Window Query and Analysis on Massive Spatio-temporal Data

A notable recent trend in time-varying volumetric data analysis and visualization is to extract data relationships and represent them in a low-dimensional abstract graph view for visual understanding and making connections to the underlying data. Nevertheless, the ever-growing size and complexity of data demands novel techniques that go beyond standard brushing and linking to allow significant reduction of cognition overhead and interaction cost. In this paper, we present a mining approach that automatically extracts meaningful features from a graph-based representation for exploring time-varying volumetric data. This is achieved through the utilization of a series of graph analysis techniques including graph simplification, community detection, and visual recommendation. We investigate the most important transition relationships for time-varying data and evaluate our solution with several time-varying data sets of different sizes and characteristics. For gaining insights from the data, we show that our solution is more efficient and effective than simply asking users to extract relationships via standard interaction techniques, especially when the data set is large and the relationships are complex. We also collect expert feedback to confirm the usefulness of our approach.

Analyzing, visualizing, and illustrating changes within time-varying volumetric data is challenging due to the dynamic changes occurring between timesteps. The changes and variations in computational fluid dynamic volumes and atmospheric 3D datasets do not follow any particular transformation. Features within the data move at different speeds and directions making the tracking and visualization of these features a difficult task. We introduce a texture-based feature tracking technique to overcome some of the current limitations found in the illustration and visualization of dynamic changes within time-varying volumetric data. Our texture-based technique tracks various features individually and then uses the tracked objects to better visualize structural changes. We show the effectiveness of our texture-based tracking technique with both synthetic and real world time-varying data. Furthermore, we highlight the specific visualization, annotation, registration, and feature isolation benefits of our technique. For instance, we show how our texture-based tracking can lead to insightful visualizations of time-varying data. Such visualizations, more than traditional visualization techniques, can assist domain scientists to explore and understand dynamic changes.

