Crime mapping and spatial analysis

Crime maps are becoming significant tools in crime and justice. Advances in the areas of information technology and Geographic Information Systems (GIS) have opened new opportunities for the use of digital mapping in crime control and prevention programs. Crime maps are also valuable for the study of the ecology and the locational aspects of crime. Maps enable areas of unusually high or low concentration of crime to be visually identified. Maps are however only pictorial representations of the results of more or less complex spatial data analyses. A hierarchical model dealing with crime analysis is proposed and applied to the regional analysis of crime in Tehran, the model helps to identify spatial concentration of crimes in specific area (area based method). In area-based methods, crime data are aggregated into geographical areas such as blocks, precincts, and for each area, the analyst computes a measure of crime value. Multicriteria evaluation concept has been used to assess the crime rate in various blocks a discrete (part) of Tehran city. In this part we used two methods for crime density assessment: • Crime assessment based on crime per block, • Crime assessment based on density of crime per population. After determination of hot spots based on two methods mentioned above spatial function is used to find suitable location to establish new police station or direct patrol to the hot spots to reduce of crime

Biological interactions of biocompatible and water-dispersed MoS2 nanosheets with bacteria and human cells

Two dimensional materials beyond graphene such as MoS2 and WS2 are novel and interesting class of materials whose unique physico-chemical properties can be exploited in applications ranging from leading edge nanoelectronics to the frontiers between biomedicine and biotechnology. To unravel the potential of TMD crystals in biomedicine, control over their production through green and scalable routes in biocompatible solvents is critically important. Furthermore, considering multiple applications of eco-friendly 2D dispersions and their potential impact onto live matter, their toxicity and antimicrobial activity still remain an open issue. Herein, we focus on the current demands of 2D TMDs and produce high-quality, few-layered and defect-free MoS2 nanosheets, exfoliated and dispersed in pure water, stabilized up to three weeks. Hence, we studied the impact of this material on human cells by investigating its interactions with three cell lines: two tumoral, MCF7 (breast cancer) and U937 (leukemia), and one normal, HaCaT (epithelium). We observed novel and intriguing results, exhibiting evident cytotoxic effect induced in the tumor cell lines, absent in the normal cells in the tested conditions. The antibacterial action of MoS2 nanosheets is then investigated against a very dangerous gram negative bacterium, such as two types of Salmonellas: ATCC 14028 and wild-type Salmonella typhimurium. Additionally, concentration and layer-dependent modulation of cytotoxic effect is found both on human cells and Salmonellas

Effects of Benzo(a)pyrene on the endometrial receptivity and embryo implantation in mice: An experimental study

Background: Benzo(a)pyrene (BaP) as an environmental pollutant is ubiquitous in the environment and it has destructive effects on human health. So far, various studies have demonstrated that BaP can cause adverse effects on the female reproductive system, but the existing information is limited about the effects of BaP on the endometrial receptivity and embryo implantation. Objective: The aim of this study was to investigate the effects of BaP on the endometrial receptivity and implantation in mice. Materials and Methods: In this experimental study, 40 pregnant BALB/c mice were divided into 5 groups (n = 8/each) as follows: experimental groups received the doses of 100 μg/kg, 200 μg/kg, and 500 μg/kg BaP dissolved in corn oil, the control group received normal saline and sham group received corn oil. Pregnant mice administered these solutions from Day 1 to Day 5 of gestation by gavage. On Day 6, the mice were sacrificed. Then their embryos were counted and the hormonal, histomorphological and molecular analyses were performed on themocusa of uterine tube. Results: The data revealed that BaP reduces estrogen and progesterone levels, decreases the number of implantation site, endometrium thickness, uterine lumen diameter, stromal cells and endometrial glands, and blood vessels in the endometrium. However, the expression of Activin receptor-like kinase 5 and E cadherin genes was not changed by BaP with different doses. Conclusion: The finding of this study showed that BaP can change estrogen and progesterone levels, and endometrial morphology leads to impairing the endometrial receptivity and decreasing the number of implantation site. Key words: Benzo(a)pyrene, Embryo implantation, Estrogen, Progesterone, ALK5, E-cadherin

A parallel-cascaded ensemble of machine learning models for crop type classification in Google Earth engine using multi-temporal sentinel-1/2 and landsat-8/9 remote sensing data

The accurate mapping of crop types is crucial for ensuring food security. Remote Sensing (RS) satellite data have emerged as a promising tool in this field, offering broad spatial coverage and high temporal frequency. However, there is still a growing need for accurate crop type classification methods using RS data due to the high intra- and inter-class variability of crops. In this vein, the current study proposed a novel Parallel-Cascaded ensemble structure (Pa-PCA-Ca) with seven target classes in Google Earth Engine (GEE). The Pa section consisted of five parallel branches, each generating Probability Maps (PMs) for different target classes using multi-temporal Sentinel-1/2 and Landsat-8/9 satellite images, along with Machine Learning (ML) models. The PMs exhibited high correlation within each target class, necessitating the use of the most relevant information to reduce the input dimensionality in the Ca part. Thereby, Principal Component Analysis (PCA) was employed to extract the top uncorrelated components. These components were then utilized in the Ca structure, and the final classification was performed using another ML model referred to as the Meta-model. The Pa-PCA-Ca model was evaluated using in-situ data collected from extensive field surveys in the northwest part of Iran. The results demonstrated the superior performance of the proposed structure, achieving an Overall Accuracy (OA) of 96.25% and a Kappa coefficient of 0.955. The incorporation of PCA led to an OA improvement of over 6%. Furthermore, the proposed model significantly outperformed conventional classification approaches, which simply stack RS data sources and feed them to a single ML model, resulting in a 10% increase in OA

