Inverse Distance Research Articles

Source appointment and health risk assessment for atmospheric carbonyls in hot tropical city, Vietnam.

The aim of this study is to assess the spatiotemporal variation, sources, and health impacts of the carbonyl compounds (carbonyls) in Ho Chi Minh City (HCMC), the third-most populous city in Southeast Asia. Sampling was conducted according to the US.EPA Method TO-11A, from 2012 to 2016 in both the dry and the rainy seasons at twelve sites. The result shows that the carbonyl mixing ratios are high when compared to typical cities. Formaldehyde, acetaldehyde, and acetone are the most abundant carbonyls together accounting for 89% of the measured carbonyls. The carbonyl mixing ratio in rainy (46.0 ± 32.2ppb) is about twofold higher than that in dry (23.7 ± 10.3ppb). An inverse distance weighting method was adopted to map the spatial distribution of carbonyls across the city. The result shows that the carbonyl levels tended to be high in the city center. Three carbonyl sources are resolved by the PCA/APCS method: industrial sources and solvent usage (54%), vehicle exhausts (24%), cooking emissions (11%). Both lifetime cancer risk (LCR) and non-cancer hazard index (HIs) were calculated to estimate the health impacts on the community due to inhalation exposure to current carbonyl levels. The LCR values vary from 5.31 × 10-6 to 5.75 × 10-5 for formaldehyde higher than those of 6.61 × 10-7 to 1.36 × 10-5 for acetaldehyde, which mostly exceeded the US.EPA recommendation for five age groups. The hazard quotient values are 12.0 to 68.4 for acrolein, 0.44 to 2.84 for acetaldehyde, 0.55 to 1.85 for formaldehyde, and 0.13 to 0.89 for propionaldehyde.

Assessment of Critical Environmental Risk Zones Around Wetlands in Colombo, Sri Lanka, Using Satellite Remote Sensing and Multi-criteria Decision Tools

Colombo, Sri Lanka’s commercial capital, is recognized as one of the first 18 global sites that were awarded the Ramsar Wetland City accreditation, and is a prime example of the intersection between urbanization and wetland conservation. Thus, this study aims to conduct an environmental risk assessment that can provide insights into the changes in biophysical properties driven by urbanization over the 2000–2020 period. The study used Geographical Information Systems and a remote sensing approach to assess critical environmental risk zones around urban freshwater lakes in the Colombo district of Sri Lanka, including Thalangama, an environmental protection area experiencing sub-urbanization. Satellite data (2000–2020) were employed, and spectral indices, including NDVI, NDBI, NDWI, NDMI, RVI and LST, were calculated. Intersecting areas with multiple risk factors were determined, and distances from these zones to lakes were calculated to generate spatially explicit risk maps using inverse distance weighting. The building density has consistently increased from 2000 to 2020 in all three sites, with ‘very high’ density areas expanding notably. NDVI reveals a gradual increase in ‘very high’ categories until 2015. Plant-water stress analysis shows significant water stress in 2015, and water body areas have decreased after 2010 in all the sites. Multi-criteria assessment identifies critical environmental risk zones around urban lakes, indicating high vulnerability to environmental damage and degradation. Overall, the study emphasizes the importance of leveraging data and risk analysis to inform decision-making processes in environmental management. By doing so, policymakers can enhance the effectiveness of conservation measures and promote the long-term sustainability of natural resources.

Assessment of different interpolation algorithms for daily rainfall spatial distribution in the Var catchment, France

AbstractTo have effective water resource management, the distributed hydrological models are commonly applied for supporting the decision‐making processes. Among different inputs, the spatial distributed rainfall plays significant role in those model simulations. Many interpolation methods have been developed for generating distributed rainfall based on measurement samples. However, depending on the catchment characteristics and data availability, the suitable interpolation algorithm is case‐dependent. This paper presents one operational approach for determining the resonable interpolation algorithm in a complex large catchment (Var catchment, France). Based on the daily rainfall data (2008–2014) collected from 16 stations in the Var catchment, six different interpolation approaches including: inverse distance weight (IDW), spline, kriging with linear and spherical semi‐variogram models and geographically weighted regression considering elevation effects and the combined impacts of elevation and distance to the sea were tested. Integrated the results of statistical and modeling assessments, the 400 m resolution distributed rainfall generated by IDW algorithm shows high preference in generating distributed rainfall in the Var catchment. Moreover, the strategy described in the article also shows promising acceptability for other catchments.