Climate change is a pervasive global issue that threatens the livelihoods and wellbeing of billions living globally. Climate change is a risk multiplier impacting all ecosystems, society, and sectors of the economy. The agriculture sector is one such sector that is highly vulnerable to changes in climate. In a country like Nepal where rainfed agriculture is a dominant occupation and a key pillar of the country’s economy, climate change brings risks and negative consequences for on-farm production, farmers' livelihoods as well as on the country’s development. The impacts of climate change including rising temperatures, an increase in the frequency or intensity of extreme weather events such as drought, and shifts in the rainfall seasonality, can cause a decline in food production and threaten the quality of food supplies, leading to reduced food security. The rise in the global population will increase global demand for food which implies that agriculture needs to boost production and increase yields, among other things. The unprecedented risks posed by climate change potentially undermine the ability of farms and farm holders to grow adequate and quality food. The severity of these risks varies due to a range of underlying factors including low economic development, their location, existing biophysical and socioeconomic conditions, and institutional arrangements. While the impacts of climate change on food production as well as agricultural practices in Nepal have been documented, there is a dearth in scholarly literature that has assessed the impacts of climate change on household food security in Nepal incorporating farmers’ perspectives and in particular smallholder subsistence farmers. Furthermore, there is only modest literature that has examined geographical variations in those experiences and understandings. This PhD study aimed, therefore, to investigate the effects of climate change on agricultural practices and food security, with a focus on subsistence smallholder farmers in three main agro- ecological zones of Nepal known as The Mountains, Hills, and the Terai. The study aimed to respond to the primary research question: How are Nepalese farming communities being impacted by climate change and how are they responding to ensure their continued food security? To answer this main research, question the study posed the following secondary research questions: Q1. How is climate changing and how is it impacting subsistence agriculture? Q2. What are the farmers experiencing and what is their understanding? a. Are there gender differences in understanding and experiencing? Q3. What is the state of food insecurity among these farmers? a. How is it being impacted by climate change? Q4. What adaptation strategies have been adopted by smallholder farmers to address threats to agricultural practices and food security from climate change and other pressures? Both the qualitative and qualitative data were collected using multiple methods to address the identified research questions. Methods included a narrative literature review, systematic review, face to face interviews with farmers, individual interviews with key informants and focus groups with the women's group. Climate data on temperature between 1971-2013 and rainfall between 1967-2013 were analysed. Additionally, secondary data on crop yield from 1980 to 2016 were also analysed to gain a better empirical understanding of the relations between climate change and yield pattern and to triangulate and validate the findings from the interviews. Quantitative data on cereal crop yields and climate data were systematically tabulated and further statistically analysed using software R. This study employed the Bayesian approach to statistical modelling. Besides, this study undertook an integrated risk assessment of food insecurity using the Bayesian Belief Network model to reflect how the risk of food insecurity is influenced under two scenarios: (1) current climate conditions and (2) the influences of different adaptation strategies employed. NVivo was used for content analysis for qualitative data obtained from the key informant interview and focus group data and analysis of transcripts from farmers' structured interviews. The findings showed that agricultural practices have undergone various changes over the past 30 years. Climate change impacts were experienced by farmers in all three agro-ecological zones of Nepal. However, the impacts varied between these zones in terms of frequency and intensity. The effect of climate change was highly pronounced in the Mountains zone followed by the Hills and the Terai. The results confirmed that rural subsistence smallholder farmers dependent on rainfed agriculture are vulnerable to climate change impacts. Moreover, it disproportionately affects the poor farmers whose income hinges solely on agriculture and associated activities. Boosting agricultural production and empowering these smallholders is key to enhancing their food security. The experiences reported by farmers are well aligned with the trend of the climatic variables obtained from climate data analysis, highlighting the importance of perception-based survey in complementing climate research. The study demonstrated both the climatic and non-climatic factors are affecting agricultural practices as well as household food security of these farming communities. It is, therefore, difficult to isolate the influence of any of these factors. This was supported by the findings from the risk assessment undertaken by Bayesian modelling. Based on Bayesian modelling, the smallholder farmers mainly at the Mountain zone were at the risk of food insecurity. The measures to increase the adaptive capacity of these smallholders were found to help them manage the risk of food insecurity. Addressing the complicated and multifaceted concerns of climate change and food security needs multidisciplinary and multisectoral adaptation interventions acknowledging underlying biophysical, social, economic, geographical and environmental circumstances. Farmers have taken some actions to adapt and reduce the worsening impacts of climate change. Nevertheless, farmers encountered several barriers in effectively adapting to climate change. This study concluded there is an urgent need for a transformative level of intervention that warrants a coordinated action and collaboration between relevant stakeholders working in this field, including governments and non-governmental organizations, to target the most vulnerable and the needy smallholder farmers addressing the constraints and pressures they face. Policy and decision-makers should work extensively and sensitively with smallholders to ensure the maintenance of their livelihood and to guarantee their food security. Combining local and scientific knowledge is needed to help direct research and tailor adaptation solutions that meet local conditions and needs.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

The paper analyses the impacts of climate change and its impact on food security in the Kalapara Upazila of Patuakhali district in Bangladesh. The paper shows current climate change scenarios for Kalapara, analyses the relationship between climate change scenarios and agricultural production and then prescribe adaptive measures to climate change. Historical yearly temperature and rainfall data from Bangladesh Meteorological Department (BMD) and crop yield data from the census of agriculture zilla series, Patuakhali, year book of agricultural statistics and agricultural ministry of Bangladesh were used. A questionnaire survey was also conducted to gather farmer perceptions on climate change in addition focus group discussions were held with selected farmers in the Upazila. The results showed evidence of climate change through analysis of climate data, crop yields and farmers perceptions. Evidence was manifested through the temperature increase, increasing frequency of natural disaster and irregular rainfalls, which are directly influenced the condition of food security of Kalapara Upazila. It was the concluded that climate change occurring in Kalapara Upazila has resulted in significant decline in agricultural production. The paper is also showed the comparison of sources of food and production amount between past and present. The people\'s perceptions show that the temperatures are rising, timely rainfall is not available and crop production seasons are shifting. The lessons show that people are aware of climate change and its impacts on food security. But lack of proper knowledge is the main causes to adapt to these changes.

The paper analyses the impacts of climate change and crop production in the communal farms of Masvingo Province in Zimbabwe. The paper shows current climate change scenarios for Masvingo, analyses the relationship between climate change scenarios crop production and then prescribe adaptive measures to climate change. Historical monthly climate data from Zimbabwe Meteorological Services Department and crop yield data from the Ministry of Agriculture and Rural Development were used. A questionnaire survey was also conducted to gather farmers perceptions on climate change in addition two focus group discussions were held with selected farmers in the province. The results showed evidence of climate change through analysis of climate data, crop yields and farmers perceptions. Evidence was manifested through the increasing frequency of droughts, dry spells and the shifts of the rainfall season. It was the concluded that climate change occurring in Masvingo has resulted in significant decline in crop production. The people's perceptions shows that the crop growing seasons are becoming shorter, temperatures are rising, rainfall amounts are declining and mid season droughts are also occurring more frequently. The lessons learnt mean that people are aware of climate change and its impacts on crop production but lack the means to adapt to these changes.

