Modeling Dataset Development and Analysis of Long-term Poultry Trade Network in China (2010–2022)

REN Hongyan^1*  WANG Qi²  XU Nankang^1,3

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

2. School of Geographic Sciences, East China Normal University, Shanghai 200241, China;

3. China Telecom Corporation Limited Shanghai Branch, Shanghai 200120, China

Abstract: This study developed the modeling dataset of long-term poultry trade network in China (2010–2022) based on city-level poultry meat production and consumption data for the period 2010–2022, employing the modified radiation model. The dataset systematically reveals the spatiotemporal evolution of China’s poultry trade patterns. The findings indicate that: Overall, consumption growth fluctuated considerably while production growth remained relatively steady; the scale of intercity poultry trade continued to expand, with short‑distance flows being dominant; both the clustering and density of the network increased, presenting a pattern of dense in the east and sparse in the west, short‑distance dominance, and enhanced connectivity. The dataset includes the following data during 2010–2022: (1) annual urban poultry production and consumption; (2) urban poultry trade directed network; (3) urban poultry trade undirected network; (4) urban poultry trade binary network; (5) distribution of poultry trade volume by distance; and (6) characteristics of poultry trade network. The dataset is archived in .xlsx and .shp formats, and consists of 12 data files with data size of 103 MB (compressed into one file with 47.3 MB).

Keywords: poultry trade; radiation model; trade network; 2010–2022

DOI: https://doi.org/10.3974/geodp.2026.05.06

Dataset Availability Statement:

The dataset supporting this paper was published and is accessible through the Digital Journal of Global Change Data Repository at: https://doi.org/10.3974/geodb.2026.06.04.V1.

1 Introduction

Poultry, as one of the primary sources of animal protein—including meat, eggs, etc.—plays a critical role in food supply. In particular, with the continuous development of urbanization in China, consumers’ awareness and consumption patterns have undergone profound changes, driving a dietary shift from a grain‑dominated structure towards meat‑oriented consumption. This consequently leads to an increasing demand for poultry meat products^[1,2]. During 2014–2024, the share of poultry meat in China’s domestic apparent meat consumption rose from 20.7% to 26.0%^[3]. Meanwhile, poultry, as an intermediate host in avian influenza transmission, is considered to play a key amplifying and bridging role in the transmission chain. In particular, the trade of poultry through live bird markets (LBMs) has been identified as an important pathway for the zoonotic transmission of avian influenza^[4–6].

Previous radiation‑model‑based studies on China’s intercity poultry trade networks at provincial or city scales^[7] mostly focused on single-year analyses, lacking a long‑term network to systematically analyze spatiotemporal changes or reveal the transmission mechanisms of zoonotic diseases like avian influenza. To address this gap, we collected national poultry production and consumption data from 2010 to 2022, constructed a long‑term poultry trade network using the modified radiation model, and compiled it into the Modeling dataset of long‑term poultry trade network in China (2010–2022).

2 Metadata of the Dataset

The metadata of the Modeling dataset of long-term poultry trade network in China (2010–2022)^[8] is summarized in Table 1. It includes the information regarding the name, authors, geographic region, data period, temporal resolution, spatial resolution, dataset files, data publication and sharing service platform, etc.

Table 1  Metadata summary of the Modeling dataset of long-term poultry trade network in China (2010–2022)

3 Methods

3.1 Data Sources

This dataset is based on city-level data on poultry meat production, per capita food expenditure, resident population, and provincial per capita poultry meat consumption. The provincial per capita poultry meat consumption data were obtained from the China Statistical Yearbook (2011–2023)^[10], while the poultry meat production, per capita food expenditure, and resident population data were sourced from the statistical yearbooks or bulletins of provinces, municipalities directly under the central government, and autonomous regions (2011–2023)^[11–41].

3.2 Data Processing

For poultry production in each city, poultry meat production was used as a proxy indicator. Based on the provincial per capita poultry meat consumption, the per capita food expenditure of each city within the province, and the resident population of each city, the poultry consumption of each city was estimated^[7]. The specific calculation Equation is as follows:

                                                                                                        (1)

                                                                                                (2)

where  denotes the annual per capita food expenditure of city  (CNY·person^–1·year^–1);  denotes the number of cities in the province where city  is located； denotes the mean annual per capita food expenditure of thecities within the province (CNY·person^–1·year^–1);  denotes the poultry consumption of city  (kg);  denotes the annual provincial per capita poultry meat consumption (kg·person^–1·year^–1);  denotes the resident population of city  (persons).

3.3 Modified Radiation Model

Drawing on the radiation model for simulating population movement patterns and estimating population flows^[42], we treat poultry in trade as an element analogous to population. By incorporating poultry production and consumption scales as key parameters, we employ the modified radiation model to simulate and calculate the poultry trade network among cities or provinces. The Equations^[7] are as follows:

                                                                                                                (3)

                                                                                (4)

where  denotes the total poultry exports of city ,  denotes the poultry production of city ,  denotes the export proportion of poultry production, which is treated as a constant (=1) in this study.  denotes the poultry trade volume between city  and city j.  denotes the poultry consumption of city ,  represents the sum of poultry consumption of all cities within the distance range from city  to city  (taking city   as the centre). All variables are in kg, except for ,  which is a dimensionless constant.

Since the poultry trade network derived from the modified radiation model is directed (i.e., bilateral flows between city pairs), it is difficult to visualize directly in space. To clearly illustrate the intercity trade pattern, we constructed an undirected poultry trade network weighted by the mean of the bidirectional trade flows between each pair of cities, and subsequently generated the spatial network map of poultry trade.

