The application of data mining techniques to interrogate Western Australian water catchment data sets

Current environmental challenges such as increasing dry land salinity, waterlogging, eutrophication and high nutrient runoff in south western regions of Western Australia may have both cultural and environmental implications in the near future. Advances in computer science disciplines, more specifically, data mining techniques and geographic information services provide the means to be able to conduct longitudinal climate studies to predict changes in the Water catchment areas of Western Australia. The research proposes to utilise existing spatial data mining techniques in conjunction of modern open-source geospatial tools to interpret trends in Western Australian water catchment land use. This will be achieved through the development of a innovative data mining interrogation tool that measures and validates the effectiveness of data mining methods on a sample water catchment data set from the Peel Harvey region of WA. In doing so, the current and future statistical evaluation on potential dry land salinity trends can be eluded. The interrogation tool will incorporate different modern geospatial data mining techniques to discover meaningful and useful patterns specific to current agricultural problem domain of dry land salinity. Large GIS data sets of the water catchments on Peel-Harvey region have been collected by the state government Shared Land Information Platform in conjunction with the LandGate agency. The proposed tool will provide an interface for data analysis of water catchment data sets by benchmarking measures using the chosen data mining techniques, such as: classical statistical methods, cluster analysis and principal component analysis.The outcome of research will be to establish an innovative data mining instrument tool for interrogating salinity issues in water catchment in Western Australia, which provides a user friendly interface for use by government agencies, such as Department of Agriculture and Food of Western Australia researchers and other agricultural industry stakeholders

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Mining climate data for shire level wheat yield predictions in Western Australia

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production. Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop failures arise from adherence to these crop recommendations by farmers that were contrary to the reported estimations. Consequently, the Department has sought to investigate new avenues for analyses that improve their estimations and recommendations. This thesis explores a new approach in the way analyses are carried out. This is done through the introduction of new methods of analyses such as data mining and online analytical processing in the strategy. Additionally, this research attempts to provide a better understanding of the effects of both gradual variation parameters such as soil type, and continuous variation parameters such as rainfall and temperature, on the wheat yields. The ultimate aim of the research is to enhance the prediction efficiency of wheat yields. The task was formidable due to the complex and dichotomous mixture of gradual and continuous variability data that required successive information transformations. It necessitated the progressive moulding of the data into useful information, practical knowledge and effective industry practices. Ultimately, this new direction is to improve the crop predictions and to thereby reduce crop failures. The research journey involved data exploration, grappling with the complexity of Geographic Information System (GIS), discovering and learning data compatible software tools, and forging an effective processing method through an iterative cycle of action research experimentation. A series of trials was conducted to determine the combined effects of rainfall and temperature variations on wheat crop yields. These experiments specifically related to the South Western Agricultural region of Western Australia. The study focused on wheat producing shires within the study area. The investigations involved a combination of macro and micro analyses techniques for visual data mining and data mining classification techniques, respectively. The research activities revealed that wheat yield was most dependent upon rainfall and temperature. In addition, it showed that rainfall cyclically affected the temperature and soil type due to the moisture retention of crop growing locations. Results from the regression analyses, showed that the statistical prediction of wheat yields from historical data, may be enhanced by data mining techniques including classification. The main contribution to knowledge as a consequence of this research was the provision of an alternate and supplementary method of wheat crop prediction within the study area. Another contribution was the division of the study area into a GIS surface grid of 100 hectare cells upon which the interpolated data was projected. Furthermore, the proposed framework within this thesis offers other researchers, with similarly structured complex data, the benefits of a general processing pathway to enable them to navigate their own investigations through variegated analytical exploration spaces. In addition, it offers insights and suggestions for future directions in other contextual research explorations

Identification and monitoring of oil pipeline spill fire using space applications

Oil pipeline spills in the Niger Delta cause a great deal of environmental damage to sensitive ecosystems and losses of many millions of dollars to the Nigerian economy every year. These spills occur along the routes of pipeline infrastructure and other oil facilities like flowlines, trunk lines, flow stations, barges, well heads etc. The causes of these spill events include: operational or maintenance error, ageing oil facilities, as well as acts of deliberate sabotage of the pipeline equipment which often result in explosions and fire outbreaks. In this project, we have investigated whether satellite observations could be used to detect these oil pipeline fires. The Nigerian National Oil Spill Detection and Response Agency (NOSDRA) database contains a total of 10 072 oil spill reports from 2007 to 2015. The space-based approach we considered in this dissertation included the use of data gathered by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra and Aqua satellites, which recorded 85 129 active fire hotspots in the Niger Delta from 2007 to 2015. Since the oil spill reports serve as validation data for these oil spill fires, we explored the capability of the MODIS instrument to study the spatio-temporal correlation between spills and fire events by attempting to investigate whether the largest spills by volume that resulted in fires could be detected from space in near-real time. Although the NOSDRA oil spill reports are plagued with several irregularities from the Joint Investigation Visits by the joint task force who visit spill sites, our approach in this dissertation automated the filtering process of the raw database to meet our research goal and objective. This study confirms that, indeed, fires resulting from oil spills are detectable using the MODIS fire products. For 43 of the largest spill events, we were able to establish a spatio-temporal correlation of spill incident reports with MODIS fires clearly associated with the oil pipeline infrastructure. Our study also shed light on the spatial and temporal characteristics of non-pipeline fires in the study area

