Mapping landscape in Longshan period’s hierarchical society (3000–2000BCE) of North Loess Plateau: from archaeological predictive model to GIS spatial analysis

Abstract

On the North Loess Plateau of China, city civilization, social complexity, and stratification emerged during the Longshan period (3000-2000BCE). Based on Geographic Information System (GIS) analysis and archaeological predictive model, we conducted a comparative analysis of environmental characteristics between sites and non-sites, ordinary and walled city sites, as well as large and smaller city sites. Initially, we developed a Binary Logistic Regression (BLR) model to predict the locations of archaeological sites from this period. Our findings indicate a high predictive accuracy of the model, demonstrating a clear environmental preference by the people of the Longshan Period. The presence or absence of the site was found to be influenced by various factors, including temperature, elevation, river distance, and precipitation. Furthermore, we discovered that walled cities had higher environmental requirements compared to ordinary sites. Terrain and land use played a more significant role in shaping prehistoric cities than climate. Lastly, the landscape in the Shimao site, which served as a most crucial and largest settlement centers on the North Loess Plateau (NLP), resembled that of other minor walled cities. Due to its abundant grassland, Shimao relied more on animal husbandry rather than agriculture. The combination of agriculture and animal husbandry has promoted the urbanization processes.

Introduction

The Longshan culture, a late Neolithic cultural type, was widespread in the middle and lower Yellow River regions, which can be divided into two periods based on cultural characteristics: the early Longshan culture (3000–2500 BCE) and the late Longshan culture (2500–2000 BCE). In ancient Chinese literature, this period is referred to as the age of 'ten thousand states,' indicating a society of chiefdoms [1]. During this time, population density increased, leading to settlement differentiation and social stratification. The Longshan period exhibited evidence of complex social organization, urbanization, specialized crafts and technologies, including bronze metallurgy, jade carving, standardized pottery tiles, and competitive interaction [2]. Regional centers emerged, constructing walls with political, religious, and economic functions.

Previous research has indicated a significant cultural advancement during the Longshan period on the NLP [3]. The number of Longshan cultural sites has notably increased compared to the preceding Yangshao culture (ca. 5000–3000 BCE). Archaeologists believe that the increase of the site number reflects population growth and cultural prosperity under favorable climate conditions. While previous studies have explored the spatial distribution of sites in relation to factors like altitude and distance to the river [4], no research has yet been focused on the connection between human activities and comprehensive geographical landscapes during this time. The use of spatial analysis method in GIS has become popular for analyzing the relationship between human activities and landscapes, and has been successfully applied in various regions such as Eastern Iberia, Crete, Romania, India, and Mongolia [5,6,7,8,9]. The academic community is now focusing on a wider range of landscape factors, including water flow, humidity index, and soil quality [10]. These successful research endeavors also inspire us to investigate the natural foundations of the cultural prosperity during the Longshan period.

Archaeological predictive models have been widely used, particularly in the study of prehistoric period sites [11,12,13,14,15,16]. In the past, numerous models were employed in Cultural Resource Management (CRM), but in recent years, there has been a growing focus on the crucial role of models in landscape simulation and research [6, 17,18,19]. There are two methods for constructing an archaeological predictive model.

The first method is known as "inductive" or "data drive." Typically, BLR or MaxEnt statistical methods are utilized to determine the correlation between known archaeological sites and environmental variables [20, 21]. Although both MaxEnt and BLR models require the removal of strongly collinear independent variables, there is a difference in how they handle the following process. Once entered into the model, the MaxEnt model does not screen the independent variables, whereas BLR can further screen the independent variables based on statistical results. It removes factors that are not significant relation to the dependent variable. As a result, the BLR method provides a more concise final result.

The second method, known as 'deductive' or 'theory-driven,' involves the use of Artificial Intelligence (AI) and fuzzy logic models. AI models, such as one-class classifiers, have shown promise in detecting buried archaeological sites [22]. However, this method does not provide a quantitative relationship between sites and environmental variables. Fuzzy logic systems can be used to analyze settlement preferences [23]. But this approach involves a complex calculation process, particularly when establishing fuzzy sets and selecting parameters [10]. The final result is influenced by the expert's experience. The weight overlay method is employed to establish models in GIS and the weight of each variable is determined based on the knowledge or assumptions of experts [24]. In conclusion, most of the current deductive models rely on expert knowledge, which may introduce subjectivity and uncertainty.

Using the BLR model, this paper aims to address the following questions based on the high predictive accuracy: What were the landscape preferences of the Longshan people? Which environmental factor has the most significant impact on the site-present or absent?

