Using kernel density estimation to understand the influence of neighbourhood destinations on BMI

Geo-Demographic and Socioeconomic Determinants of Diagnosed Hypertension among Urban Dwellers in Ibadan, Nigeria: A Community-based Study

Background

The relationship between diagnosed high blood pressure (HBP) and proximity to health facilities and noise sources is poorly understood. We investigated the relationship between proximity to noise sources, sociodemographic and economic factors, and diagnosed HBP in Ibadan, Nigeria.

Methods

We investigated 13,531 adults from the African Rigorous Innovative Stroke Epidemiological Surveillance (ARISES) study in Ibadan. Using a Geographic Information System (GIS), the locations of healthcare facilities, pharmaceutical shops, bus stops, churches, and mosques were buffered at 100m intervals, and coordinates of persons diagnosed with HBP were overlaid on the buffered features. The number of persons with diagnosed HBP living at every 100m interval was estimated. Gender, occupation, marital status, educational status, type of housing, age, and income were used as predictor variables. Analysis was conducted using Spearman rank correlation and binary logistic regression at p<0.05.

Results

There was a significant inverse relationship between the number of persons diagnosed with HBP and distance from pharmaceutical shops (r=-0.818), churches (r=-0.818), mosques (r=-0.893) and major roads (r=-0.667). The odds of diagnosed HBP were higher among the unemployed (AOR=1.58, 95% CI: 1.11-2.24), currently married (AOR=1.45, CI: 1.11-1.89), and previously married (1.75, CI: 1.29-2.38). The odds of diagnosed HBP increased with educational level and age group.

Conclusion

Proximity to noise sources, being unemployed and educational level were associated with diagnosed HBP. Reduction in noise generation, transmission, and exposure could reduce the burden of hypertension in urban settings.

Tobacco retailer density and smoking behaviour: how are exposure and outcome measures classified? A systematic review

INTRODUCTION

To date only a limited number of reviews have focused on how exposure and outcome measures are defined in the existing literature on associations between tobacco retailer density ('density') and smoking behaviour ('smoking'). Therefore this systematic review classified and summarised how both density and smoking variables are operationalised in the existing literature, and provides several methodological recommendations for future density and smoking research.

METHODS

Two literature searches between March and April 2018 and April 2022 were conducted across 10 databases. Inclusion and exclusion criteria were developed and keyword database searches were undertaken. Studies were imported into Covidence. Cross-sectional studies that met the inclusion criteria were extracted and a quality assessment was undertaken. Studies were categorised according to the density measure used, and smoking was re-categorised using a modified classification tool.

RESULTS

Large heterogeneity was found in the operationalisation of both measures in the 47 studies included for analysis. Density was most commonly measured directly from geocoded locations using circular buffers at various distances (n = 14). After smoking was reclassified using a smoking classification tool, past-month smoking was the most common smoking type reported (n = 26).

CONCLUSIONS

It is recommended that density is measured through length-distance and travel time using the street network and weighted (e.g. by the size of an area), or by using Kernel Density Estimates as these methods provide a more accurate measure of geographical to tobacco and e-cigarette retailer density. The consistent application of a smoking measures classification tool, such as the one developed for this systematic review, would enable better comparisons between studies. Future research should measure exposure and outcome measures in a way that makes them comparable with other studies.

IMPLICATIONS

This systematic review provides a strong case for improving data collection and analysis methodologies in studies assessing tobacco retailer density and smoking behaviour to ensure that both exposure and outcome measures are clearly defined and captured. As large heterogeneity was found in the operationalisation of both density and smoking behaviour measures in the studies included for analysis, there is a need for future studies to capture, measure and classify exposure measures accurately, and to define outcome measures in a manner that makes them comparable with other studies.

Capturing the Complex Relationship Between Internal and External Training Load: A Data-Driven Approach

BACKGROUND

Training load is typically described in terms of internal and external load. Investigating the coupling of internal and external training load is relevant to many sports. Here, continuous kernel-density estimation (KDE) may be a valuable tool to capture and visualize this coupling.

