Spatio-temporal monitoring of cotton cultivation using ground-based and airborne multispectral sensors in GIS environment

2017 ◽  
Vol 189 (7) ◽  
Author(s):  
Antonis Papadopoulos ◽  
Dionissios Kalivas ◽  
Sid Theocharopoulos
Keyword(s):  
Spatio Temporal ◽  
Gis Environment ◽  
Multispectral Sensors

scholarly journals Spatio-temporal Analysis of Maximum and Minimum Temperature in Bangladesh

2015 ◽  
Vol 7 (2) ◽  
pp. 73-77 ◽  
Author(s):  
MN Uddin ◽  
MSA Mondal ◽  
NMR Nasher
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Statistical Significance ◽  
Temporal Analysis ◽  
Temperature Data ◽  
Spatially Distributed ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Maximum Temperatures ◽  
Kendall Method

The analysis of annual mean maximum and annual mean minimum temperature data are studied in GIS environment, obtained from 34 meteorological stations scattered throughout the Bangladesh from 1948 to 2013. IDW method was used for the spatial distribution of temperature over the study area, using ArcGIS 10.2 software. Possible trends in the spatially distributed temperature data were examined, using the non-parametric Mann-Kendall method with statistical significance, and the magnitudes of available trends were determined using Sen’s method in ArcMap depiction. The findings of the study show positive trends in annual mean maximum temperatures with 90%, 95%, 99% and 99.9% significance levels.DOI: http://dx.doi.org/10.3329/jesnr.v7i2.22210 J. Environ. Sci. & Natural Resources, 7(2): 73-77 2014

Visualization of spatio temporal GPS uncertainty within a GIS environment

2002 ◽  
Author(s):  
Suresh K. Lodha ◽  
Amin P. Charaniya ◽  
Nikolai Faaland ◽  
Srikumar Ramalingam
Keyword(s):  
Spatio Temporal ◽  
Gis Environment

scholarly journals Spatio-Temporal Analysis of Urban Crime Pattern and its Implication for Abuja Municipal Area Council, Nigeria

2017 ◽  
Vol 49 (2) ◽  
pp. 145
Author(s):  
Taiye Oluwafemi Adewuyi ◽  
Patrick Ali Eneji ◽  
Anthonia Silas Baduku ◽  
Emmanuel Ajayi Olofin
Keyword(s):  
Information System ◽  
Spatial Data ◽  
Temporal Analysis ◽  
Urban Crime ◽  
High Rate ◽  
Geographical Information ◽  
Positioning Systems ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Crime Pattern

This study examined the spatio-temporal analysis of urban crime pattern and its implication for Abuja Municipal Area Council of the Federal Capital Territory of Nigeria; it has the aim of using Geographical Information System to improve criminal justice system. The aim was achieved by establishing crime incident spots, types of crime committed, the time it occurred and factors responsible for prevailing crime. The methods for data collection involved Geoinformatics through the use of remote sensing and Global Positioning Systems (GPS) for spatial data. Questionnaires were administered for other attribute information required. The analysis carried out in a Geographic Information System (GIS) environment especially for mapping and the establishment of spatial patterns.  The results indicated that the main types of crime committed were theft and house breaking (42.9%), followed by assault (12.4%), mischief (11.3%), forgery (10.5%), car snatching (9.05%), armed robbery (8.5%), trespass (5.2%) and culpable homicide (0.2%). In terms of hot spots the districts recorded the following: Garki (27.62%), Maitama (25.7%), Utako (24.3%), Wuse (20.9%) and Asokoro district (1.4%) respectively with most of the crime committed during the day time. Many attributed the crimes to mainly high rate of unemployment and poverty (79.1%). Consequently to reduce the crime rate, the socio-economic situation of the city must be improved through properly constructed interventions scheme in areas known to quickly generate employment such as agriculture, small and medium scale enterprises, mining and tourism. 

scholarly journals Crop-management factor calculation using weights of spatio-temporal distribution of rainfall erosivity

2018 ◽  
Vol 13 (No. 3) ◽  
pp. 150-160
Author(s):  
Brychta Jiří ◽  
Janeček Miloslav ◽  
Walmsley Alena
Keyword(s):  
Temporal Distribution ◽  
Geographic Location ◽  
Crop Management ◽  
Rainfall Erosivity ◽  
Spatial And Temporal Variability ◽  
R Factor ◽  
Factor Evaluation ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Crop Management Factor

Inappropriate integration of USLE or RUSLE equations with GIS tools and Remote Sensing (RS) data caused many simplifications and distortions of their original principles. Many methods of C and R factor estimation were developed due to the lack of optimal data for calculations according to original methodology. This paper focuses on crop-management factor evaluation (C) weighted by fully distributed form of rainfall erosivity factor (R) distribution throughout the year. We used high resolution (1-min) data from 31 ombrographic stations (OS) in the Czech Republic (CR) for monthly R map creation. All steps of the relatively time-consuming C calculation were automated in GIS environment with an innovative procedure of R factor weight determination for each agro-technical phase by land parcel geographic location. Very high spatial and temporal variability of rainfall erosivity within each month and throughout the year can be observed from our results. This highlights the importance of C factor calculation using a correctly presented method with emphasis on the geographic location of given land parcels.