Archaeological background

Existing archaeological site prediction models and GIS spatial analyses often overlook the inherent differences among sites, treating them as equal entities. However, during the Longshan period, the society on the NLP experienced increasing complexity and the emergence of a hierarchical structure. By 2300 BCE, the region had established complex trade networks and hierarchical social systems, accompanied by the increase of settlement number [1]. These sites can be categorized into at least three levels. Based on the presence or absence of stone walls, the sites can be divided into walled cities and ordinary settlements. The latter are generally smaller in size, suggesting that the walled enclosures reflected a degree of regional importance. Walled cities are further classified as either large central city sites Shimao or small to medium-sized city sites, based on site size and the quantity and value of unearthed artifacts. The recently discovered Shimao site (400 ha, ca. 2300–1800 BCE), a Neolithic walled city on the NLP, has sparked significant interest in academia regarding the Longshan period's social organization, hierarchy, and human-nature relationship [25,26,27,28]. It was not only one of the most crucial and largest settlement centers of the NLP but also in northern China. The presence of numerous exquisite jade artifacts, large stone statues, bronze ware, bone and pottery indicates that it was likely served as a civilized center with political, economic, and religious functions [29, 30].

Around Shimao, there are numerous smaller walled cities of various sizes situated on hilltops, alongside a significant number of ordinary settlement sites. This paper aims to examine the environmental variables of this hierarchical social structure. Specifically, our study aims to analyze the environmental factors that contribute to the presence of walled cities and determine if there are any notable distinctions between these cities and ordinary sites. Furthermore, we seek to investigate if spatial data in GIS can unveil significant environmental differences between Shimao (the mega city) and the intermediate or minor city sites.

Data and methods

Study area

According to administrative boundaries, the NLP spreads over two regions: the Yulin Region and the Yan’an Region. This article focuses on Yulin Region, which is the northernmost region of Shaanxi. Yulin is confined by 36°57′- 39°34′N latitude and 107°28′–111°15′E longitude. The area covers 43,113 km², accounting for 45% of Shaanxi region. The main watersheds in Yulin include the Kuye, Wuding, and Tuwei River and the areas of these three river basins are 8706km², 30260km² and 3294km², respectively. There are higher altitudes in the northwest compared to the southeast, with an average altitude ranging from 650 to 1500 m (Fig. 1). This area exhibits two types of geomorphology. The terrain in the northern portion of the aeolian plateau is characterized by its flatness, while the river valley in this area is broad but relatively short. The landscape primarily consists of fixed and semi-fixed sand dunes, interspersed with saline-alkali beaches and lakes. Moving towards the southernmost edge zone, mobile sand belts can be observed. On the other hand, the terrain in the southern loess hilly region displays a high relief, featuring dense gullies and alternating loess ridges and loess tablelands. This area is a transition zone between monsoon and non-monsoon regions. The annual average temperature ranges from 8 to 12 °C, and the annual mean precipitation is between 350 and 600 mm, mostly occurring in summer [31].

Fig. 1

A Location of the study area, China. B Map of the study area and the 270 random selected sites are shown. C Ten walled sites of Tuwei River Basin discussed in the paper

The regression model

Binary Logistic Regression (BLR) has been utilized to establish the statistical relationship between independent variables and the dependent variable [32]. It is useful where the dependent variable is dichotomous (e.g., succeed/fail, live/die, site-present and site-absent). Assumptions and statistical requirements are not strictly adhered to. Normality of the variables is not necessary. However, the independent variables in the model should be non-collinear. In this study, the collinear test was conducted by the SPSS21 software before running the model (Additional file 2: Table S1). In comparison to other methods used for assessing relationships between predictive and dependent variables, BLR has shown reliability [33].

Dependent variable

The BLR model requires two types of location-specific input data: site-present and site-absent data. The first type can be obtained from known archaeological sites, while the second consists of random points generated by ArcGIS10.2 software. For this study, a total of 338 Longshan period sites have been incorporated into the GIS system. The dependent variable comprised 270 (80%) of the 338 sites with accurate location data and 270 random points were taken as the non-site locations. The model was then tested using the remaining 68 known archaeological sites and the same number of random points.

