# Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data

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## Abstract

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## 1. Introduction

- This paper proposed a new research framework for learning urban dynamic interaction, which used a dynamic community detection algorithm and a clustering algorithm to mine the urban dynamic interaction patterns.
- By using Baidu migration data, we learned the inter-monthly dynamic interaction patterns of Chinese cities.

## 2. Study Area and Datasets

#### 2.1. Study Area

#### 2.2. Datasets and Data Pre-Processing

- Spatial interaction strength definition.

- 2.
- Constructing Dynamic Urban Spatial Interaction Networks.

## 3. Methodology

#### 3.1. Dynamic Community Detection

#### 3.1.1. Louvain Community Detection Algorithm

**First step:**local modularity optimization.

- 2.
**Second step:**folding the communities into nodes.

#### 3.1.2. Jaccard Matching Method

**Growth:**a community that grew by integrating new city nodes.**Contraction:**a community that contracted by rejecting some of its city nodes.**Merge:**two communities or more that merged into a single one.**Split:**one community that split into two or more communities.**Continue:**a community that did not change.

#### 3.2. Hierarchical Clustering Method

## 4. Results

#### 4.1. Urban Communities and Dynamic Events

#### 4.2. Dynamic Patterns of Urban Agglomeration

- Fixed spatial interaction pattern

- 2.
- Long-term spatial interaction pattern

- 3.
- Short-term spatial interaction pattern

## 5. Discussion and Conclusions

- The interaction between some cities on the edges of the provinces and cities of neighboring provinces was usually higher than those within the provinces, such as Hulunbeier and Chifeng in Inner Mongolia. There were also cities on the edge of the provinces that had strong interactions with both the cities within the provinces and adjacent provinces, such as Puyang in Henan.
- Some provinces that the public thought were highly interactive seemed to be not closely connected in the urban spatial interaction network. For example, the "three provinces in northeastern China" (Heilongjiang, Jilin, and Liaoning), "YunGuiChuan" (Yunnan, Guizhou, and Sichuan), and "Qinghai-Tibet Region" (Tibet and Qinghai), often mentioned by people, had not formed long-term spatial interaction patterns. Some provinces with large economic differences had formed fixed spatial interaction patterns instead, such as GY. Maybe they shared the same regional culture.

- Some cities both in developed and less developed areas showed relatively stable urban interaction structures. However, the reasons for their formation were different. Due to the radiation effect of big cities, economically developed areas interacted stably with surrounding cities to form independent communities. However, in the less developed regions, due to the limitations of geographical, economic, or traffic conditions, these cities did not interact with external cities and formed independent urban agglomerations themselves.
- The monthly dynamic changes in cities in medium-level developed areas were obvious. These cities were in central and western China. The radiation capacity of these cities was limited and only attracted other cities for a few periods. Therefore, the interaction of these cities tended to split and merge in different periods, representing short-term spatial interaction patterns.

**Data**: The long duration and wide coverage of Baidu migration data have indeed played an important role in mining the inter-monthly dynamic patterns of Chinese cities. However, due to the protection of user privacy, we could not know the specific migration volume, which limited the further study of this paper.**Analysis:**Some “Gordian knots” in spatial interaction networks still exist [54]. Current visualization methods make it difficult to show the dynamic changes in urban spatial interaction. In addition, there are a lack of relevant spatial analysis methods to understand the driving force of dynamic change in urban inter-monthly interactions. It is mainly because urban interaction is affected by many factors, such as urban economic conditions, relevant policies of local governments, natural conditions of different regions, etc. Due to the lack of monthly statistical data on the urban economy, further analysis was not accessible.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**The process of the Louvain algorithm. (

**a**) is the initial setup of the network, assigning all nodes as separate communities, (

**b**) is the result of local modularity optimization, where different colors mean different communities, and (

**c**) is the result of folding communities into new nodes, forming a new network, where the new network contains edges not only between nodes but also within nodes.

**Figure 4.**The urban communities of each month. Each sub-plot represents the information of the urban communities in that month.