An intercomparison of climate trends between 2 adjacent large cities, Shenzhen and Hong Kong, in the past 46 yr (1968�2013) suggests that the rate of urbanization is clearly reflected in the rate of the change in local climate. Since becoming a special economic zone in 1980, Shen- zhen has experienced a very rapid urbanization process. In only 30 yr, Shenzhen has transformed from a small town into a metropolis rivaling Hong Kong. The gross domestic product and urban built-up area in Shenzhen have increased rapidly. Climate data analysis over the last 46 yr showed a greater rate of change in climate in Shenzhen than in Hong Kong over the same period. In Shenzhen, average annual temperature warmed by 1.63 ± 0.18°C (~0.35 ± 0.04°C per decade), average maximum temperature increased by 0.90 ± 0.19°C, average minimum temperature in - creased by 2.09 ± 0.23°C and the diurnal temperature range (DTR) decreased by 1.18 ± 0.23°C. Over the same period in Hong Kong, average temperature increased by 0.47 ± 0.20°C (~0.10 ± 0.04°C per decade), maximum temperature increased by 0.12 ± 0.28°C, minimum temperature in - creased by 0.55 ± 0.20°C and DTR decreased by 0.43 ± 0.27°C. In addition, relative humidity in Shenzhen decreased by 13.13 ± 1.78% in the last 46 yr, while there were no significant changes in Hong Kong. Finally, data analysis showed that urbanization has no significant effect on total rainfall for both cities.

<p>According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.</p><p>Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.</p><p>We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.</p><p><strong>Acknowledgement</strong>:</p><p>1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.</p><p>2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.</p><p> </p>

Climate Change in Algeria: Vulnerability and Strategy of Mitigation and Adaptation

Color mapping plays a critical role in visualization of time-varying data and also sets a challenge for researchers due to the consistency of mapping and great changes in time-varying data. In order to solve this problem and generate feature-prominent animation, we present a two phase optimization technique using bilateral filtering and global energy functions. In the first phase, for each time step, we use a weighted mapping function which combines linear mapping and feature histogram. In the second phase, an optimization function taking global color mapping and minimum color difference into consideration is designed. So users can clearly distinguish between data in variable time steps and easily understand the corresponding relationships between different structures. The experiments' results show that our method can achieve high quality visualization for both static and time-varying data.

First posted January 25, 2024 For additional information, contact: Director, Dakota Water Science CenterU.S. Geological Survey821 East Interstate Avenue, Bismarck, ND 585031608 Mountain View Road, Rapid City, SD 57702Contact Pubs Warehouse Flood-frequency analysis, also called peak-flow frequency or flood-flow frequency analysis, is essential to water resources management applications including critical structure design and floodplain mapping. Federal guidelines for doing flood-frequency analyses are presented in a U.S. Geological Survey Techniques and Methods Report known as Bulletin 17C. A basic assumption within Bulletin 17C is that for drainage basins without major hydrologic alterations, statistical properties of the distribution of annual peak streamflows (peak flows) are stationary; that is, the mean, variance, and skew are constant. The stationarity assumption has been widely accepted within the flood-frequency community; however, a better understanding of long-term climatic persistence and concerns about potential climate change and land-use change has caused a reexamination of the stationarity assumption. Flood-frequency analyses that do not incorporate observed trends and abrupt changes may result in a poor representation of the true flood risk. Bulletin 17C does not offer guidance for incorporating nonstationarities when estimating floods, and it describes a need for studies that incorporate changing climate or basin characteristics. In response to this need and a history of concern regarding nonstationarity peak flows in the region, this study was done by the U.S. Geological Survey, in cooperation with the Departments of Transportation of Illinois, Iowa, Michigan, Minnesota, Missouri, South Dakota, and Wisconsin; the Montana Department of Natural Resources and Conservation; and the North Dakota Department of Water Resources, to assess potential nonstationarity in peak flows in the north-central United States.This chapter summarizes the methods used to detect hydroclimatic changes in peak-flow data in the study region. A wide range of analyses and statistical approaches are applied to document the primary mechanisms controlling floods and characterize temporal changes in hydroclimatic variables and peak flow. Four periods were selected for analysis of peak flow, daily streamflow, and climate data. The periods are (1) a 100-year period, 1921–2020; (2) a 75-year period, 1946–2020; (3) a 50-year period, 1971–2020; and (4) a 30-year period, 1991–2020. The climate data consist of monthly time series estimates of temperature, precipitation, potential evapotranspiration, actual evapotranspiration, snowfall, soil moisture storage, snow water equivalent, and runoff on a 3.1-mile by 3.1-mile grid for the conterminous United States.Statistical and graphical analyses were used to investigate potential changes in hydrology and climate. The starting point for these analyses was the initial data analysis of peak flow described in Bulletin 17C, which includes plotting the peak flow and checking for autocorrelation, monotonic trends, and changes points. Analyses were added to examine additional features in the data. To examine potential causal drivers of changes, the climate data were analyzed graphically and statistically. Results are provided in a U.S. Geological Survey data release. The study limitations are documented for users of the results.