Predictive variables

Table 1 provides detailed information about the variables used in the model. The Aster DEM data with a resolution of 30 m was downloaded from the https://www.gscloud.cn/. This data was utilized to generate variables such as elevation, slope, relief (within 300 m), and aspect. The annual average precipitation, temperature data with a resolution of 500 m, and land cover data with a resolution of 1 km were obtained from the National Earth System Science Data Infrastructure of China. The river data was digitized from the topographic maps. Furthermore, the Distance to the river variable was calculated based on Euclidean distance tool in ArcGIS10.2. The Vegetation cover variable was computed from the Landsat 8 image using ENVI 4.8 software. All data was resampled to a resolution of 1 km. Over the past few thousand years, the geological background of this region appears tectonically stable with a single material composition, namely, loess. Thus, rock uplift has limited influence on landform evolution on the NLP. The erosion process dominated the landform evolution process during this period [34]. Although the soil erosion changed the specific values of elevation, slope, and aspect subtly. For example, the downward erosion rate was 0.2 cm/ka in the NLP [35]. It would not change the landform’s spatial distribution pattern. Therefore, the landform features put into the model was extracted from the modern DEM data. However, the reconstructed 0.5° × 0.5° mid-Holocene temperature and precipitation data are not precise enough for small-scale regional studies [36]. Therefore, the paleoclimate raster data was derived by subtracting climate fluctuation values from modern data. The detailed reconstruction processes refer to supplementary materials (Additional file 4: Text S1 and Additional file 3: Table S2).

Table 1 Variable names and descriptions

The environmental variables comparison of different social hierarchy sites

Fifty ordinary sites and 50 walled sites were randomly selected from the 338 sites using the ArcGIS10.2 software. To ensure reliable statistics, the buffer zone concept was often applied to environmental analysis of archaeological sites [37]. The average value of the environmental data within a 5-km radius of these sites was calculated. The calculated results were then exported to SPSS 21 for descriptive statistics analysis. Then, the potential environmental differences between the walled cities and the ordinary sites were examined. To further demonstrate the environmental disparities related to social hierarchy, the paper focused on the Tuwei River Basin, where the largest site, Shimao, is located. The precise geographic locations of ten walled cites of this area were obtained from published sources [29]. Subsequently, we compared the environmental variables of Shimao with those of the middle and small walled sites.

To enhance the understanding of the relationship between land use patterns and the distribution of sites among various social classes, this study has integrated more detailed land classification data. As there is no available prehistoric land use data, the 30-m resolution land classification data from China in 1985 was utilized. It is important to note that significant efforts have been made since the 1950s to manage and reclaim sand land in the local area. Through the construction of numerous irrigation channels, previously barren land has been transformed into cultivable land. In order to minimize the impact of human activities, we have employed a model to modify the cultivated land in 1985 and have also revised the distribution of cultivated land and sandy land in another submitted paper [38]. This revised land use data can serve as a substitute for prehistoric land use, which was less influenced by human activity.

Results

Model output: human landscape selection preference

To identify the environmental variables that would have a significant effect on the model, potential variables were screened in SPSS21 (Table 1). The backward elimination (wald) method was used to eliminate the least significant variable at each iteration. A significance level (Sig.) of 0.1 was set when testing the variable's correlation using Wald statistics [33]. The retained variables and their correlation coefficients are used to establish the model (Table 2). B represents the partial regression coefficient and S.E. represents the standard error. Wals is used to assess the impact of the independent variable on the dependent variable. A higher Wals or a smaller corresponding Significance (Sig.) indicates a more significant impact. Sig. denotes the significance level, represented by the P-value. Df represents the degrees of freedom. EXP (B) represents the odds ratio (OR), which quantifies the extent of an independent variable's effect on the dependent variable.

Table 2 Significant environmental variables identified as being associated with site location

The formula is as follows:

$${\text{Z}} = - {1}.{567} + \, \left( {{\text{Precipitation}}*0.00{8}} \right) \, + \, \left( {{\text{Elevation}}* - 0.00{4}} \right) \, + \, \left( {{\text{Temperature}}*0.{4}0{5}} \right) \, + \, \left( {{\text{Distance }}\left( {1} \right) \, * - 0.{292}} \right) \, + \, \left( {{\text{Distance }}\left( {2} \right) \, * - 0.{8}0{3}} \right) \, + \, \left( {{\text{Distance }}\left( {3} \right) \, * - 0.{578}} \right) \, + \, \left( {{\text{Distance }}\left( {4} \right) \, *0.{116}} \right) \, + \, \left( {{\text{Distance }}\left( {5} \right) \, * - {18}.{786}} \right)$$

The following equation was used to convert the regression output to a probability score [24] and a predictive map was generated by this equation (Additional file 1: Figure S1). Here, e represents the base of the natural log system, Z is the optimal linear fit result of predictive variables.