Land development in Massachusetts: Its effect on the environment within Essex and Middlesex counties from 1990 to 2007

Since the 1970\u27s urban centers in and surrounding Essex and Middlesex Counties in Massachusetts have expanded and proliferated into adjacent communities. This expansion has led to the conversion of land for housing, businesses, schools, recreation, and parks, placing significant strain on existing land cover, land use, and available natural resources. Mounting growth pressures and a reduction of undeveloped land have raised serious concerns as cropland and forest fragmentation, wetland destruction, protected open-space infringement, pollution, and systematic losses of rural conditions have become obvious. To monitor development, the post-classification change detection method was applied to Landsat Thematic Mapper (TM) satellite data and GIS was used to detect, quantity, and document the extent of development and its effect on the environment and to assess and quantify the demographic changes that occurred within the counties from 1990 to 2007. Classification of the 1990 image resulted in 217 clusters and 214 clusters for the 2007 image The overall accuracy achieved for the 1990 image classification was 87.3% with a KHAT value of 0.848, and the overall accuracy for the 2007 classification was 86.27% with a KHAT value of 0.840. From 1990 to 2007 land cover change occurred primarily along major transportation corridors. The post-classification change detection results indicate that Essex and Middlesex County combined gained 23,435.66 new acres of land development from 1990 to 2007 through a loss and change in acreage from the Bareland, Forest, Grassland, Water, and Wetland land cover class categories. Results indicate that there was an approximate 0.56% overall (net) increase of newly developed land areas within the 1990 and 2007 image classifications from 415.46 acres or 0.64 square miles. In addition, there was a substantial decrease (-40.0%) within the grassland category. Land development was responsible for a portion of the decrease of grasslands (-13.63%), which occurred mostly within Middlesex County. Results also indicate that new land development occurred within several Commonwealth of Massachusetts designated environmentally-sensitive areas: 722 acres in areas of critical environmental concern, 670 acres in priority habitats of rare species, 1,092 acres in living waters core habitats and critical supporting watersheds, 1,318 acres in protected and recreational open spaces, and within 0-1000 feet of 600 certified vernal pools. In addition, several rare or imperiled species inhabiting these areas may have been adversely affected by land development through habitat loss, change, or fragmentation, and/or passage corridor disruptions. A GIS comparison of the new land development acreages and census demographic statistics within Essex and Middlesex County cities and towns during this period indicate that communities with more families with children exhibited more land development, and communities with higher median household income exhibited less land development. Land change detection over the 17-year period indicated encroachment of development in areas of environmental concern, but level of development varied by socio-demographic factors. This study also illustrated that the combined use of remotely sensed data, Geographic Information Systems (GIS) technology, and demographic data are effective for use as a diagnostic tool and/or base to be built upon to explore associations, indicators, or drivers which may influence land cover change and its effects on existing environmental conditions in areas exhibiting change. In addition, this study provided awareness to ancillary research where scientific guidelines were derived for the protection of specific wildlife habitats and resident species. Lastly, this study presented several land cover modeling and web deployed data dissemination tools for the dissertation results as well as provided a conceptual framework for the successful adoption and implementation of these tools for organizations engaged in natural resource planning and management

