events of perceived temperature over the present climate, and to be useful as a tool for quantifying future changes in geographical patterns of exposed areas over Europe. Following the RCP8.5 emission scenario, an expansion of the area subject to dangerous conditions is suggested from the middle of the current century, reaching 60 °N. The most significant increase of perceived extreme temperature conditions is found comparing the 2066–2095 projections to the 1976–2005 period; bearing in mind that changes in relative humidity may either amplify or offset the health effects of temperature, a less pronounced projected reduction of relative humidity in the north-eastern part of Europe, associated with extreme humidex events, makes northern Europe the most prone region to an increase of moderate to extreme values of perceived temperature. This is in agreement with a pronounced projected specific humidity increase.

Modelling wind resources in climate change scenarios in complex terrains

Wind farms have suffered a spectacular growth in Europe in the last few decades. However, future changes in climate could affect the availability of the wind resource in certain regions. This work assesses the capabilities of a Regional Climate Model (WRF) applied at medium-to-high resolution (10 km, 33 vertical layers) over a particularly complex and vulnerable terrain (North Eastern Iberian Peninsula) to derive mean wind speed and direction for 1981–2000. The model is able to reproduce geographical wind patterns, although it generally overemphasises surface wind intensity when compared to individual observations. Projections of mean wind speed changes for 2031–2050 suggest a decrease in surface wind intensity. Energy density estimations at 60 m agl (typical hub-height) show that the implications of the wind speed weakening could affect the preferential areas for the wind farm locations defined at present. Reductions up to 20% of wind energy density are found already in mid-21st century. Therefore, it is advisable to derive dynamic methodologies to assess the preferential areas for wind farm locations, not only considering past-time wind climatologies, but also considering future climate changes in geographical patterns. Regional Climate Models applied at medium-to-high resolution can be useful for this purpose.

European Integration and Knowledge Flows across European Regions

Cappelli R. and Montobbio F. European integration and knowledge flows across European regions, Regional Studies. Using data on inventor citations and inventor collaborations, changes in geographical patterns of knowledge flows between European regions during the period 1981–2000 are analysed. It is shown that inventor collaborations become less geographically localized, while inventor citations become more localized. The European integration process has a significant effect on reducing barriers to knowledge flows between new and old European Union members. For inventor citations, this effect relates only to the European Union enlargement of 1995 and is confined to knowledge flows from Austria, Finland and Sweden to old European Union members.

Precipitation and winter temperature predict long-term range-scale abundance changes in Western North American birds.

Predicting biodiversity responses to climate change remains a difficult challenge, especially in climatically complex regions where precipitation is a limiting factor. Though statistical climatic envelope models are frequently used to project future scenarios for species distributions under climate change, these models are rarely tested using empirical data. We used long-term data on bird distributions and abundance covering five states in the western US and in the Canadian province of British Columbia to test the capacity of statistical models to predict temporal changes in bird populations over a 32-year period. Using boosted regression trees, we built presence-absence and abundance models that related the presence and abundance of 132 bird species to spatial variation in climatic conditions. Presence/absence models built using 1970-1974 data forecast the distributions of the majority of species in the later time period, 1998-2002 (mean AUC=0.79±0.01). Hindcast models performed equivalently (mean AUC=0.82±0.01). Correlations between observed and predicted abundances were also statistically significant for most species (forecast mean Spearman's ρ=0.34±0.02, hindcast=0.39±0.02). The most stringent test is to test predicted changes in geographic patterns through time. Observed changes in abundance patterns were significantly positively correlated with those predicted for 59% of species (mean Spearman's ρ=0.28±0.02, across all species). Three precipitation variables (for the wettest month, breeding season, and driest month) and minimum temperature of the coldest month were the most important predictors of bird distributions and abundances in this region, and hence of abundance changes through time. Our results suggest that models describing associations between climatic variables and abundance patterns can predict changes through time for some species, and that changes in precipitation and winter temperature appear to have already driven shifts in the geographic patterns of abundance of bird populations in western North America.