$${\text{Prob}}\left({\text{S}}\right)=\frac{1}{1+{e}^{-z}}$$

After evaluating the accuracy using two methods, it is shown that the model demonstrates high prediction accuracy (Additional file 5: Text S2) and can effectively explain the environmental preferences of human sites. Out of the ten variables initially included in the database, only four variables have demonstrated statistical significance: Temperature, Elevation, Precipitation, and Water Distance. Both Temperature and Precipitation exhibit positive coefficients, indicating that areas with higher temperature and precipitation values are more likely to contain a site. Specifically, a one-unit increase in temperature leads to a 1.5-unit increase in the probability of site presence (Table 2). The impact of precipitation, however, is relatively smaller compared to temperature. Additionally, there is a slight decrease in the probability of site presence as elevation increases.

The relationship between site presence and the Distance to the river is complex. The Distance to the river data was divided into six distinct classes: ≤ 500 m, 500–1000 m, 1000–2000 m, 2000–5000 m, 5000–10,000 m, and ≥ 50,000 m. In the model, this variable was taken as a dummy variable which are created to represent each category, allowing for easier interpretation of regression results [39]. In this case, the reference category is the distance between 0 and 500 m. The influence of other categories on the probability of site presence were compared to this reference level. Table 2 shows that only Distance (2) (1000–2000 m) significantly differs from the reference level. This suggests that the probability of site presence decreases as the distance from water increases. The last category (≥ 50,000 m) has a small number of observations data, which may explain why the difference from the reference level is not significant. However, it is known that there are no site-present instances within this distance range. Therefore, it can serve as a useful indicator for predicting non-site locations.

The regression model is not significantly affected by the variables that were removed. To evaluate the relationship between site distribution and the removed variables, we plotted the frequency distribution of known sites in the training set across various environmental variables. The distribution of the sites is skewed towards low-slope and low-relief regions. Additionally, a significant proportion of sites face east and south (67.5–247.5). The vegetation cover of known locations is primarily centered on Class 2 and Class 3 (10% < FVC < 30% and 30% < FVC < 45%). The sites are located in both farmland and non-farmland areas (Fig. 2), and there are also numerous non-site points within the farmland. Although these variables play a role in determining the site's location, they are not essential for the site's existence. There is no statistically significant difference between sites and non-sites in terms of these environmental factors, leading to their exclusion from the model (Fig. 2).

Fig. 2

Frequency distribution maps of known sites for the eliminated variables

Through a comparative analysis of the values of two environmental variables, namely the variable included in the model and the variable excluded, for both site and non-site locations, we can determine which variable has a greater discriminatory potential. To illustrate this, we take temperature (the input variable) and slope (the removal variable) as the example. And the variable values obtained from site and non-site were compared. While there is some overlap in temperature values between sites and non-sites, the majority of non-sites have significantly lower temperatures compared to sites (Fig. 3). Therefore, temperature can effectively differentiate the two groups. A significance test was performed using SPSS21 software, which showed a significant differentiation between sites and non-sites (Table 3). On the other hand, the slope values of sites and non-sites completely overlap, making differentiation these two categories unattainable. Similarly, other variables have been excluded from the model for the same reason.

Fig. 3

Scatterplots of Temperature and Slope values extracted for the site and non-site location

Table 3 Temperature Independent Samples T-Test for sites and non-sites

Environmental variables comparison on the walled city and ordinary sites

During the Longshan period, some sites became fortified with stone walls. The inter-regional climatic change, population pressure, and intergroup conflict perhaps have caused the construction of these walled cities [1]. The locations of 50 walled sites from the Longshan period were collected. These sites are usually located on hilltops near rivers, and stone walls were constructed on the top of natural cliffs to protect the settlements. The walled sites were often surrounded by smaller non-walled sites, indicating that these fortifications may have protected for the people of the surrounding area [1]. Therefore, these stone-walled sites have a crucial role in society. Here, we will investigate what types of environmental conditions supported the prehistoric walled city system and whether they differed from ordinary Longshan sites. For comparison, these environment data were divided into three categories (climate, terrain, and land use) and Excel bar graphs were generated (Fig. 4).