A Hybrid Data Assimilation and Dynamic Mode Decomposition Approach for Xenon Dynamic Prediction of Nuclear Reactor Cores

In this paper, a dynamic prediction scheme that combines the data assimilation method and dynamic mode decomposition (DMD) is brought out for the prediction of the whole-core power distribution under xenon oscillations within the HRP1000 reactor. The DMD is used to predict the power values over the nodes where in-core detectors exist, and predicted power is then extended to the whole core using data assimilation methodologies, e.g. the inverse distance–based data assimilation method. In the data assimilation stage, the selection of the background physical field and the regularization factor under different noise levels is investigated. A series of numerical experiments, based on the HPR1000 proof of feasibility of the coupling scheme, is conducted under low noise levels or low prediction step sizes. Finally, the optimal application conditions and the prediction performance of the coupling scheme in different noise levels are analyzed for practical engineering usage.

Sub-daily precipitation returns levels in ungauged locations: Added value of combining observations with convection permitting simulations

Extreme rainfall events trigger natural hazards, including floods and debris flows, posing serious threats to society and the economy. Accurately quantifying extreme rainfall return levels in ungauged locations is crucial for improving flood protection infrastructure and mitigating water-related risks. This paper quantifies the added value of combining rainfall observations with Convection Permitting Model (CPM) simulations to estimate sub-daily extreme rainfall return levels in ungauged locations. We assess the performance of CPM-informed estimates of extreme return level against a traditional interpolation techniques. We find that kriging methods with external drift outperform inverse distance weighting for both traditional and CPM-informed approaches. We then assess the effectiveness of the two methods under different scenarios of station density. At the highest station density (1/196 km²), traditional interpolation methods outperform the CPM-informed method for durations under 6 h. The performance becomes comparable between 6 and 24 h. For lower station densities (1/400 and 1/800 km²), the CPM-informed method outperforms the traditional method, with average reductions in fractional standard error of 24 %, 13 %, and 8 % for return periods of 2, 10 and 50 years, respectively for a rain gauge density of 1/800 km², and 16 %, 8 %, and 3 % for density of 1/400 km². Information from CPM simulations can thus be useful for estimating sub-daily extreme rainfall events in ungauged sites, particularly in data-scarce areas in which the density of rain gauges is low.

A Localized Evaluation of Surface Water Quality Using GIS-Based Water Quality Index along Satpara Watershed Skardu Baltistan, Pakistan

Surface water quality in Skardu, Gilgit-Baltistan, Pakistan, is of immense importance because of the city’s dependence on these resources for domestic uses, agriculture, and drinking water. The water quality index (WQI) was integrated with the Geographic Information System (GIS) to spatially envision and examine water quality data to facilitate the identification of pollution hotspots, trend analysis, and knowledge-based decision-making for effective water resource management. This study aims to evaluate the physiochemical and bacteriological parameters of the Satpara watershed and to provide the spatial distribution of these parameters. This study endeavors to achieve Sustainable Development Goal 6 (SDG 6) by identifying localities with excellent and unfit water for drinking, sanitation, and hygiene. A total of fifty-one surface water samples were collected from various parts of the Satpara watershed during the fall season of 2023. Well-established laboratory techniques were used to investigate water for parameters like Electrical Conductivity (EC), pH, turbidity, total dissolved solids (TDSs), major cations (K+, Na+, Mg2+, Ca2+), major anions (Cl−, SO42−, NO3−, HCO3−), and bacteriological contaminants (E. coli). Spatial distribution maps of all these parameters were created using the Inverse Distance Weighted (IDW) technique in a GIS environment. A significant variation in the quality of water was observed along the study area. The level of Escherichia coli (E. coli) contamination is above the permissible limit at various locations along the watershed, making water unsafe for direct human consumption in these areas. Some regions showed low TDS values, which could adversely affect human health and agricultural yield. From the WQI valuation, 58.82% of the collected samples were “Poor”, 31.8% were “Very poor” and 9.8% were found to be “Unfit for drinking”. The research findings emphasize the pressing need for consistent monitoring and adoption of water management strategies in Skardu City to warrant sustainable soil and water use. The spatial maps generated for various parameters and the water quality index WQI offer critical insights for targeted intercessions.