AIM

Using training load data in speed skating, we evaluated how well bivariate KDE plots describe the coupling of internal and external load and differentiate between specific training sessions, compared to training impulse scores or intensity distribution into training zones.

METHODS

On-ice training sessions of 18 young (sub)elite speed skaters were monitored for velocity and heart rate during 2 consecutive seasons. Training session types were obtained from the coach's training scheme, including endurance, interval, tempo, and sprint sessions. Differences in training load between session types were assessed using Kruskal-Wallis or Kolmogorov-Smirnov tests for training impulse and KDE scores, respectively.

RESULTS

Training impulse scores were not different between training session types, except for extensive endurance sessions. However, all training session types differed when comparing KDEs for heart rate and velocity (both P < .001). In addition, 2D KDE plots of heart rate and velocity provide detailed insights into the (subtle differences in) coupling of internal and external training load that could not be obtained by 2D plots using training zones.

CONCLUSION

2D KDE plots provide a valuable tool to visualize and inform coaches on the (subtle differences in) coupling of internal and external training load for training sessions. This will help coaches design better training schemes aiming at desired training adaptations.

A spatial obesity risk score for describing the obesogenic environment using kernel density estimation: development and parameter variation

BACKGROUND

Overweight and obesity are severe public health problems worldwide. Obesity can lead to chronic diseases such as type 2 diabetes mellitus. Environmental factors may affect lifestyle aspects and are therefore expected to influence people's weight status. To assess environmental risks, several methods have been tested using geographic information systems. Freely available data from online geocoding services such as OpenStreetMap (OSM) can be used to determine the spatial distribution of these obesogenic factors. The aim of our study was to develop and test a spatial obesity risk score (SORS) based on data from OSM and using kernel density estimation (KDE).

METHODS

Obesity-related factors were downloaded from OSM for two municipalities in Bavaria, Germany. We visualized obesogenic and protective risk factors on maps and tested the spatial heterogeneity via Ripley's K function. Subsequently, we developed the SORS based on positive and negative KDE surfaces. Risk score values were estimated at 50 random spatial data points. We examined the bandwidth, edge correction, weighting, interpolation method, and numbers of grid points. To account for uncertainty, a spatial bootstrap (1000 samples) was integrated, which was used to evaluate the parameter selection via the ANOVA F statistic.

RESULTS

We found significantly clustered patterns of the obesogenic and protective environmental factors according to Ripley's K function. Separate density maps enabled ex ante visualization of the positive and negative density layers. Furthermore, visual inspection of the final risk score values made it possible to identify overall high- and low-risk areas within our two study areas. Parameter choice for the bandwidth and the edge correction had the highest impact on the SORS results.

DISCUSSION

The SORS made it possible to visualize risk patterns across our study areas. Our score and parameter testing approach has been proven to be geographically scalable and can be applied to other geographic areas and in other contexts. Parameter choice played a major role in the score results and therefore needs careful consideration in future applications.

Exploring non-linear relationships between neighbourhood walkability and health: a cross-sectional study among US primary care patients with chronic conditions

BACKGROUND

A recent study of licensed drivers found a non-linear relationship between density of non-residential destinations (NRDs), a proxy for walkability and body mass index (BMI) across a wide range of development patterns. It is unclear if this relationship can be replicated in a population with multiple chronic conditions or translated to health outcomes other than BMI.

METHODS

We obtained health data and home addresses for 2405 adults with multiple chronic conditions from 44 primary care clinics across 13 states using the Integrating Behavioral health and Primary Care Trial. In this cross-sectional study, the relationships between density of NRDs (from a commercial database) within 1 km of the home address and self-reported BMI, and mental and physical health indices were assessed using several non-linear methods, including restricted cubic splines, LOWESS smoothing curves, non-parametric regression with a spline basis and piecewise linear regression.