The predictability of high-risk zones for heat-related mortality within seven U.S. cities

Background: Within major metropolitan areas there is spatial variability in heat-related mortality, but it is not known whether the places associated with high mortality are consistent across time. Temporal changes in geographic patterns of heat risk would have important implications for intervention strategies and projections of health outcomes in future climates. Aims: We sought to quantify the predictability and temporal variability of high-risk zones for heat-related mortality in seven climatically diverse cities in the United States. Our hypothesis is that high-risk zones can be reliably predicted from historical data. Methods: 25 years of daily postal code-level counts of mortality data were split into training and testing data sets. A two-stage modelling approach was used to identify zones within each city with significantly high mortality during the training period. These zones were labelled as hypothetical “targets” for intervention activities. We then evaluated model skill in predicting high-risk zones in advance. We also used cross-validation to quantify the overall temporal variability irrespective of chronological sequence. Confounding variables including seasonality and long-term trends were accounted for with a generalized additive model. The study was limited to days that were associated with both high temperatures and high mortality rates at the city-wide scale. Results: Heat-related mortality in the testing period was found to be significantly higher in target zones compared to non-target zones. Target zones were also more likely to be high mortality zones in the testing period than non-target zones. In most cities the number of zones of high heat-related mortality declined from the training to the testing period. Conclusions: Geographically-targeted long-term intervention measures aimed at reducing the public health burden associated with heat stress may be effective because the location of high-risk zones is relatively consistent over time.

Spatial patterns of malaria reported deaths in Yunnan Province, China.

Malaria has been a heavy social and health burden in the remote and poor areas in southern China. Analyses of malaria epidemic patterns can uncover important features of malaria transmission. This study identified spatial clusters, seasonal patterns, and geographic variations of malaria deaths at a county level in Yunnan, China, during 1991-2010. A discrete Poisson model was used to identify purely spatial clusters of malaria deaths. Logistic regression analysis was performed to detect changes in geographic patterns. The results show that malaria mortality had declined in Yunnan over the study period and the most likely spatial clusters (relative risk [RR] = 23.03-32.06, P < 0.001) of malaria deaths were identified in western Yunnan along the China-Myanmar border. The highest risk of malaria deaths occurred in autumn (RR = 58.91, P < 0.001) and summer (RR = 31.91, P < 0.001). The results suggested that the geographic distribution of malaria deaths was significantly changed with longitude, which indicated there was decreased mortality of malaria in eastern areas over the last two decades, although there was no significant change in latitude during the same period. Public health interventions should target populations in western Yunnan along border areas, especially focusing on floating populations crossing international borders.

Individual-level space-time analyses of emergency department data using generalized additive modeling.

BackgroundAlthough daily emergency department (ED) data is a source of information that often includes residence, its potential for space-time analyses at the individual level has not been fully explored. We propose that ED data collected for surveillance purposes can also be used to inform spatial and temporal patterns of disease using generalized additive models (GAMs). This paper describes the methods for adapting GAMs so they can be applied to ED data.MethodsGAMs are an effective approach for modeling spatial and temporal distributions of point-wise data, producing smoothed surfaces of continuous risk while adjusting for confounders. In addition to disease mapping, the method allows for global and pointwise hypothesis testing and selection of statistically optimum degree of smoothing using standard statistical software. We applied a two-dimensional GAM for location to ED data of overlapping calendar time using a locally-weighted regression smoother. To illustrate our methods, we investigated the association between participants’ address and the risk of gastrointestinal illness in Cape Cod, Massachusetts over time.ResultsThe GAM space-time analyses simultaneously smooth in units of distance and time by using the optimum degree of smoothing to create data frames of overlapping time periods and then spatially analyzing each data frame. When resulting maps are viewed in series, each data frame contributes a movie frame, allowing us to visualize changes in magnitude, geographic size, and location of elevated risk smoothed over space and time. In our example data, we observed an underlying geographic pattern of gastrointestinal illness with risks consistently higher in the eastern part of our study area over time and intermittent variations of increased risk during brief periods.ConclusionsSpatial-temporal analysis of emergency department data with GAMs can be used to map underlying disease risk at the individual-level and view changes in geographic patterns of disease over time while accounting for multiple confounders. Despite the advantages of GAMs, analyses should be considered exploratory in nature. It is possible that even with a conservative cutoff for statistical significance, results of hypothesis testing may be due to chance. This paper illustrates that GAMs can be adapted to measure geographic trends in public health over time using ED data.