Backpropagation-based inference for spatial interpolation to estimate the blastability index in an open pit mine

The blastability index (BI) is a measure that indicates the resistance of rock to fragmentation when blasting. With novel technologies, miners are now able to collect and calculate BI at different depths while drilling. In this research, we propose an approach to estimate the BI at multiple depths for new areas using only spatial locations and observed BI measurements of previously drilled holes. Spatial interpolation techniques are investigated. This study introduces a novel treatment for Gaussian Processes (GPs) and Inverse Distance Weighting (IDW). Variography is leveraged to ensure an appropriate fit between the data and the spatial component. The parameters controlling anisotropy are constrained to intervals chosen to reflect the observed anisotropy. Gradient descent with back-propagation is used for optimization. The proposed approach improves the performance of GP and IDW at predicting BI. The similarities between the IDW variant proposed and a single-layer neural network are discussed.

A novel on-line dual sensing system for soil property measurement and mapping

Real-time assessment of within-field soil fertility variation is crucial for making informed and sustainable decisions, like crop nitrogen (N) fertilization. Two commonly used soil sensors i.e., visible-near-infrared (vis-NIR) spectroscopy and ion-selective electrodes (ISE) have been reported to successfully estimate various soil macronutrients and nitrate-N, respectively, however, their integrated use for mapping a full-scale soil fertility variation has not been taken into consideration to date. The aim of this study is to use an integrated on-line dual-sensor system of vis-NIRS and ISE to estimate and map within-field variation in multiple soil fertility parameters including pH, organic carbon (OC), extractable- phosphorus (P), potassium (K), calcium (Ca) and moisture content (MC) and nitrate-N. The sensing system was mounted to the three-point linkage of a tractor and surveyed two arable fields in Belgium. Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. Geostatistical surface maps for on-line measured soil attributes were generated using the inverse distance weighting and ordinary kriging methods for ISE data and vis NIR, respectively. The linear correlation confirms a very high similarity between ISE-measured nitrate-N and laboratory-analyzed nitrate-N (coefficient of determination (R2) = 0.93). Besides, the vis-NIRS demonstrates very good prediction accuracies for all the fertility attributes with R2 = 0.70-0.78, root mean square error (RMSE) =0.52-2.46 %, residual of prediction deviation (RPD) = 1.89 – 2.13 and the ratio of the performance to interquartile distance (RPIQ) = 1.72 – 6.56. Validation between laboratory and on-line measured soil maps also shows quite comparable spatial distribution patterns. Therefore, the proposed dual on-line sensor system has a high potential to estimate and map within-field spatial fertility distributions including nitrate-N, offering a basis for sustainable management decisions for precision soil and crop production.

Analyzing Soil Physical Properties in Kirkuk City, Iraq, Utilizing a Geographic Information System – Based Inverse Distance - Weighted Technique

Analyzing Soil Physical Properties in Kirkuk City, Iraq, Utilizing a Geographic Information System – Based Inverse Distance - Weighted Technique

ANALISIS POLA DISTRIBUSI CURAH HUJAN LEBAT DOMINAN DI DAERAH ALIRAN SUNGAI (DAS) CITANDUY HULU

Rainfall plays an important role in hydrological and environmental planning. Meteorological conditions affect rainfall through its intensity, amount, and duration. However, heavy rainfall can cause significant damage and loss. Research was conducted in the Upper Citanduy watershed, West Java, using rainfall data from 8 stations over a range of 5–14 years. The study took a heavy rainfall event threshold of ≥ 50 mm in 24 hours. The research aims to analyze the dominant heavy rainfall patterns in the Upper Citandyu watershed. Empirical analysis used the Mononobe Method and Alternating Block Method (ABM), while rainfall distribution was analyzed with Inverse Distance Weighted (IDW) using Geographic Information Systems. The dominant heavy rainfall duration in the Upper Citanduy watershed occurred over 4–6 hours. Rainfall distribution analysis showed a pattern that was not influenced by the elevation of the rainfall station. The analysis results show that the Mononobe Method is closer to the dominant observed heavy rainfall distribution for 4 hours, while ABM is closer to the durations of 5 and 6 hours. The suitability of the distribution pattern was measured using Correlation (r), Root Mean Squared Error (RMSE), and Nash-Sutcliffe Efficiency (NSE) criteria, with Mononobe providing better results than ABM method. This research contributes to the understanding of rainfall patterns that can form the basis for analyzing designed rainfall intensity as a step in flood disaster mitigation in the Upper Citanduy watershed.Keywords: heavy rainfall, rainfall distribution pattern, rainfall intensity, upper Citanduy watershed, geographic information system