RESULTS

All methods demonstrated similar non-linear relationships. Piecewise linear regression was selected for ease of interpretation. BMI had a positive marginal rate of change below the NRD density inflection point of 15 establishments/hectare (β=+0.09 kg/m²/non-residential buildings ha-1; 95% CI +0.01 to +0.14), and a negative marginal rate of change above the inflection point (β=-0.02; 95% CI -0.06 to 0.02). Mental health decreased with NRD density below the inflection point (β=-0.24; 95% CI -0.31 to -0.17) and increased above it (β=+0.03; 95% CI -0.00 to +0.07). Results were similar for physical health (β= -0.28; 95% CI -0.35 to -0.20) and (β=+0.06; 95% CI 0.01 to +0.10).

CONCLUSION

Health indicators were the lowest in middle density (typically suburban) areas and got progressively better moving in either direction from the peak. NRDs may affect health differently depending on home-address NRD density.

TRIAL REGISTRATION NUMBER

NCT02868983.

Nonlinear Relationships among the Natural Environment, Health, and Sociodemographic Characteristics across US Counties

Background: The aim of this study was to explore the nonlinear relationships between natural amenities and health at the intersection of sociodemographic characteristics among primary care patients with chronic conditions. Methods: We used survey data from 3409 adults across 119 US counties. PROMIS-29 mental and physical health summary scores were the primary outcomes. The natural environment (measured using the County USDA Natural Amenities Scale (NAS)) was the primary predictor. Piecewise spline regression models were used to explore the relationships between NAS and health at the intersection of sociodemographic factors. Results: We identified a nonlinear relationship between NAS and health. Low-income individuals had a negative association with health with each increase in NAS in high-amenity areas only. However, White individuals had a stronger association with health with each increase in NAS in low-amenity areas. Conclusions: In areas with low natural amenities, more amenities are associated with better physical and mental health, but only for advantaged populations. Meanwhile, for disadvantaged populations, an increase in amenities in high-amenity areas is associated with decreases in mental and physical health. Understanding how traditionally advantaged populations utilize the natural environment could provide insight into the mechanisms driving these disparities.

Spatial Variation in Risk for Highly Pathogenic Avian Influenza Subtype H5N6 Viral Infections in South Korea: Poultry Population-Based Case–Control Study

Given the substantial economic damage caused by the continual circulation of highly pathogenic avian influenza (HPAI) outbreaks since 2003, identifying high-risk locations associated with HPAI infections is essential. In this study, using affected and unaffected poultry farms’ locations during an HPAI H5N6 epidemic in South Korea, we identified places where clusters of HPAI cases were found. Hotspots were defined as regions having clusters of HPAI cases. With the help of the statistical computer program R, a kernel density estimate and a spatial scan statistic were employed for this purpose. A kernel density estimate and detection of significant clusters through a spatial scan statistic both showed that districts in the Chungcheongbuk-do, Jeollabuk-do, and Jeollanam-do provinces are more vulnerable to HPAI outbreaks. Prior to the migration season, high-risk districts should implement particular biosecurity measures. High biosecurity measures, as well as improving the cleanliness of the poultry environment, would undoubtedly aid in the prevention of HPAIV transmission to poultry farms in these high-risk regions of South Korea.

Nonlinear relationship between nonresidential destinations and body mass index across a wide range of development

BACKGROUND

Destination accessibility is an important measure of the built environment that is associated with active transport and body mass index (BMI). In higher density settings, an inverse association has been consistently found, but in lower density settings, findings are limited. We previously found a positive relationship between the density of nonresidential destinations (NRD) and BMI in a low-density state. We sought to test the generalizability of this unexpected finding using data from six other states that include a broader range of settlement densities.

METHODS

We obtained the address, height, and weight of 16.9 million residents with a driver's license or state identification cards, as well as the location of 3.8 million NRDs in Washington, Oregon, Texas, Illinois, Michigan, and Maine from Dun & Bradstreet. We tested the association between NRDs∙ha^-1 within 1 km of the home address, and self-reported BMI (kg∙m^-2). Visualization by locally-weighted smoothing curves (LOWESS) revealed an inverted U-shape. A multivariable piecewise regression with a random intercept for state was used to assess the relationship.