Next-generation sequencing (NGS) techniques produce high quantities of DNA and RNA sequencing data, enabling variation analysis at a whole genome level. Due to the massive availability of NGS data, the processing and analysis now constitute a serious challenge for research in life sciences. This necessitates the development of novel, user-friendly and scalable software tools, which are able to jointly handle large NGS data sets in order to break the bottleneck that has already moved from data generation to data analysis in high-throughput biology. Here we introduce a highly efficient NGS analysis and visualization software tool (Rikurator), which is designed for researchers who are struggling with NGS data analysis and may not have access to a dedicated bioinformatics team. Rikurator can be applied to a multitude of genomic analysis tasks, including discovery of pathogenic mutations in familial or sporadic sample sets, analysis of structural variants and somatic characterization of cancer genomes. To these ends the software implements interactive variant filtering and annotation methods based on sample comparison, control data (e.g. ExAC), amino acid changes and quality scores, which provide an intuitive view at the studied samples. Shared candidate genes in a set of dozens of individuals can be discovered in a matter of minutes with an ordinary laptop computer. Importantly, the software is able to analyze hundreds of whole-genome sequenced samples using only a modest amount of RAM. The tool also visualizes and integrates genes, variants (VCF file format), sequence reads (BAM) and user defined features (BED/GFF). In addition, Rikurator is not limited to human studies, supporting reference genomes and genome annotations in standard formats. The software supports targeted (e.g. exome), whole-genome, RNA, Oxford Nanopore and PacBio sequencing data. Rikurator can be run on Windows, Linux and Mac, and it will be freely available after publication. Citation Format: Riku Katainen, Veli Mäkinen, Lauri A. Aaltonen, Esa Pitkänen. Fast and scalable software for comparative variant analysis and visualization of massive next-generation sequencing data. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5281.