The Change in European Foreign Trade from 1960 to 2010: A Geography of Leading Merchandise Exporting Partners

An American geographer whose research focuses on Europe reports on changes in geographical patterns and trends in merchandise export trade after the dismantling of political- economic divisions prevailing during the Cold War and their replacement by European political and economic integration as well as globalization. Analysis and comparison of international trade data for the years 1960 and 2010 reveal a dramatic expansion in the role of Germany and China in European merchandise trade, accompanied by a decline in the importance of the United States and United Kingdom. The author also demonstrates how some countries such as the Netherlands have expanded their exports to a broader range of European countries, whereas others appear to be consolidating their roles as leaders of regional trade blocs (e.g., Italy in Southeastern Europe).

EFFECTS OF CLIMATE CHANGE ON CARBON STORAGE IN BOREAL FORESTS OF CHINA: A LOCAL PERSPECTIVE

The BIOME3 model was used to simulate the distribution patterns and carbon storage of the horizontal, zonal boreal forests in northeast and northwest China using a mapping system for vegetation patterns combined with carbon density estimates from vegetation and soils. The BIOME3 prediction is in reasonable good agreement with the potential distribution of Chinese boreal forests. The effects of changing atmospheric CO2 concentration had a nonlinear effect on boreal forest distribution, with 3.5–10.8% reduced areas for both increasing and decreasing CO2. In contrast, the increased climate together with and without changing CO2 concentration showed dramatic changes in geographic patterns, with 70% reduction in area and disappearance of almost boreal forests in northeast China. The baseline carbon storage in boreal forests of China is 4.60 PgC (median estimate) based on the vegetation area of actual boreal forest distribution. If taking the large area of agricultural crops into account, the median value of potential carbon storage is 6.92 PgC. The increasing (340–500 ppmv) and decreasing CO2 concentration (340–200 ppmv) led to decrease of carbon storage, 0.33 PgC and 1.01 PgC respectively compared to BIOME3 potential prediction under present climate and CO2 conditions. Both climate change alone and climate change with CO2 enrichment (340–500 ppmv) reduced largely the carbon stored in vegetation and soils by ca. 6.5 PgC. The effect of climate change is more significant than the direct physiological effect of CO2 concentration on the boreal forests of China, showing a large reduction in both distribution area and carbon storage.

Alternate ranging methods for cancer mortality maps.

Mapping techniques can highlight the spatial or temporal variations in rates of cancer mortality. In mapping geographic patterns of cancer mortality, spatial units are grouped into categories defined by specified rate ranges, and then the units in each category are assigned a particular color in the map. We examined the consequences of using different ranging methods when comparing maps over several time intervals. Maps of mortality rates for cancers of the breast, lung (including the lung, trachea, bronchus, and pleura), and cervix uteri in the United States by county or state economic area are created for different time intervals between 1950 and 1994. Two ranging methods are employed: 1) Ranges are defined for individual time interval by the deciles of rates in that interval (ranging within intervals), and 2) constant ranges for all time intervals are defined by the deciles of rates for the entire 45-year period from 1950 through 1994 (ranging across intervals). The time intervals from 1950 through 1969 and from 1970 through 1994 were chosen to accommodate the availability of detailed county-level population estimates specifically for blacks starting in 1970. The ranging method has little impact on maps for breast cancer mortality, which changed little over time. For lung cancer, which increased over time, and cervix uteri cancer, which decreased over time, ranging within time intervals shows the geographic variability but does not convey the temporal trends. Trends are evident when ranging across time intervals is employed; however, geographic variability is partially obscured by the predominance of spatial units in the highest rate categories in the recent time intervals for lung cancer and in the early time intervals for cervix uteri cancer. Ranging within time intervals displays geographic patterns and changes in geographic patterns, regardless of time trends in rates. Ranging across time intervals shows temporal changes in rates but with some loss of information about geographic variability.