The Johannesburg Metropolitan Police Department (JMPD) is one of the South African institutions with growing levels of corruption. This is despite the presence of several anti-corruption strategies as well as institutions implemented to deal with this epidemic. The ineffectiveness of these strategies and institutions is attributed to, amongst others, the application of ineffective detection methods. The under-detection of corruption in the JMPD is affecting road safety, the economy, and the image of the JMPD itself. It is against this background that the researcher sought to establish how intelligence analysis could add value in detecting corruption in the JMPD. A qualitative research approach was applied to investigate this problem. As such, it has been proven that the use of hotlines and the over-reliance on whistleblowers is less effective in detecting corruption in the JMPD. The results have further shown that intelligence analysis may add value in detecting corruption in the JMPD. / Umnyango wamaphoyisa edolobha elikhulu eGoli (JMPD) ungenye yezikhungo zaseNingizimu Afrika ezithola amazinga akhulayo enkohlakalo. Lokhu kungakhathaliseki ukuthi kukhona amasu amaningi okulwa nenkohlakalo kanye nezikhungo ezisetshenziselwa ukubhekana nesifo sobunkohlakalo. Ukungafezeki kwalawa masu kanye nezikhungo kubhekwa ukuthi, phakathi kwabanye, ukusetshenziswa kwezindlela ezingafenele zokuthola. Ukutholakala kwezinkohlakalo ku-JMPD kuthinta ukuphepha komgwaqo, umnotho nomfanekiso we-JMPD uqobo. Kungokumelene nalesi sizinda ukuthi umcwaningi lufuna ukusungula ukuthi ukuhlaziywa ngobuhlakani kungasiza kanjani ekutholeni inkohlakalo ku-JMPD. Indlela yokucwaninga efanele yasetshenziswa ukuphenya le nkinga. Njengalokhu, kuye kwafakazelwa ukuthi ukusetshenziswa kwama-hotlines nokuxhaswa ngokweqile kwababika ngenkohlakalo akwanele ukuthola inkohlakalo ku-JMPD. Imiphumela ibonise ukuthi ukuhlaziywa kobuhlakani kungasiza ekutholeni inkohlakalo ku-JMPD. / Lefapha la Sephodisa la Teropokgolo ya Johannesburg (JMPD) ke nngwe ya ditheo tsa Aforika Borwa tse di itemogelang maemo a a golang a bobodu. Seno ke kwa ntle ga go nna teng ga ditogamaano tse dingwe tse di kgatlhanong le bobodu ga mmogo le ditheo tse di diragadiwang go samagana le leroborobo la bobodu. Go se nne le nonofo ga ditogamaano tseno le ditheo e amana, mo go tse dingwe, le tiragatso ya mekgwa ya go lemoga go se nne le nonofo. Go nna kwa tlase ga go lemoga bobodu mo go JMPD go ama pabalesego ya tsela, ikonomi le setshwantsho sa JMPD ka boyona. Ke mabapi le lemorago leno gore mmatlisisi o ne a batla go tlhomamisa gore tshetshereganyo ya tsa matlhale e ka thusa mo go lemogeng bobodu mo go JMPD. Boitlhagiso jwa patlisiso ya boleng bo ne jwa diragadiwa go tlhotlhomisa bothata jono. Fa go le jalo, go ne ga netefatswa gore tiriso ya megala ya tlhamalalo e e kgethegileng ya tshoganyetso le go ikaega thata ka batho ba ba tlaleyang ga go a lekana go lemoga bobodu mo go JMPD. Dipholo di bontshitse gape gore tshekatsheko ya tsa matlhale e ka thusa go lemoga bobodu mo go JMPD. / Criminology and Security Science / M.A. (Criminal Justice)

Indiana University-Purdue University Indianapolis (IUPUI) / Identifying spatial and temporal patterns of disease occurrence by mapping the residential locations of affected people can provide information that informs response by public health practitioners and improves understanding in epidemiological research. A common method of locating patients at the individual level is geocoding residential addresses stored in electronic medical records (EMRs) using address matching procedures in a geographic information system (GIS). While the process of geocoding is becoming more common in public health studies, few researchers take the time to examine the effects of using different address databases on match rate and positional accuracy of the geocoded results. This research examined and compared accuracy and match rate resulting from four commonly-used geocoding databases applied to sample of 59,341 subjects residing in and around Marion County/ Indianapolis, IN. The results are intended to inform researchers on the benefits and downsides to their selection of a database to geocode patient addresses in EMRs.

Indiana University-Purdue University Indianapolis (IUPUI) / Environmental planning is very cumbersome work for environmentalists, government agencies like USDA and NRCS, and farmers. There are a number of conflicts and issues involved in such a decision making process. This research is based on the work to provide a common platform for environmental planning called WRESTORE (Watershed Restoration using Spatio-Temporal Optimization of Resources). We have designed a system that can be used to provide the best management practices for environmental planning. A distributed system was designed to combine high performance computing power of clusters/supercomputers in running various environmental model simulations. The system is designed to be a multi-user system just like a multi-user operating system. A number of stakeholders can log-on and run environmental model simulations simultaneously, seamlessly collaborate, and make collective judgments by visualizing their landscapes. In the research, we identified challenges in running such a system and proposed various solutions. One challenge was the lack of fast optimization algorithm. In our research, several algorithms are utilized such as Genetic Algorithm (GA) and Learning Automaton (LA). However, the criticism is that LA has a slow rate of convergence and that both LA and GA have the problem of getting stuck in local optima. We tried to solve the multi-objective problems using LA in batch mode to make the learning faster and accurate. The problems where the evaluation of the fitness functions for optimization is a bottleneck, like running environmental model simulation, evaluation of a number of such models in parallel can give considerable speed-up. In the multi-objective LA, different weight pair solutions were evaluated independently. We created their parallel versions to make them practically faster in computation. Additionally, we extended the parallelism concept with the batch mode learning. Another challenge we faced was in User Modeling. There are a number of User Modeling techniques available. Selection of the best user modeling technique is a hard problem. In this research, we modeled user's preferences and search criteria using an ANN (Artificial Neural Network). Training an ANN with limited data is not always feasible. There are many situations where a simple modeling technique works better if the learning data set is small. We formulated ways to fine tune the ANN in case of limited data and also introduced the concept of Deep Learning in User Modeling for environmental planning system.