Undergraduate Research Journal, Volume 15

Table of Contents: The Effects of Snorkel-Based Tourism on the Behavior of Reef Fishes / by Savannah Clapp, Lauren Rowsey, and Jordan Grant (p.1-18) -- People Broken Into Pieces Trying to Join: Byzantine Erotica and the Provocative Paradox / by Kendall DeBoer (p.19-28) -- The Legacy of 1830 Land Cover on Present-Day Massachusetts Forest Composition / by Sofie McComb (p.29-58) -- The Role of Labels in Making Inferences about Group Categorization / by Subhashini Madhavan (p.59-76) -- Transcending Autobiography: Simultaneity and Meaning in Elisabet Ney’s Lady Macbeth / by Kendall DeBoer (p.77-92) -- Comparison of Microbial Diversity in Local Wasps and Plant Surfaces / by Dylan Fall and Jo-anne Holley (p.93-104) -- Ambiguity in Biotechnology Patent Policy: Lessons from Association for Molecular Pathology v. Myriad Genetics, Inc. / by Chari Noddings (p.105-126) -- About the uncountability of the number of irrational powers of irrational numbers evaluated as rationals and solutions’ estimation for xx=y and xx =y / by Anca Andrei (p.127-133)Senate of College Council

Development and evaluation of low cost 2-d lidar based traffic data collection methods

Traffic data collection is one of the essential components of a transportation planning exercise. Granular traffic data such as volume count, vehicle classification, speed measurement, and occupancy, allows managing transportation systems more effectively. For effective traffic operation and management, authorities require deploying many sensors across the network. Moreover, the ascending efforts to achieve smart transportation aspects put immense pressure on planning authorities to deploy more sensors to cover an extensive network. This research focuses on the development and evaluation of inexpensive data collection methodology by using two-dimensional (2-D) Light Detection and Ranging (LiDAR) technology. LiDAR is adopted since it is economical and easily accessible technology. Moreover, its 360-degree visibility and accurate distance information make it more reliable. To collect traffic count data, the proposed method integrates a Continuous Wavelet Transform (CWT), and Support Vector Machine (SVM) into a single framework. Proof-of-Concept (POC) test is conducted in three different places in Newark, New Jersey to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances, resulting in 83% ~ 94% accuracy. It is discovered that the proposed method\u27s accuracy is affected by the color of the exterior surface of a vehicle since some colored surfaces do not produce enough reflective rays. It is noticed that the blue and black colors are less reflective, while white-colored surfaces produce high reflective rays. A methodology is proposed that comprises K-means clustering, inverse sensor model, and Kalman filter to obtain trajectories of the vehicles at the intersections. The primary purpose of vehicle detection and tracking is to obtain the turning movement counts at an intersection. A K-means clustering is an unsupervised machine learning technique that clusters the data into different groups by analyzing the smallest mean of a data point from the centroid. The ultimate objective of applying K-mean clustering is to identify the difference between pedestrians and vehicles. An inverse sensor model is a state model of occupancy grid mapping that localizes the detected vehicles on the grid map. A constant velocity model based Kalman filter is defined to track the trajectory of the vehicles. The data are collected from two intersections located in Newark, New Jersey, to study the accuracy of the proposed method. The results show that the proposed method has an average accuracy of 83.75%. Furthermore, the obtained R-squared value for localization of the vehicles on the grid map is ranging between 0.87 to 0.89. Furthermore, a primary cost comparison is made to study the cost efficiency of the developed methodology. The cost comparison shows that the proposed methodology based on 2-D LiDAR technology can achieve acceptable accuracy at a low price and be considered a smart city concept to conduct extensive scale data collection

AXMEDIS 2008

The AXMEDIS International Conference series aims to explore all subjects and topics related to cross-media and digital-media content production, processing, management, standards, representation, sharing, protection and rights management, to address the latest developments and future trends of the technologies and their applications, impacts and exploitation. The AXMEDIS events offer venues for exchanging concepts, requirements, prototypes, research ideas, and findings which could contribute to academic research and also benefit business and industrial communities. In the Internet as well as in the digital era, cross-media production and distribution represent key developments and innovations that are fostered by emergent technologies to ensure better value for money while optimising productivity and market coverage

Use of a weighted matching algorithm to sequence clusters in spatial join processing

One of the most expensive operations in a spatial database is spatial join processing. This study focuses on how to improve the performance of such processing. The main objective is to reduce the Input/Output (I/O) cost of the spatial join process by using a technique called cluster-scheduling. Generally, the spatial join is processed in two steps, namely filtering and refinement. The cluster-scheduling technique is performed after the filtering step and before the refinement step and is part of the housekeeping phase. The key point of this technique is to realise order wherein two consecutive clusters in the sequence have maximal overlapping objects. However, finding the maximal overlapping order has been shown to be Nondeterministic Polynomial-time (NP)-complete. This study proposes an algorithm to provide approximate maximal overlapping (AMO) order in a Cluster Overlapping (CO) graph. The study proposes the use of an efficient maximum weighted matching algorithm to solve the problem of finding AMO order. As a result, the I/O cost in spatial join processing can be minimised