Encouragingly, more people accept that climate change is happening than ever before (and is largely human-made); almost two-thirds of the UK agree with the scientific consensus, according to a recent survey (ComRes, 2017). However, misconceptions still exist, and apathy is common. So how can climate scientists make people aware of the options available for action? In February 2017, the Royal Meteorological Society co-hosted a meeting with the Grantham Institute – Climate Change and the Environment – at Imperial College London, to exchange ideas on better ways to engage with, and communicate their science to, the public, businesses and government. As the issue of climate change grows in complexity, we need to find new ways to communicate. Data visualisations are a powerful way of exploring and spotting patterns in data; one of the most compelling examples is Spiralling global temperatures showcased at the Rio Olympics (Figure 1). Rosamund Pearce, a multimedia specialist at Carbon Brief, opened the event with a guide to creating engaging and accurate data visualisations. She began by making the important distinction between data visualisations and infographics. Infographics are usually subjective and explain a specific story, while visualisations tend to be automatically generated and are objective. Ros added that when choosing a chart, you have to balance the ease of interpretation with visual impact and memorability; for example, a bar chart might be easily understood, but it is not very eye-catching or memorable, and she recommended websites to help with chart selection (see, for example, www.datavizcatalogue.com). She also discussed colour perception and cultural differences in interpreting maps. India, for example, has its own official map. In the current era, climate scientists might be reticent about talking to journalists. In his talk, Pete Castle, a media expert from the University of Reading, provided reassurance and urged experts to hit back at climate scepticism. A recent poll shows scientists to be one of the most trusted sources – fourth in a list of 25 professions (79% agreed; Ipsos MORI, 2016). The media and public interest in climate change fell back in 2016, even though it was the warmest year on record and the Paris climate agreement was ratified. In 2017, Donald Trump has put the subject back on the agenda, Pete noted. While traditional news journalism is changing rapidly, it still influences public opinion. Pete provided some practical tips on how to work positively with the media. He urged scientists to build relationships by briefing journalists on upcoming stories and by seeking help from organisations such as the Science Media Centre. He provided real-life case studies to illustrate how it can be good for a scientist’s institution and scientific career, and for climate science overall. Our third speaker was Alyssa Gilbert from the Grantham Research Institute, who is an expert at connecting academics with business leaders. Alyssa highlighted reasons for working with business, which include increased access to facilities and funding as well as help with scaling-up discoveries to commercialisation (such as photovoltaics) and driving innovation through spin-off companies (such as Climate KIC – a public–private innovation partnership focused on climate innovation): It is equally important to understand what challenges businesses are facing, and be able to put your science in that context, in order to build a solution and to describe your science in a way that is meaningful to the business community. She reasoned that all types of university research could potentially be shared with businesses, but it is important to assess how far different types of knowledge are from having a commercial impact. Diverse types of organisations could be interested, especially the retail and finance sectors. Alyssa listed the types of job function that could potentially be worth talking to, and she emphasised the importance of timing in anticipating external drivers such as COP-21 (the 2015 United Nations Climate Change Conference). Alyssa further outlined novel ways to interact with businesses, such as commissioned research, membership clubs, advisory boards and fellowships, and organised hackathon events. Science can also have a profound effect on international policy. David Warrilow, former Head of Science at the Department of Energy and Climate Change (now the Department for Business, Energy and Industrial Strategy (BEIS)), gave a historical perspective on how science and policy have developed together over the last 30 years. He also described effective ways of improving the dialogue between the scientific community and policymakers: The way in which science is communicated to the outside world goes through media, politicians and policymakers, and the biggest impact is often from a high-profile figure. It was a key speech from Margaret Thatcher, delivered in 1988, which actually positioned climate change high on the political agenda, but it gained traction as the public had other environmental concerns (e.g. acid rain and ozone depletion). David highlighted how big pushes on policy also transpired after each Intergovernmental Panel on Climate Change (IPCC) report. The evidence was accepted because there was confirmation from several independent voices, a rigorous review approach (e.g. IPCC) had been used, and there was broad agreement both nationally and internationally. The final speaker of the afternoon was Bob Ward, Policy and Communications Director of the Grantham Research Institute, LSE, who finished with a strong and urgent call to action for climate scientists to improve their communication skills. The underlying message from Bob was that all our children and future generations will be affected by climate change. However, climate scientists have a special moral dilemma which may lead them to go beyond talking about their own research and to commit to advocating increased awareness of climate change more broadly. He said that talking about how the lives and livelihoods of people will be affected by climate change can be enormously powerful in public outreach. He also emphasised the importance of talking about local impacts (e.g. threats to productivity and cities) as well as global issues. The slides, audio and presentations from this meeting can be downloaded here: https://www.rmets.org/events/avoiding-myth-mayhem-and-myopia-challenge-climate-science-communication

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models’ historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.

With the growing need for visualization to aid users in understanding large, complex datasets, the ability for users to interact and explore these datasets is critical. As visual analytic systems have advanced to leverage powerful computational models and data analytics capabilities, the modes by which users engage and interact with the information are limited. Often, users are taxed with directly manipulating parameters of these models through traditional GUIs (e.g., using sliders to directly manipulate the value of a parameter). However, the purpose of user interaction in visual analytic systems is to enable visual data exploration -- where users can focus on their task, as opposed to the tool or system. As a result, users can engage freely in data exploration and decision-making, for the purpose of gaining insight. In this position paper, we discuss how evaluating visual analytic systems can be approached through user interaction analysis, where the goal is to minimize the cognitive translation between the visual metaphor and the mode of interaction (i.e., reducing the "interaction junk"). We motivate this concept through a discussion of traditional GUIs used in visual analytics for direct manipulation of model parameters, and the importance of designing interactions the support visual data exploration.