Comparison of Inverse Distance Weighted and Thin Plate Spline Interpolation Methods in Projecting the Strength of the West Sumatra Earthquake

The Indonesian archipelago is situated in a highly active geological zone, making it prone to frequent earthquakes. West Sumatra, located on the west coast of central Sumatra, comprises lowland coastal areas and volcanic plateaus formed by the Barisan Mountains, covering a land area of 42,297.30 km² (2.17% of Indonesia's territory). This research aims to determine which interpolation method—Inverse Distance Weighted (IDW) and Thin Plate Spline (TPS)—provides more accurate predictions of earthquake strength in West Sumatra. The dataset consists of 229 earthquake events, divided into 90% for training (206 points) and 10% for testing (23 points). The training data was further subdivided into 80% training data 2 (164 points) and 20% validation data (42 points). The interpolation processes using the IDW and TPS methods were repeated 100 times, with the training 2 and validation data randomly shuffled in each iteration. Visualization of the interpolation results indicated that the earthquake magnitudes ranged from 2.0 to 4.5. Although the Mean Absolute Percentage Error (MAPE) values for the TPS method on the test and validation datasets were 16.42 and 14.29, respectively—slightly lower than the MAPE values for the IDW method—the t-test results showed no statistically significant difference between the two methods. Statistically, there is no significant difference between IDW and TPS in terms of predictive accuracy. However, researchers prefer the IDW method due to its computational efficiency and simplicity. Therefore, IDW is considered the most suitable method for analyzing earthquake strength in the West Sumatra region

APPLICATION OF GLOBAL CIRCULATION MODELS (GCMS) TO PREDICT RAINFALL AND TEMPERATURE VARIABILITIES IN KANO STATE, NIGERIA

The study explored present and future climate scenarios of Kano using Global Circulation Models (GCMs). The metrological data from 12 locations were used for calibration of Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). The historical record of 10 years was used for both temperatures and rainfall data which represent a period of 2010 to 2019. The future climate projections were made for 2041 to 2060 centered in the 2050s under RCP2.6 and RCP8.5 emission scenarios. The spatial analysis was conducted using the Inverse Distance Weighted (IDW) tool in an ArcGIS environment. The result shows that the temperature of Kano has increased relative to baseline condition by 1.025oC (2.45%) and 4.6 oC (10.98%) under RCP2.6 and RCP8.5 scenarios respectively. However, the annual rainfall was found to decrease from the historical amount by -37.9 mm (5%), and -68.2 mm (9%) under RCP2.6 and RCP8.5 emission scenarios. Moreover, the spatial analysis indicated that there was climate variability across the width and breadth of the state. The study concludes that the climate change effect will cause the temperature of Kano to rise considerably in the future with the consequences of decreased rainfall amount. The findings recommend that policymakers should deploy adaptation strategies to reduce the adverse effects that might be experienced.

Characterization of Natural Gas Hydrate Constrained by Well and Seismic Data in Qiongdongnan Basin

This study investigates the natural gas hydrates within the Qiongdongnan Basin by integrating well-log and seismic data. Through pre-stack inversion and rock physics analysis, key parameters such as P-wave and S-wave impedances were utilized to distinguish hydrate-bearing formations from other geological bodies. A low-frequency model was constructed using the Inverse Distance Weighting (IDW) algorithm to improve the precision of parameter inversion. This study employs a multi-constraint inversion strategy, incorporating hard constraints from multiple wells and soft constraints from geological frameworks, ensuring reliable inversion results. Findings indicate that hydrate reservoirs are characterized by increased wave velocity and density due to hydrate accumulation, providing insights into the spatial distribution and characteristics of hydrates. This research enhances the understanding of hydrate reservoirs and offers valuable data for exploration in the Qiongdongnan Basin.

