The eye is a single web platform which helps the urban designer and decision maker to simulate and predict the impact of different urban planning scenarios. This platform uses real-time data through the computer algorithm and machine learning in order to transform the raw data to the knowledge about cities dynamic state. Furthermore, the project opens up a new workflow for urban planning by providing a range of concussion that proposals will cause. This insight could help the designer in the next generation of their design.

Today the urban planning is based on the assumption and speculation from uncertain data through the subjective process.

As mentioned in the video. In the urban environment, we are facing a complex dynamic system which is growing rapidly. and a series of demands are compelled. To address the requirement of this system we divided the organization between different stakeholders. Also it worth to mention that because we living in a consumer based society most of the decisions are in favor of the financial benefit.


This dissertation tends to use the real-time data through the processes of a computer algorithm and machine learning to be able to create a single platform within users (will be defined in later) can access to and process the data. Moreover, they can simulate and predict the impact of urban planning.

the stakeholders who are involved in the city organization can simulate their proposals. By this, they can have an insight into the impact that their decision may cause. The platform is also a way of communication between different stakeholder for a coherent workflow in the city management system.

To clear out certain terminologies that may intercept your understanding in the following section.

Data: is the smallest quantity of information

Information: is Data within the interconnections and correlations.

Knowledge: is the product of the cognitive on the information.


There are many projects in the discipline of Urban designing that try to investigate digital data as a source of information. The mainstream of this project is data visualization to convey a relation between data and geolocation.


This is the flow of the data model in the current state of the art.

As mentioned earlier by the digital revolution we have access to a vast amount of data from different sources and their updating in each second. This almost impossible for humans to process and react to this amount of information in real-time. What has been proposed is an automation process white the help of artificial intelligence in the way to find out the connection between each bit of data.

Web platform: easy accessibility.

Data visualization

                                                    1-perceive easier with our sight.

                                                    2-clear distinguishing

                                                    3- simple interacting


                                                    1-see the impact of certain planning

                                                    2- Understand the influence of different parameter

                                                    3-Evaluate each decision or planning


                                                    1-to be communicate

                                                    2-to share

                                                    3-to be collective

This diagram demonstrates how the data flow happening in the platform.

In order to test this proposal, an experiment was designed. For this experiment will focus on mobility data in particular. Since mobility is the most requested type of data. Also, It is changing rapidly, thus it could be a good representative of cities dynamic behavior. For the context of the experiment, Manhattan is chosen for two reasons. First because of easy accessibility to data. On the other side because of high interest in terms of complexity

The goal of the experiment is to find the relation between traffic and flow of the people in the context of the urban environment with the help of machine learning algorithm.

These are the land use and infrastructure that has been considered in the experimentation.

And this is the mobility parameter that has been looked.


In this experiment, data has been modeled in the four following way.


Grid intersection


Random points


Raster image


Street Buffering


For the objective of this experiment, the street buffer model worked quite well. Because of the restriction on traffic data the experiment being held on traffic simulation data, However, the algorithm reached an acceptable level of prediction.





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