RESULTS

After accounting for age, sex, year of issue, and census tract social and economic variables, BMI correlated positively with NRDs in the low-to-mid density stratum (β = +0.005 kg∙m^-2/nonresidential building∙ha^-1; 95% CI: +0.004,+0.006) and negatively in the mid-to-high density stratum (β = -0.002; 95% CI: -0.004,-0.0003); a significant difference in slopes (P < 0.001).

CONCLUSIONS

BMI peaked in the middle density, with lower values in both the low and high-density extremes. These results suggest that the mechanisms by which NRDs are associated with obesity may differ by density level.

Spatial Distribution and Land Use of Traditional Villages in Southwest China

Traditional villages are the historical and cultural heritage of people around the world. With the increases in urbanization and industrialization, the continuation of traditional villages and the inheritance of historical and cultural heritage are facing risk. Therefore, to grasp the spatial characteristics of them and the human–nature interaction mechanism in Southwest China, we analyzed the distribution pattern of traditional villages using the ArcGIS software. Then, we further analyzed the spatial clustering characteristics, influencing factors and landscape pattern, and put forward relevant protection countermeasures and suggestions. The results revealed that traditional villages in Southwest China were clustered, being mainly distributed in areas with relatively low elevation, gentle slopes, low relative positions, nearby water sources, and convenient transportation. They can be divided into four categories due to obvious differences in influencing factors such as elevation, slope, relative position, distance to the nearest river, population density, etc. The landscape pattern of traditional villages differed among the different clusters, being mainly composed of forests, shrubs, and cultivated land. With the increase in the buffer radius, the landscape pattern of them changed significantly. The results of this study reflect that traditional villages and the natural environment are interdependent, so the protection of traditional villages should carry out measures according to local conditions.

Where we eat is who we are: a survey of food-related travel patterns to Singapore's hawker centers, food courts and coffee shops

Background

The development of empirically-grounded policies to change the obesogenic nature of urban environment has been impeded by limited, inconclusive evidence of the link between food environments, dietary behaviors, and health-related outcomes, in part due to inconsistent methods of classifying and analyzing food environments. This study explores how individual and built environment characteristics may be associated with how far and long people travel to food venues,that can serve as a starting point for further policy-oriented research to develop a more nuanced, context-specific delineations of ‘food environments’ in an urban Asian context.

Methods

Five hundred twenty nine diners in eight different neighborhoods in Singapore were surveyed about how far and long they travelled to their meal venues, and by what mode. We then examined how respondents’ food-related travel differed by socioeconomic characteristics, as well as objectively-measured built environment characteristics at travel origin and destination, using linear regression models.

Results

Low-income individuals expended more time traveling to meal destinations than high-income individuals, largely because they utilized slower modes like walking rather than driving. Those travelling from areas with high food outlet density travelled shorter distances and times than those from food-sparse areas, while those seeking meals away from their home and work anchor points had lower thresholds for travel. Respondents also travelled longer distances to food-dense locations, compared to food-sparse locations.

Conclusion

Those seeking to improve food environments of poor individuals should consider studying an intervention radius pegged to typical walking distances, or ways to improve their transport options as a starting point. Policy-focused research on food environments should also be sensitive to locational characteristics, such as food outlet densities and land use.

Schistosoma japonicum-infected sentinel mice: Surveillance and spatial point pattern analysis in Hubei province, China, 2010-2018

OBJECTIVES

Progress in national schistosomiasis control in China has successfully reduced disease transmission in many districts. However, a low transmission rate hinders conventional snail surveys in identifying areas at risk. In this study, Schistosoma japonicum-infected sentinel mice surveillance was conducted to identify high-risk areas of schistosomiasis transmission in Hubei province, China.

METHODS

The risk of schistosomiasis transmission was assessed using sentinel mice monitoring in Hubei province from 2010 to 2018. Field detections were undertaken in June and September, and the sentinel mice were kept for approximately 35 days in a laboratory. They were then dissected to determine whether schistosome infection was present. Ripley's K-function and kernel density estimation were applied to analyze the spatial distribution and positive point pattern of schistosomiasis transmission.

