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Discovering patterns and trends through spatial mapping of data. Designing digital maps with geostatistics
collecting and assimilating data and interpreting it in order to identify changes and forecast trends
اس دیتا استارتاپی هست که به تمام علاقمندان و فعالان در حوزه داده کمک خواهد کرد که شبکه سازی کنند
در بازه های زمانی متفاوت به عنوان مشاور تحقیقات بازار و مدیر تحقیق و توسعه
در پروژههای اندازهگیری ارزش برند، بررسی سهم بازار، طراحی کمپینهای تبلیغاتی و …..
در مدلسازی مدل اعتبارسنجی مشتریان و راه اندازی سامانه اعتبارسنجی و اندازه گیری ارزش برند
در پروژههای اندازهگیری ارزش برند، طبقهبندی مشتریان، تحلیل داده های افکارسنجی و شبکههای اجتماعی، ارزیابی اثر بخشی تبلیغات و ….
We introduce a two-step method to perform spatio-temporal balanced sampling in a design-based approach. For populations with spatio-temporal trends and with anisotropic effects in the variable of interest, the prediction can be further improved by selecting samples that are well spread over the entire population in space and time. We control the spread of the sample over the population by using the volume of the corresponding three-dimensional Voronoi tessellation. Indeed, spatio-temporal design-based balanced sampling is even more efficient under the presence of a trend and anisotropic effects. We present an intensive simulation study comparing our method to other available methods for spatio-temporal sampling. Finally, we analyze real data by sampling from a population of temperature stations over six European countries.
Spatial statistics is the analytical science of spatial correlated data. In environmental fields of studies that deal with spatially correlated data due to their location in a given area, on the other hand, in the survey sampling it is assumed that the sample is taken from a population with independent units. This assumption is used at all stages of sampling, analysis and modeling. But when the units of the study population are correlated, the entire process, such as sampling methods and statistical process requires review and the entire process will be considering the correlation structure. In the classic sampling methods from a variable, when some covariates exist, then the balanced sampling is used for improving the quality of sample. In this paper, we introduce spatial balanced sampling design where the components of the spatial locations are considered as covariates. Then in an intensive simulation study Is shown that the kriging error induced by our sampling method is less than foe the other available methods. Finally, the application of the proposed method is shown in a real example.
The economic evaluation and the financing of transportation projects require comprehensive estimation and determination of all transportation-related external costs. The effect of accessibility on property values and the hedonic price of environmental attributes related to the transportation system are among the most important external effects. In this study, the willingness to pay (WTP) for improved accessibility and environmental quality was determined by the use of a stated preference (SP) technique. With SP data collected in Tehran, Iran, a multinomial logit model was developed, and WTP was estimated with this model. Because the WTP for environmental attributes was estimated with qualitative measures, a fuzzy transformation was used to estimate the WTP for a unit increase in environmental quality.
As the use of new communication technologies have grown largely in the previous decade, researchers’ interest in estimating Origin Destinations (O/Ds) from these data sources have increased. One of the major limitations of the previous efforts on the O/D estimation is the lack of trip purpose data. This data are important for travel demand modeling and travel behavior analysis. In this paper, a Canonical Discriminant Analysis has been used to classify trips into different trip purposes. The data collected during the Mashhad city comprehensive travel survey (2008) has been used in this paper. As the information regarding the trip purposes was available only for these trips, the data was used. The validation results have shown that the model was successful in the classification of 66% of the trips. These results show that the proposed model could be used to classify trips that are tracked by different sources in order to obtain distinct O/D matrices for each trip purpose of interest.
Khavarzadeh, R. Mohammadzadeh, M. (2017). A simple two-step method for spatio-temporal design-based balanced sampling. Transportation Research Board 96th Annual Meeting. 2017
Khavarzadeh, R. Kalantari, N. Alirezaei, N. (2015). A Predictive SARIMA Model for PM10 and PM2.5 levels in Mashhad based on traffic flow and metrological data. The 14th International conference on Traffic and Transportation Engrineering
Kalantari, N. and Khavarzadeh, R. “Trip Purpose Estimation by Canonical Discriminant Analysis.” Transportation Research Board 94th Annual Meeting. No. 15-4884. 2015.
Khavarzadeh, R. Kalantari, N. Pournaghi, M. Alirezaei, N. (2015). A Latent analysis on The Effect of Social Networks on Major Shopping Center Choice. The 14th International conference on Traffic and Transportation Engrineering.
Khavarzade, R. (2014). Investigation of Pedestrian’s Gap Acceptance behavior at Crosswalk. The 13th International conference on Traffic and Transportation Engrineering.
۷-Kalantari,N. Ssadjedi, SJ. Khavarzadeh, R. (2014). Willingness to Pay Method to Estimate the Effect of Accessibility on Property Price. Transportation Research Board 93rd annual meeting.