2D BAO vs. 3D BAO: Solving the Hubble Tension with Bimetric Cosmology

Ordinary 3D Baryon Acoustic Oscillations (BAO) data are model-dependent, requiring the assumption of a cosmological model to calculate comoving distances during data reduction. Throughout the present-day literature, the assumed model is ΛCDM. However, it has been pointed out in several recent works that this assumption can be inadequate when analyzing alternative cosmologies, potentially biasing the Hubble constant (H0) low, thus contributing to the Hubble tension. To address this issue, 3D BAO data can be replaced with 2D BAO data, which are only weakly model-dependent. The impact of using 2D BAO data, in combination with alternative cosmological models beyond ΛCDM, has been explored for several phenomenological models, showing a promising reduction in the Hubble tension. In this work, we accommodate these models in the theoretically robust framework of bimetric gravity. This is a modified theory of gravity that exhibits a transition from a (possibly) negative cosmological constant in the early universe to a positive one in the late universe. By combining 2D BAO data with cosmic microwave background and type Ia supernovae data, we find that the inverse distance ladder in this theory yields a Hubble constant of H0=(71.0±0.9)km/s/Mpc, consistent with the SH0ES local distance ladder measurement of H0=(73.0±1.0)km/s/Mpc. Replacing 2D BAO with 3D BAO results in H0=(68.6±0.5)km/s/Mpc from the inverse distance ladder. We conclude that the choice of BAO data significantly impacts the Hubble tension, with ordinary 3D BAO data exacerbating the tension, while 2D BAO data provide results consistent with the local distance ladder.

Long-term exposure to PM2.5 and mortality among Japanese community-dwelling adults

Abstract Background Long-term exposure to PM2.5 (particulate matter with a diameter of 2.5 μm or smaller) is a risk factor for mortality and cardiovascular disease including stroke. However, there was limited evidence for the association between PM2.5 exposure and stroke in Asia where the major cardiovascular disease is stroke. Additionally, the causal association between PM2.5 and respiratory disease is still unresolved. The aim of this study was to investigate the association between long-term exposure to PM2.5 and all-cause, cardiovascular, and respiratory disease mortality in a Japanese community-based prospective cohort. Methods This study included 46,974 participants (19,707 men; 27,267 women) from the second cohort of the Ibaraki Prefectural Health Study. The participants were followed up from baseline survey (in 2009) to 31 December 2019. We confirmed underlying causes by death certificates. Using the PM2.5 concentrations estimated by the inverse distance weighting methods based on ambient air monitoring data, administrative area level concentrations were assigned to each participant. The hazard ratios (HRs) and 95% confidence intervals (CIs) of mortality using a Cox proportional hazard model were presented. Results We identified 2,789 all-cause deaths during the 470,178 person-years of follow-up. PM2.5 exposure had no significant association with all-cause and cause-specific mortality. The multivariable-adjusted HRs per 1 μg/m3 increase in the PM2.5 concentration were 0.94 (95% CI = 0.80-1.12) for coronary heart disease, 0.92 (95% CI = 0.81-1.04) for stroke and 1.09 (95% CI = 0.97-1.23) for non-malignant respiratory disease. Conclusions We observed little evidence of increased risk of all-cause and cause-specific mortality by long-term exposure to PM2.5 whose concentrations ranged from 8.3 to 13.1 μg/m3. A somewhat increased trend for non-malignant respiratory disease mortality may not be negligible. Key messages • We found little evidence of increased risk of all-cause and cause-specific mortality by long-term exposure to PM2.5. • Somewhat increased tendency for non-malignant respiratory disease mortality associated with PM2.5 exposure was observed.

Application of GIS for Monitoring Firefly Population Abundance (Pteroptyx tener) and the Influence of Abiotic Factors

This study focuses on the <i>Pteroptyx tener</i> species in the Sepetang River, Malaysia, aiming to evaluate the firefly’s abundance and explore its correlation with various biotic and abiotic parameters. The study was conducted over six months, from November 2021 to April 2022, utilizing Geographic Information System (GIS) software to apply hotspot mapping and Inverse Distance Weighting (IDW) analysis to elucidate the spatial distribution of firefly populations. A total of 111,615 individuals were recorded, with a particular focus on this firefly species’ presence on their display trees. Hotspot analysis showed that Station 6, located at the mouth of a river with dense mangroves, hosted 55,723 fireflies (50.01%). In contrast, Stations 9 and 10, near ponds and shrimp settlements, recorded 517–723 fireflies (0.65% and 0.46%). Pearson’s correlation coefficient (r) unveiled a statistically significant positive correlation (r = 0.88, p < 0.05) between wind speed and the abundance of firefly populations within the Sepetang River. However, no statistically significant correlation (p > 0.05) was found between firefly abundance and various other abiotic parameters, including relative humidity (RH), air temperature, tide level, pH, electrical conductivity (EC), salinity, total dissolved solids (TDS), and water clarity. Thus, the results revealed the preference for fireflies due to the availability of vegetation, wind speed and minimal disturbance in this area. In conclusion, this study’s information significantly adds to our understanding of these interesting insects and their complicated relationships in nature. It underscores the importance of preserving their habitats and ecosystems.