RESULTS

In total, 190 sentinel mice surveillance sites were selected to detect areas of schistosomiasis infection from 2010 to 2018, with 29 (15.26%) sites showing infected mice. Of 4723 dissected mice, 256 adult worms were detected in 112 infected mice. The infection rate was 2.37%, with an average of 2.28 worms detected per infected mouse. Significantly more infected mice were detected in the June samples than in the September samples (χ²=12.11, p<0.01). Ripley's L(d) index analysis showed that, when the distance was ≤34.52km, the sentinel mice infection pattern showed aggregation, with the strongest aggregation occurring at 7.86km. Three hotspots were detected using kernel density estimation: at the junction of Jingzhou District, Gong'an County, and Shashi District in Jingzhou City; in Wuhan City at the border of the Huangpi and Dongxihu Districts, and in the Hannan and Caidian Districts.

CONCLUSION

The results showed that sentinel mice surveillance is useful in identifying high-risk areas, and could provide valuable information for schistosomiasis prevention and control, especially concerning areas along the Yangtze River, such as the Fu-Lun, Dongjing-Tongshun, and Juzhang River basins.

Geographic mapping of Enterobacteriaceae with extended-spectrum β-lactamase (ESBL) phenotype in Pereira, Colombia

Background

Antimicrobial resistance is an ecological and multicausal problem. Infections caused by extended-spectrum β-lactamase producing Enterobacteriaceae (ESBL-E) can be acquired and transmitted in the community. Data on community-associated ESBL-E infections/colonizations in Colombia are scarce. Georeferencing tools can be used to study the dynamics of antimicrobial resistance at the community level.

Methods

We conducted a study of geographic mapping using modern tools based on geographic information systems (GIS). Two study centers from the city of Pereira, Colombia were involved. The records of patients who had ESBL-producing Enterobacteriaceae were reviewed. Antimicrobial susceptibility testing and phenotypic detection of ESBL was done according to CLSI standards.

Results

A population of 415 patients with community-acquired infections/colonizations and 77 hospital discharges were obtained. Geographic distribution was established and heat maps were created. Several hotspots were evidenced in some geographical areas of the south-west and north-east of the city. Many of the affected areas were near tertiary hospitals, rivers, and poultry industry areas.

Conclusions

There are foci of antimicrobial resistance at the community level. This was demonstrated in the case of antimicrobial resistance caused by ESBL in a city in Colombia. Causality with tertiary hospitals in the city, some rivers and the poultry industry is proposed as an explanation of the evidenced phenomenon. Geographic mapping tools are useful for monitoring antimicrobial resistance in the community.

Categorization of Green Spaces for a Sustainable Environment and Smart City Architecture by Utilizing Big Data

Urban green spaces promote outdoor activities and social interaction, which make a significant contribution to the health and well-being of residents. This study presents an approach that focuses on the real spatial and temporal behavior of park visitors in different categories of green parks. We used the large dataset available from the Chinese micro-blog Sina Weibo (often simply referred to as “Weibo”) to analyze data samples, in order to describe the behavioral patterns of millions of people with access to green spaces. We select Shanghai as a case study because urban residential segregation has already taken place, which was expected to be followed by concerns of environmental sustainability. In this research, we utilized social media check-in data to measure and compare the number of visitations to different kinds of green parks. Furthermore, we divided the green spaces into different categories according to their characteristics, and our main findings were: (1) the most popular category based upon the check-in data; (2) changes in the number of visitors according to the time of day; (3) seasonal impacts on behavior in public in relation to the different categories of parks; and (4) gender-based differences. To the best of our knowledge, this is the first study carried out in Shanghai utilizing Weibo data to focus upon the categorization of green space. It is also the first to offer recommendations for planners regarding the type of facilities they should provide to residents in green spaces, and regarding the sustainability of urban environments and smart city architecture.

Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai

Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors.

Validating a GPS-based approach to detect health facility visits against maternal response to prompted recall survey

Introduction

Common approaches to measure health behaviors rely on participant responses and are subject to bias. Technology-based alternatives, particularly using GPS, address these biases while opening new channels for research. This study describes the development and implementation of a GPS-based approach to detect health facility visits in rural Pune district, India.

