Forecasting Hotspots—A Predictive Analytics Approach

Maciejewski, Ross; Hafen, Ryan; Rudolph, Stephen; Larew, Stephen G.; Mitchell, Margaret; Cleveland, William S.; Ebert, David S.

doi:10.1109/tvcg.2010.82

Cited by 73 publications

(37 citation statements)

References 35 publications

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“…Data visualization is paramount to enhance the understanding of data. Maciejewski et al [9] have provided visual analytics systems to users to explore trends in their data. Linked views and interactive displays provide insight into correlations among people, events, and places in space and time.…”

Section: Related Workmentioning

confidence: 99%

Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

SenthilarumugamVeilukandammal¹,

Nilakanta²,

Ganapathysubramanian³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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Section: Related Workmentioning

confidence: 99%

Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

SenthilarumugamVeilukandammal¹,

Nilakanta²,

Ganapathysubramanian³

et al. 2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…Maciejewski et al, [9], demonstrated the benefits of predictive visual analytics for forecasting syndromic hotspots. By linking a variety of data sources and models, they are able to enhance the hypothesis generation and exploration abilities of state epidemiologists.…”

Section: Litreature Reviewmentioning

confidence: 99%

Predictive Analytic Modeling: A Walkthrough

Shah¹,

Butt

Baba

2017

IJARCSSE

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Abstract-Predictive analytic modeling is the key step involved in building a successful Data Mining solution. Predictive analytic modeling is a set of iterative activities in which a predictive model is built from the observed data set. Predictive analytics is the area of data mining concerned with the prediction of future probabilities and trends using archive data. It brings together management, information technology and modeling that eases researchers in making suitable predictions. Many methodologies have been proposed for model building, and these are based on some basic industrial engineering frameworks. The proposed research work discusses CRISP, DMAIC and SEMMA methodologies which provide the building blocks for an efficient predictive model. Keywords-Predictive Analytics, Data Mining, Modeling, CRISP, DMAIC and SEMMA. I. INTRODUCTIONToday's demanding atmosphere is forcing more organizations to explore and adopt predictive analytics. Data being the indispensible assets of an organization is increasing exponentially and so does the data storage need. Selling large amount of data has little value, but if one can add insight via analytics to data then we have the opportunity to monetize it. There is a sea change in the corporate world, researchers and IT organizations can seize upon the opportunity to reinvent themselves and transform from a support function into a profit center [1]. "We are sitting on a mountain of gold but we're not mining it as effectively as we could," says Michael Masciandaro, director of business intelligence at Rohm & Haas, a global specialty materials manufacturer. Data Mining is the technique that retrieves useful information from large amounts of data. Data mining is defined as a process involving the extraction of useful and interesting information from the underlying data [2]. Modeling is the main part of Predictive Analytics. Predictive Analytics modeling is used in representation of real world situations for rendering or description of reality. Limited, imprecise, but useful, a model helps us to make sense of the world [3]. The result of the models may give important information that can be used in decision making and future prediction. Predictive Analytic Modeling helps in optimizing existing processes, better understanding and predicting customer behavior, identify unexpected opportunities and recessions, and predict problems before they happen. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions [4]. Predictive analytic modeling involves searching for meaningful relationships among variables and representing those relationships in models. These variables are called predictors and constitute the core element of predictive analytics. A predictor could be measured for an indi...

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“…A summary of some other methods that involve geospatial modeling can be found in [11,12]. Maciejewski et al [20] utilize the seasonal trend decomposition by loess smoothing for generating temporal predictions for modeling spatiotemporal healthcare events. They also use the kernel density estimation technique for creating probability distributions of patient locations for use in healthcare data.…”

Section: Predictive Visual Analyticsmentioning

confidence: 99%

“…To predict using the STL method, we apply the methodology described in [20], where the fitted valuesŶ = (ŷ 1 , ...,ŷ n ) generated using the loess operator in the STL decomposition step are considered to be a linear transformation of the input time series Y = (y 1 , ..., y n ). This is given byŷ i = ∑ n i=1 h i j y j ⇒Ŷ = HY , where H is the operator matrix whose (i, j)-th diagonal elements are given by h i, j .…”

Section: Time Series Prediction Using Seasonal-trend De-composition Bmentioning

confidence: 99%

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Malik

Maciejewski

Towers

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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Abstract-In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

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Forecasting Hotspots—A Predictive Analytics Approach

Cited by 73 publications

References 35 publications

Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

Big Data and Parkinson’s Disease: Exploration, Analyses, and Data Challenges.

Predictive Analytic Modeling: A Walkthrough

Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

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