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

Hydrochemical Assessment of Groundwater in Ludhiana and Amritsar Districts of Punjab and Identification of Fluoride Hotspots using GIS

High fluoride concentrations in soil, water, or air can pose serious environmental and health risks to plants, and animals. Along with other hydrochemical parameters, this study investigates fluoride concentrations in the groundwater in the Ludhiana and Amritsar districts of Punjab, India. A total of 222 water samples were uniformly collected at approximately five-kilometer intervals for hydrochemical analyses. Statistical methods such as inverse distance weighting (IDW) and correlation matrices were used to assess the fluoride distribution and its relationships with other parameters. According to WHO guidelines, most fluoride concentrations were below 0.6 ppm in Ludhiana (84.30%) and Amritsar (77.23%). Fluoride levels that were within the permissible range (0.6–1.5 ppm) were found in 15.70% of Ludhiana’s samples and 21.78% of Amritsar’s samples; only 1% of Amritsar’s samples exceeded the permissible limit (>1.5 ppm). The water quality index (WQI) analysis indicated that0.83% of the groundwater samples from the Ludhiana district and 4.95% from the Amritsar district were unfit for consumption. This study demonstrates the importance of standardized sample collection and the use of GIS technology for comprehensive hydrochemical assessments, raising awareness and reducing health risks.

Mapping the distribution of tuberculosis cases and associated factors identified through routine program implementation and community-based active screening in Central Ethiopia.

Smear-positive TB patients greatly contribute to community-level transmission of this disease. Locating hotspots would make it easier to prioritize and target control interventions. This study aimed to assess the spatial distribution of smear-positive index TB cases and their secondary cases and the predictors of clustering of smear-positive TB cases. This study was conducted in the Silti Zone of Central Ethiopia from 2020 to 2022. The data of smear-positive index TB patients were collected from the unit TB registries of healthcare facilities. Contacts of all index TB patients were screened in the community and tested to identify secondary TB patients. We performed spatial analysis, including Moran's I statistic, the Getis-Ord Gi* statistic and geographically weighted regression (GWR), to assess the global distribution, local clustering and predictors of clustering of smear-positive TB patients, respectively. Additionally, we used inverse distance weighting (IDW) interpolation to predict the distribution of smear-positive TB cases and develop a continuous raster map for places with no data. Spatial autocorrelation analysis revealed that the distribution of smear-positive TB patients exhibited significant clustering (Moran's I = 0.70029; p value < 0.000). The Getis-Ord Gi* output indicated the presence of statistically significant hotspots as well as cold spots in the study area. Significant hotspots were found in 11 Kebeles of the Silti, Dalocha and Misrak Silti districts. Significant coldspots were also found in five kebeles of the Silti and Misrak districts. GWR analysis revealed that no education, primary education, family size and thatched roof houses were significant predictors of the spatial clustering of smear-positive TB cases. We also found that the majority of the secondary TB cases were found in hotspots identified through spatial analysis. The study revealed a heterogenous distribution of smear positive TB in the study area and it could act as a model that can be replicated in other regions. The identified hotspots of TB could be targeted through location-based interventions such as systematic active screening in the form of outreach programs to improve the performance of TB prevention and control, including reducing the transmission of TB. Educational status, family size and housing type were some of the factors that significantly influenced the spatial distribution of smear-positive TB in the study area. The distribution of secondary TB cases found through household contact screening coincided with the identified hotspots, indicating greater transmission of the disease in these places.

Measuring Geographic Opportunity for Higher Education in US Metropolitan Areas

AbstractResearch demonstrates that students who live closer to postsecondary institutions are more likely to attend college. Yet many communities offer few or no postsecondary options. In this study, we expand on prior research on the geographic distribution of postsecondary opportunity by developing a finer-grained measure that accounts for both the variety and density of college options in each metropolitan statistical area (MSA). Our estimates use several decay functions to measure the distance-weighted characteristics of institutions, allowing us to cross-validate different approaches and identify the most appropriate model to inform future analyses of geographic opportunity. The results of the cross-validation indicate that both the type of geographic measure used and the penalty in the inverse distance weighting function result in substantively different model fit. We find that each of the measures used in a spatial model—enrollment size, tuition, and net price—has a positive and statistically significant relationship with postsecondary enrollment at the MSA level, indicating the continuing importance of geographic opportunity for college attendance.