Methods

Participants were mothers of under-five year old children within the Vadu Demographic Surveillance area. Participants received GPS-enabled smartphones pre-installed with a location-aware application to continuously record and transmit participant location data to a central server. Data were analyzed to identify health facility visits according to a parameter-based approach, optimal thresholds of which were calibrated through a simulation exercise. Lists of GPS-detected health facility visits were generated at each of six follow-up home visits and reviewed with participants through prompted recall survey, confirming visits which were correctly identified. Detected visits were analyzed using logistic regression to explore factors associated with the identification of false positive GPS-detected visits.

Results

We enrolled 200 participants and completed 1098 follow-up visits over the six-month study period. Prompted recall surveys were completed for 694 follow-up visits with one or more GPS-detected health facility visits. While the approach performed well during calibration (positive predictive value (PPV) 78%), performance was poor when applied to participant data. Only 440 of 22 251 detected visits were confirmed (PPV 2%). False positives increased as participants spent more time in areas of high health facility density (odds ratio (OR) = 2.29, 95% confidence interval (CI) = 1.62-3.25). Visits detected at facilities other than hospitals and clinics were also more likely to be false positives (OR = 2.78, 95% CI = 1.65-4.67) as were visits detected to facilities nearby participant homes, with the likelihood decreasing as distance increased (OR = 0.89, 95% CI = 0.82-0.97). Visit duration was not associated with confirmation status.

Conclusions

The optimal parameter combination for health facility visits simulated by field workers substantially overestimated health visits from participant GPS data. This study provides useful insights into the challenges in detecting health facility visits where providers are numerous, highly clustered within urban centers and located near residential areas of the population which they serve.

Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data

Green parks are vital public spaces and play a major role in urban living and well-being. Research on the attractiveness of green parks often relies on traditional techniques, such as questionnaires and in-situ surveys, but these methods are typically insignificant in scale, time-consuming, and expensive, with less transferable results and only site-specific outcomes. This article presents an investigative study that uses location-based social network (LBSN) data to collect spatial and temporal patterns of park visits in Shanghai metropolitan city. During the period from July 2016 to June 2017 in Shanghai, China, we analyzed the spatiotemporal behavior of park visitors for 157 green parks and conducted empirical research on the impacts of green spaces on the public’s behavior in Shanghai. Our main findings show (i) the check-in distribution of users in different green spaces; (ii) the seasonal effects on the public’s behavior toward green spaces; (iii) changes in the number of users based on the hour of the day, the intervals of the day (morning, afternoon, evening), and the day of the week; (iv) interesting user behavior variations that depend on temperature effects; and (v) gender-based differences in the number of green park visitors. These results can be used for the purpose of urban city planning for green spaces by accounting for the preferences of visitors.

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

Geospatial intelligence and health analitycs: Its application and utility in a city with high tuberculosis incidence in Brazil

BACKGROUND

Geospatial Intelligence and Health Analysis have been used to identify tuberculosis (TB) hotspots and to better understand their relationship to social and economic factors. The purpose of this study was to use geospatial intelligence to assess the distribution of TB and its correlations with Human Development Index (HDI) in a city with high TB incidence in Brazil.

METHODS

We conducted an ecological study, using National System of Information on Noticeable Disease (SINAN) to identify TB cases. Geocoding was performed using QGIS 2.0 software and Google Maps API 3.0. We applied geospatial intelligence to detect where in the city clustering of TB cases occurred, and assessed the association of an area's HDI (each one of the components - longevity, education, and income) with TB spatial distribution.

RESULTS

During the study period (2011-2013), there were 737 TB cases. TB cases showed heterogeneity across the 29 neighborhoods. The neighborhoods with HDI-income lower than the mean had higher TB incidence (p = 0.036).

CONCLUSIONS

We found several hotspots of TB across the 29 neighborhoods, and an inverse association between HDI-income and TB incidence. These findings provide useful information and may help to guide TB control programs.

Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.