3.4 Complex Network Analysis

In this study, we employed complex network analysis, focusing on 2 indicators, namely network density and clustering coefficient, to analyze the structural characteristics of the poultry trade network.

Network density is used to measure the connection strength among nodes, and its calculation Equation is as follows:

                                                                                                       (5)

where  is the network density,  is the number of connections in the network, and  is the number of nodes in the network. 

The clustering coefficient reflects the degree of clustering among the neighbors of a node, and its calculation Equation is as follows:

                                                                             (6)

where  is the clustering coefficient,  is the number of actual connections among the neighbors of node ,   is the degree of node ,   is the number of nodes in the network.

Since the poultry trade network derived from the modified radiation model is fully connected—i.e., trade flows exist between any pair of cities—its network density and clustering coefficient are constant values, and thus cannot effectively reflect the structural characteristics or changes of the poultry trade network. To address this limitation, we applied the three‑sigma rule to identify outliers in trade volumes and used the mean of the remaining data as the global threshold. For each city pair, if the trade volume in either direction exceeded this threshold, the value was assigned as 1; otherwise, it was assigned as 0, thereby constructing a binary poultry trade network. Based on this network, we further calculated the network density and clustering coefficient to reveal the structural characteristics of the poultry trade network.

4 Data Results

4.1 Dataset Composition

The Modeling dataset of long‑term poultry trade network in China (2010–2022) consists of: (1) annual urban poultry production and consumption; (2) urban poultry trade directed network; (3) urban poultry trade undirected network; (4) urban poultry trade binary network; (5) distribution of poultry trade volume by distance; and (6) characteristics of poultry trade network.

Among these, the urban poultry production and consumption data are raw data and serve as the fundamental input to the radiation model. The directed network, undirected network, trade‑volume‑by‑distance data, and network characteristic data are output data, which are used to characterize intercity poultry trade relationships and the changes in network structure. In addition, to facilitate network visualization and structural feature calculation, we provide the binary network data as intermediate data.

4.2 Basic Situation of Poultry Production and Consumption in China

Both poultry production and consumption in China have shown an overall upward trend. By 2022, production had exceeded 25 million tons, while consumption had surpassed 15 million tons. During 2010–2022, poultry production, consumption, and the production‑consumption gap exhibited different change patterns: production showed a fluctuating growth trend; consumption first increased and then declined; and the gap first rose, then fell, and then rebounded. In particular, from 2012 onwards, the growth rate of consumption accelerated notably, accompanied by a shift of the production‑consumption gap from an upward to a downward trend. By 2020, consumption began to decline while the production‑consumption gap started to rise, and throughout this process, production increased only moderately (Figure 1).

Figure 1  Basic situation of China’s poultry market (2010–2022)

4.3 Poultry Trade Network and Structural Characteristics

The structure of China’s intercity poultry trade network exhibits significant differentiation in both temporal and spatial dimensions. At the spatial level, as shown in Figure 2, poultry trade activities are widely distributed across the country. Intercity flows with trade volumes exceeding 20,000 tons are found in most provinces, and showed a clear increasing trend during 2010–2022 (as indicated by the increasing number of red flow lines representing trade volumes above 20,000 tons).

Meanwhile, as shown in Figure 3a, there is a significant distance decay effect in intercity poultry trade flows, with approximately 80% of the trade volume concentrated within 180 km and 90% within 280 km. Further analysis of the urban poultry trade network using complex network methods reveals that, during 2010–2022, both the network density and the clustering coefficient of China’s urban poultry trade network exhibited clear upward trends (Figure 3b). Specifically, the network density increased from 0.0017 to approximately 0.0040, an increase of about 135%, reflecting a sustained strengthening of connection intensity among cities, with increasingly close and diversified trade linkages. The clustering coefficient rose from 0.025 to 0.08, an increase of approximately 220%, indicating that the regional clustering characteristics within the trade network have been continuously reinforced, and that the trade connections between node cities and their neighboring cities have become increasingly rich.

In summary, over the study period, the scale of poultry trade at both city and provincial levels in China continued to expand, with short‑distance flows dominating intercity trade, and both the clustering and network density of the poultry trade network shown an upward trend.

Figure 2  Maps of the intercity poultry trade network in China (2010–2022) (trade volume > 500 t)

Figure 3  Characteristics of intercity poultry trade network of China

5 Discussion and Conclusion

This study collected city-level poultry consumption and production data and, by applying the modified radiation model, obtained a long‑term poultry trade network of China, providing a reference for studies on poultry trade and even avian influenza transmission.

The findings are as follows: (1) During 2010–2022, China’s poultry supply‑demand pattern exhibited consumption‑driven phased fluctuations over time, while production growth remained relatively moderate. (2) At the city scale, the scale of poultry trade continued to expand, with short‑distance flows dominating; both the clustering and network density of the resulting poultry trade network increased, revealing a pattern of dense in the east and sparse in the west, short‑distance dominance, and enhanced connectivity.

This dataset reflects the spatiotemporal changes of China’s poultry trade pattern from 2010 to 2022, and provides scientific data for optimizing industrial layout, ensuring meat supply security, and establishing a cross‑regional joint prevention and control system for epidemics that centers on poultry trade regulation while taking regional differences into account.

Author Contributions

Ren, H. Y. designed the overall framework of the dataset. Ren, H. Y. and Xu, N. K. collected and processed the data, and designed the models and algorithms. Ren, H. Y. and Wang, Q. performed data validation and visualization, and drafted the data paper. Ren, H. Y., Wang, Q. and Xu, N. K. completed the revision and improvement of the dataset and data paper.

Conflicts of Interest

The authors declare no conflicts of interest.

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