SPATIAL ANALYSIS OF CERVICAL CANCER INCIDENCE IN IRAQ DURING 2010-2015

Cervical cancer is the most common cancer in Iraq. This study aimed to examine the spatial pattern and high-risk clusters of cervical cancer in different areas of Iraq (except the Kurdish region) during the period 2010-2015. The age-adjusted incidence rates (AAIRs), age-specific incidence rates (ASIRs), and annual percent changes (APCs) were calculated for 2010–2015. We used three techniques of spatial statistical analysis which are Global spatial autocorrelation, Getis-Ord Gi* statistic, and Anselin Local Moran’s statistic to investigate spatial clustering and outliers. Over the period 2000-2015, 1037 cases were diagnosed with AAIR of incidence of (3.521 per 100,000). Depending on AAIRs, we found cervical cancer incidence was highest (8.212 per 100,000) in the age group of 60-69 years old. The APC of cervical cancer age-specific rates has significantly risen for the age groups 60-69 only. We identified spatial high-risk clusters for incidence. The cluster was mainly located in the center of Iraq (Baghdad, Najaf, Kerbala, and Babil provinces), the central-eastern part of Iraq (Diyala province), and the southern part of Iraq (Basrah province). While the provinces in the northern and northeastern parts of Iraq, as well as the western province, had relatively lower AAIRs from cervical cancer. A spatial cluster pattern for the incidence of cervical cancer in Iraq was revealed, which will be valuable for improving the allocation of health resources in Iraq.

Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network’s data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors’ spatial operations are more intense than residents’ spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists’ temporal behaviors vary significantly while citizens’ movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities.

The Coexistence of Nanostores within the Retail Landscape: A Spatial Statistical Study for Mexico City

This paper aims to improve understanding of the grocery retail landscape in a megacity in the developing world. Over the past ten years (i.e., 2010–2020), retail in the grocery sector in Mexico City has changed significantly. The fast growth of chain convenience stores (CCS) and the financial stability of modern channel stores (MCS) has provoked speculation about whether nanostores (i.e., mom-and-pop stores) are going to disappear or if they will endure. In developing countries nanostores dominate the grocery retail market, providing a source of income to millions of families. While some studies suggest that nanostores will keep growing in number because they are more likely to be attached to growing middle- and low-income consumers in developing countries, our results show that high- and medium–high-income consumers purchase at nanostores too. Through a comprehensive spatial statistical analysis, we provide evidence that nanostores will endure and, most importantly, coexist with MCS and CCS regardless of the socioeconomic levels that coexist in the urban areas of a developing megacity. To gain a thorough understanding of how the grocery retail market is organized in Mexico City, and provide a richer discussion on the logistics and managerial implications for stakeholders (e.g., customers, practitioners, shopkeepers, suppliers, and policymakers), we also validate hypotheses from the literature related to this context and the three channels studied in this work.

SPATIAL STATISTICAL ANALYSIS OF FEMALE BREAST CANCER INCIDENCE IN IRAQ DURING 2000-2015

The spatial statistical analysis of breast cancer incidences across Iraq has not been explored in Iraq. This paper aimed to explore the spatial pattern and risk clusters of female breast cancer incidence from 2000 to 2015 in Iraq (except the Kurdish region). To enhance statistical stability and to access the changes over time, we split the data according to the geographical district into three periods (2000-2004, 2005-2009, and 2010-2015). The age-standardized incidence rates (ASRs) were calculated using the world standard population. Having obtained estimates ASRs, the global index of spatial autocorrelation (Moran’s statistic) was used to assess spatial dependence across districts for ASRs. Anselin local Moran’s statistic was used to identify spatial outliers. Additionally, we used Getis-Ord statistic to detect hotspots and coldspots over entire Iraq that represent clusters of districts with significantly high or low ASRs. A total of 44,496 cases were reported in 2000-2015 in Iraq, with an ASR of incidence of (32.81/100,000). The ASR incidence of breast cancer showed a significant average percentage change of 5.40% from 2002 to 2015. The spatial autocorrelation analysis showed insignificant positive spatial autocorrelation in 2000-2004, and significant positive spatial autocorrelation in 2005-2009, and in 2010-2015. This study identified four districts as high-risk areas for breast cancer during the two periods 2005-2009 and 2010-2015, including Al-Karkh, Al-Adhamia, Al-Rissafa, and Al-Sadir. This information can assist the allocation of health care resources and expand cancer prevention efforts.

Spatial analysis of COVID-19 for the state of Uttar Pradesh, India

The world is struggling to combat COVID-19 pandemic, which is caused by the SARS-CoV-2 virus. The pandemic has affected millions of people all across the globe since the first case has been reported in the Wuhan city of China in December 2019. India is the second most affected country in the world with more than 8.5 million confirmed cases (as of 10 November 2020) after USA. India is facing an unprecedented crisis due to the pandemic, leading to the Nation’s economy to a near standstill. The share of COVID-19 confirmed cases in six most affected States of India is approximately 60 percent. The analytical research tries to assess the impact of COVID-19 through spatial-statistical analysis for the state of Uttar Pradesh, which is one of the most affected states by COVID-19 in India. The detailed analysis has been carried out at district level. The impact of pandemic is more in regions (or districts), which are either having metropolis or airports along with high population density and growth rate during the last decade. Furthermore, inadequate number of health infrastructure facilities and low number of testing are some of the major factors making the situation worse in India. The spatial-statistical analysis enables to understand the pattern of spreading of disease by identifying the hot-spot areas, perceiving the trend of transmission of disease spatially, and understanding the extent of the pandemic over a period of time.

White matter microstructure characterisation in temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy. Parameters of microstructural abnormalities derived from diffusion tensor imaging(DTI) have been reported to be helpful in differentiating between Left and Right TLE (L-TLE and R-TLE) but few of them compared L-TLE and R-TLE with a voxelwise approach. In this study, a whole brain tract based spatial statistical analysis was performed on DTI, diffusion kurtosis and NODDI derived parameters of 88 subjects to identify specific white matter patterns of alteration in patient affected by L-TLE and R-TLE with respect to healthy controls. Our findings demonstrated the presence of specific patterns of white matter alterations, with L-TLE more widely affected both in cerebral and cerebellar regions. This result supports the need to consider patients separately, according to the side of their pathology.

Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.