GIS and Crime Analysis

Geography ◽  
2021 ◽  
Keyword(s):  
Crime Prevention ◽  
Crime Analysis ◽  
Government Agencies ◽  
Contributing Factors ◽  
Private Companies ◽  
Crime Patterns ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Basic And Applied Research ◽  
Spatial Analysis Of Crime

Spatial analysis of crime has gained increasing attention during the past thirty years, coupled with the growth of geographic information systems (GIS). Most crime analysis tasks are either carried out in a GIS environment or supported by a GIS. GIS is typically used as a tool for data management, data processing, data visualization, and data analysis for crime studies. Crime analysis normally involves the following elements: uncovering spatio-temporal patterns of crime distribution, such as crime hotspots; explaining these patterns and discerning major contributing factors based on multivariate regression modeling; predicting future crime patterns using machine learning and other predictive methods; developing crime prevention approaches based on historical and future crime patterns; and evaluating the effectiveness of crime prevention, to find out if crime is reduced in the targeted area and whether the nearby areas are affected by the intervention. It should be noted that crime analysis is inherently multidisciplinary, including but not limited to geography, criminology, computer science, statistics, urban planning, and sociology. Therefore, an effective crime analyst should be well trained in multiple disciplinary approaches. Any crime analysis that leads to real-world impact must rely on sound theories and effective methodologies. Many of the theories covered in this article are related to geography, criminology, and sociology. The methods are mostly influenced by GIS, spatial statistics, and artificial intelligence. Crime analysis also involves multiple stakeholders, including at least government agencies, universities, and private companies. Universities conduct basic and applied research, private companies convert the research to products, and government agencies provide funding for research and implement crime prevention strategies. In addition, crime analysis needs to pay close attention to potential issues related to ethics, privacy, confidentiality, and discrimination.

scholarly journals Spatio-Temporal Distribution of Landslides in Java and the Triggering Factors

2017 ◽  
Vol 31 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Danang Sri Hadmoko ◽  
Franck Lavigne ◽  
Junun Sartohadi ◽  
Christopher Gomez ◽  
D Daryono
Keyword(s):  
Temporal Distribution ◽  
Rainfall Threshold ◽  
Short Term ◽  
Rainfall Events ◽  
Landslide Occurrence ◽  
Central Java ◽  
Causative Factors ◽  
Spatio Temporal ◽  
Gis Environment

Java Island, the most populated island of Indonesia, is prone to landslide disasters. Their occurrence and impact have increased mainly as the result of natural factors, aggravated by human imprint. This paper is intended to analyse: (1) the spatio-temporal variation of landslides in Java during short term and long-term periods, and (2) their causative factors such as rainfall, topography, geology, earthquakes, and land-use. The evaluation spatially and temporally of historical landslides and consequences were based on the landslide database covering the period of 1981 – 2007 in the GIS environment. Database showed that landslides distributed unevenly between West Java (67 %), Central Java (29 %) and East Java (4 %). Slope failures were most abundant on the very intensively weathered zone of old volcanic materials on slope angles of 30O – 40O. Rainfall threshold analysis showed that shallow landslides and deep-seated landslides were triggered by rainfall events of 300 – 600 mm and > 600 mm respectively of antecedent rainfall during 30 consecutive days, and many cases showed that the landslides were not always initiated by intense rainfall during the landslide day. Human interference plays an important role in landslide occurrence through land conversion from natural forest to dryland agriculture which was the host of most of landslides in Java. These results and methods can be used as valuable information on the spatio-temporal characteristics of landslides in Java and their relationship with causative factors, thereby providing a sound basis for landslide investigation in more detail.

scholarly journals Spatial Variation Study of Heavy Metal in Ground Water of Southern Part of Upper Berach River

2021 ◽  
Vol 10 (9) ◽  
pp. 357-367
Author(s):  
Dheeraj Kumar P. K. Singh ◽  
Jitandar Kumar K. K. Yadav ◽  
B. L. Tailor
Keyword(s):  
Heavy Metals ◽  
Heavy Metal ◽  
Ground Water ◽  
Temporal Variations ◽  
Metal Analysis ◽  
Heavy Metal Analysis ◽  
Acceptable Limit ◽  
Spatio Temporal ◽  
Gis Environment ◽  
Drinking Purpose

The present study was conducted to study heavy metal pollution in ground water of southern part of Upper Berach river. The water samples for the heavy metal analysis were collected from the open well of the 38 locations from Upper Berach river to find out concentration of different heavy metal such as Iron (Fe), Manganese (Mn), Copper (Cu), Zinc (Zn), Cadmium (Cd), Lead (Pb) and Nickel (Ni). The different heavy metals thematic maps of study area were prepared under GIS environment and the spatio-temporal variations of these parameters were analyzed. Results of the study indicates that about 57.94 per cent area shows Fe within BIS acceptable limit (< 0.3 mg/lit) and 42.06 per cent area show exceed Fe above acceptable limit of drinking purpose. About 42.03 per cent area shows Mn within BIS acceptable limit (< 0.1 mg/lit) and 57.97 per cent area shows Mn above permissible limit of drinking purpose.

scholarly journals CHANGE PATTERN EXPLORATION WITH HIERARCHICAL BI-CLUSTERING ON SENTINEL-1 SAR AND NIGHTTIME LIGHT DATA

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 13-18
Author(s):  
M. Che ◽  
P. Gamba
Keyword(s):  
Remote Sensing ◽  
Hierarchical Clustering ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Change Pattern ◽  
Novel Approach ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Spatio Temporal ◽  
Multispectral Sensors

Abstract. In the last few decades, urbanization activities have promoted the emergence of megacities, megalopolis, urban clusters or large urban aggregations, but only a few studies have analyzed them using remote sensing data in both the spatial and the temporal domains. In this paper, combining SAR and multispectral sensors with different resolutions, a novel approach, improved by means of a hierarchical clustering technique, is proposed. Urban changes are mapped in the form of multiple spatio-temporal patterns, visualized by change vectors exploiting the combination of SAR and nighttime light data.

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