Photogrammetric evaluation of space linear array imagery for medium scale topographic mapping

This thesis is concerned with the 2D and 3D mathematical modelling of satellite-based linear array stereo images and the implementation of this modelling in a general adjustment program for use in sophisticated analytically-based photogrammetric systems. The programs have also been used to evaluate the geometric potential of linear array images in different configurations for medium scale topographic mapping. In addition, an analysis of the information content that can be extracted for topographic mapping purposes has been undertaken. The main aspects covered within this thesis are: - 2D mathematical modelling of space linear array images; - 3D mathematical modelling of the geometry of cross-track and along-track stereo linear array images taken from spacebome platforms; - the algorithms developed for use in the general adjustment program which implements the 2D and 3D modelling; - geometric accuracy tests of space linear array images conducted over high-accuracy test fields in different environments; - evaluation of the geometric capability and information content of space linear array images for medium scale topographic mapping; This thesis concludes that the mathematical modelling of the geometry and the adjustment program developed during the research has the capability to handle the images acquired from all available types of space linear array imaging systems. Furthermore it has been developed to handle the image data from the forthcoming very high-resolution space imaging systems utilizing flexible pointing of their linear array sensors. It also concludes that cross-track and along-track stereo images such as those acquired by the SPOT and MOMS- 02 linear array sensors have the capability for map compilation in 1:50,000 scales and smaller, but only in conjunction with a comprehensive field completion survey to supplement the data acquired from the satellite imagery

Using pixel-based and object-based methods to classify urban hyperspectral features

Object-based image analysis methods have been developed recently. They have since become a very active research topic in the remote sensing community. This is mainly because the researchers have begun to study the spatial structures within the data. In contrast, pixel-based methods only use the spectral content of data. To evaluate the applicability of object-based image analysis methods for land-cover information extraction from hyperspectral data, a comprehensive comparative analysis was performed. In this study, six supervised classification methods were selected from pixel-based category, including the maximum likelihood (ML), fisher linear likelihood (FLL), support vector machine (SVM), binary encoding (BE), spectral angle mapper (SAM) and spectral information divergence (SID). The classifiers were conducted on several features extracted from original spectral bands in order to avoid the problem of the Hughes phenomenon, and obtain a sufficient number of training samples. Three supervised and four unsupervised feature extraction methods were used. Pixel based classification was conducted in the first step of the proposed algorithm. The effective feature number (EFN) was then obtained. Image objects were thereafter created using the fractal net evolution approach (FNEA), the segmentation method implemented in eCognition software. Several experiments have been carried out to find the best segmentation parameters. The classification accuracy of these objects was compared with the accuracy of the pixel-based methods. In these experiments, the Pavia University Campus hyperspectral dataset was used. This dataset was collected by the ROSIS sensor over an urban area in Italy. The results reveal that when using any combination of feature extraction and classification methods, the performance of object-based methods was better than pixel-based ones. Furthermore the statistical analysis of results shows that on average, there is almost an 8 percent improvement in classification accuracy when we use the object-based methods

Monitoring Long-Term Spatiotemporal Changes in Iran Surface Waters Using Landsat Imagery

Within water resources management, surface water area (SWA) variation plays a vital role in hydrological processes as well as in agriculture, environmental ecosystems, and ecological processes. The monitoring of long-term spatiotemporal SWA changes is even more critical within highly populated regions that have an arid or semi-arid climate, such as Iran. This paper examined variations in SWA in Iran from 1990 to 2021 using about 18,000 Landsat 5, 7, and 8 satellite images through the Google Earth Engine (GEE) cloud processing platform. To this end, the performance of twelve water mapping rules (WMRs) within remotely-sensed imagery was also evaluated. Our findings revealed that (1) methods which provide a higher separation (derived from transformed divergence (TD) and Jefferies–Matusita (JM) distances) between the two target classes (water and non-water) result in higher classification accuracy (overall accuracy (OA) and user accuracy (UA) of each class). (2) Near-infrared (NIR)-based WMRs are more accurate than short-wave infrared (SWIR)-based methods for arid regions. (3) The SWA in Iran has an overall downward trend (observed by linear regression (LR) and sequential Mann–Kendall (SQMK) tests). (4) Of the five major water basins, only the Persian Gulf Basin had an upward trend. (5) While temperature has trended upward, the precipitation and normalized difference vegetation index (NDVI), a measure of the country’s greenness, have experienced a downward trend. (6) Precipitation showed the highest correlation with changes in SWA (r = 0.69). (7) Long-term changes in SWA were highly correlated (r = 0.98) with variations in the JRC world water map

Spatially and Temporally Distinct Encoding of Muscle and Kinematic Information in Rostral and Caudal Primary Motor Cortex

The organizing principle of human motor cortex does not follow an anatomical body map, but rather a distributed representational structure in which motor primitives are combined to produce motor outputs. Electrophysiological recordings in primates and human imaging data suggest that M1 encodes kinematic features of movements, such as joint position and velocity. However, M1 exhibits well-documented sensory responses to cutaneous and proprioceptive stimuli, raising questions regarding the origins of kinematic motor representations: are they relevant in top-down motor control, or are they an epiphenomenon of bottom-up sensory feedback during movement? Here we provide evidence for spatially and temporally distinct encoding of kinematic and muscle information in human M1 during the production of a wide variety of naturalistic hand movements. Using a powerful combination of high-field functional magnetic resonance imaging and magnetoencephalography, a spatial and temporal multivariate representational similarity analysis revealed encoding of kinematic information in more caudal regions of M1, over 200 ms before movement onset. In contrast, patterns of muscle activity were encoded in more rostral motor regions much later after movements began. We provide compelling evidence that top-down control of dexterous movement engages kinematic representations in caudal regions of M1 prior to movement production