City A | Location A | City B | Location B | Spatial Interaction Strength | Month |
---|---|---|---|---|---|

Beijing | 116.40, 34.90 | Tianjin | 117.20, 39.08 | 24.38 | 2021 Jan. |

Beijing | 116.40, 34.90 | Shijiazhuang | 114.51, 38.04 | 6.08 | 2021 Jan. |

Tianjin | 117.20, 39.08 | Shijiazhuang | 114.51, 38.04 | 3.95 | 2021 Jan. |

... | ... | … | … | … | … |

**Table 2.**Event definition table. A is the urban community of the month before and B is the urban community of the month after.

Jaccard Score | Relationship | Events |
---|---|---|

$J\left(A,B\right)$ = 1 | A = B | Continue |

$\mathrm{Threshold}\le $$J\left(A,B\right)$ < 1 | $A\subseteq $B | Growth |

$A\supseteq $B | Contraction | |

$0\le $$J\left(A,B\right)$ < threshold | $A\subseteq $B | Merge |

$A\supseteq $B | Split | |

0 | - | No event |

Communities | Provinces | Communities | Provinces | Communities | Provinces |
---|---|---|---|---|---|

EX | Hubei; Hunan | JJJL | Beijing; Tianjin; Hebei; Liaoning | XIANG | Hunan |

GAN | Jiangxi | JJJLJ | Beijing; Tianjin; Hebei; Liaoning; Jilin | XIN | Xinjiang |

GCY | Guizhou; Sichuan; Chongqing | JZHH | Jiangsu; Zhejiang; Shanghai; Anhui | XQ | Xinjiang; Qinghai |

GM | Jiangxi; Fujian | LJ | Liaoning; Jilin | YG | Yunan; Guizhou |

GNMS | Gansu; Ningxia; Inner Mongolia; Shaanxi | LJJJ | Shandong; Beijing; Tianjin; Heibei | YJ | Henan; Shanxi |

GQ | Gansu; Qinghai | LU | Shandong | YU | Henan |

GY | Guangxi; Guangdong | MIN | Fujian | YUN | Yunnan |

HEI | Heilongjiang | NMS | Ningxia; Inner Mongolia; Shaanxi | ZANG | Tibet |

HJ | Heilongjiang; Jilin | QIN | Qinghai | ZCY | Tibet; Sichuan; Chongqing |

HLJ | Heilongjiang; Jilin; Liaoning | QIONG | Hainan | XIANG | Hunan |

EX | Hubei; Hunan | JJJL | Beijing; Tianjin; Hebei; Liaoning | XIN | Xinjiang |

GAN | Jiangxi | JJJLJ | Beijing; Tianjin; Hebei; Liaoning; Jilin |

Clusters | Urban Communities |
---|---|

C0 | QIONG; JZHH; GY; YG; XIN; E |

C1 | HEI; CY; LJ; GQ; NMS; ZANG; JJJJ; XG |

C2 | XIANG; MIN; GM; JIN; GNMS; ZCY; GAN; QZ; LJJJ; JJJL; HJ; JJJLJ; EX; QIN; HLJ; YUN; XQ; YJ; GCY |

C3 | YU |

C4 | LU |

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## Share and Cite

**MDPI and ACS Style**

Jiang, H.; Luo, S.; Qin, J.; Liu, R.; Yi, D.; Liu, Y.; Zhang, J.
Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data. *ISPRS Int. J. Geo-Inf.* **2022**, *11*, 486.
https://doi.org/10.3390/ijgi11090486

**AMA Style**

Jiang H, Luo S, Qin J, Liu R, Yi D, Liu Y, Zhang J.
Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data. *ISPRS International Journal of Geo-Information*. 2022; 11(9):486.
https://doi.org/10.3390/ijgi11090486

**Chicago/Turabian Style**

Jiang, Heping, Shijia Luo, Jiahui Qin, Ruihua Liu, Disheng Yi, Yusi Liu, and Jing Zhang.
2022. "Exploring the Inter-Monthly Dynamic Patterns of Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data" *ISPRS International Journal of Geo-Information* 11, no. 9: 486.
https://doi.org/10.3390/ijgi11090486