1.Searching for paper I researched what network analysis is possible with people’s location data. Among them, the following paper distinguished regions and structured space. Based on this paper, I will add more network analysis based on the local data of Pohang city’s ‘구’ and ‘동’. 2.Study how to control data For studying, I used jupyter…

1.Community detection Since the amount of data is small, I analyzed how much community was extracted by increasing the resolution. The top five communities in the collected data were analyzed. By the modularity value, the same community has the same color. *Incheon *Daejeon *Pohang 2.Geocoding API To extract the latitude and longitude values, I created…

Network drawing in python I studied how to draw a simple network using the libraries provided by python, Networkx and Pyplot. *Code *Results *UML I didn’t draw it because it’s simple code. *Link

1.Using Open data *Air port data I am analyzing the network with public data (Node and edge csv file) and comparing it with the data I have gathered. -Node size change according to number of degree : This network makes it easy to find airports that serve as hubs around the world. -degree distribution -Diameter…

1.Network Visualization There were problems with analysis for using collected data. I decided to use open data for understanding how to analyze the network. I used airport location data files in world wide. I could check the data format exactly. And it was good chance to understand the community detection. *Network *Number of edge and…

1.Network Visualization by location I visualized the network with the data I have. Although the data can be analyzed, the latitude and longitude are very finely divided, making it difficult to analyze. There seems to be a need to unify latitude and longitude with standards. *Incheon *Daejeon 2. Modularity Analysis The location data is so fragmented…

1. Data pre-processing A total of 10 user data were all preprocessed. But even if other nodes are in the same location, we need to find a way to recognize them as the same node. There are 5545 Nodes and 5534 Edges. I uploaded the list files. *Node List *Edge List 2. Java Study I…

1. Data pre-processing I organized the data for three users and visualized the time on the network *Node (CSV file) *Edge (CSV file) * time 2.Java Study I studied how real developers use interfaces. *Code *UML *Link

1.Collecting Data I have collected a total of 10 users’ data by today. And as shown in the previous post, I need to proceed with data preprocessing. Before proceeding, Node and Edge were defined. But among nodes, I’m looking into how to proceed for nodes caused by other users but which have same location. In…

1.Network visualization First, I challenged the visualization with one persons’s location data. I used gephi by installing two plug-ins, geolayout and map of countries. As I thought, the network was well formed with geodata via geolayout. However, the map of countries plugin is optimized for world map, so the map of Korea is not detailed.…