Fig. 4

Environmental variables comparison between the walled city and the ordinary sites during the Longshan period

The walled city and ordinary sites have minor differences in temperature and precipitation. The former has a slightly higher mean temperature of 0.11 ℃ and lower precipitation of 0.99 mm. In terms of terrain factors, the walled city sites have lower elevation and shorter distance to the water compared to the ordinary sites. However, the walled city has higher slope and relief, which is advantageous for defense. Additionally, walled cities prefer southern-facing locations, while ordinary sites prefer north-facing locations, indicating significant aspect differences. The preference for south-facing locations may be due to factors such as better solar exposure, warmer climate, or cultural beliefs. All these factors suggest that larger sites, particularly city sites, have stricter criteria for location selection compared to ordinary sites (Fig. 4). This is because city sites serve multiple functions, including political, economic, and military defense. As a result, the duration of city sites is extended, and environmental factors carry more weight in site selection.

The land use of various site types demonstrates significant diversity. Both walled cities sites and ordinary sites have a proportion of cropland area, with the former having a higher proportion. Additionally, walled city sites have more abundant grassland resources, which were considered valuable for farmers or nomads. It is worth noting that some ordinary sites are located within 5 km of deserted lands. Despite a short climate reversal during the Longshan period, we can conclude that these locations were not the best choice for the sites due to their susceptibility to desertification. The cultural layers of several Longshan sites are situated just above the desert, indicating that the ordinary sites were sometimes located in suboptimal areas for survival. These places might have served as temporary dwellings with relatively low environmental requirements. Furthermore, out of the ten walled city sites in the Tuwei River Basin, two were surrounded by broad-leaved forests, while no forests were found around the 50 ordinary sites. This suggests that the presence of forests was a deliberate choice made by humans, who utilized wood as a source of fuel, building material, and tools.

Environment variables comparison between the largest city site Shimao and the other walled cities

The recently excavated Shimao site has uncovered a highly advanced city center in the NLP, previously considered a fringe region of Chinese civilization [29, 30]. The emergence of the first civilizations often took place in areas with favorable geography for intensive agriculture [40,41,42]. These civilizations originated from agrarian communities that produced sufficient food to support cities, leading to the development of social hierarchies based on factors such as gender, wealth, and division of labor. In order to demonstrate the hierarchical structure of Longshan society, we conducted a comparison of environmental data between Shimao (SM) and other walled cities. However, no significant differences were found in these variables (Fig. 5).

Fig. 5

Environmental variables for the 10 walled cities

Discussion

Settlement preference, the subsistence strategies and the war

The use of an archaeological predictive model allows for the identification of site location ‘s environment preferences. Despite the potential background noise caused by diverse environmental landscapes, this model enables us to explore the relationship between landscapes and human environmental preferences. The BLR model demonstrated that people in the NLP relied heavily on several key factors including Temperature, Distance to river, Precipitation, and Elevation when faced with diverse environmental conditions. By analyzing the predicted map of archaeological sites, a clear division between high and low probability areas can be observed (Additional file 1: Figure S1). The eastern part of the study area shows a high potential for archaeological sites. It was found that areas with higher average annual temperature, closer proximity to water sources, higher precipitation, and lower altitude were considered as optimal environments for human site selection.

The model excluded variables such as Aspect, Slope, Farmland, and Vegetation cover due to their limited ability to distinguish between sites and non-sites. During the Longshan period, many ordinary residences were partially underground and may not have required significant heating sources, thus eliminating the Aspect factor. The relationship between vegetation coverage and human site selection is intricate, and further research is necessary to fully understand it. Moreover, the model assumes that agriculture was the primary focus during the Longshan period, implying a strong correlation between arable land distribution and the existence of sites. Additionally, slope should also be a significant factor limiting human survivability, since steeper slopes are not conducive to agricultural production. However, the model's results do not align with the expected outcomes, which suggests that human survival was not significantly impacted by the slope factor during that period.

First, this discrepancy could be attributed to the location of the study area in the ecological edge zone, where the influx of cattle and sheep has led to changes in traditional production methods and the emergence of animal husbandry as a predominant activity [43, 44]. Previous studies have also indicated that compared with Yangshao culture (5000–3000 BCE), there was a tendency for the population during the Longshan period to migrate towards areas with steeper slopes [45]. This suggests that human living spaces expanded beyond flat agricultural areas, with the utilization of mountain and grassland resources to enhance animal husbandry. The presence of milk collecting at multiple Longshan period sites in the research region further supports this notion [46, 47]. The diachronic statistical analysis of ancient human bone collagen carbon isotopes in Shaanxi, Shanxi, and other regions reveals a peak followed by a decline approximately 4500–4000 years ago. This suggests that the shift in carbon isotope values was more likely due to the consumption of C3-fed beef, lamb, and milk rather than the introduction of wheat [48, 49]. These protein sources would have enhanced the nutrition and fitness of our ancestors, contributing to their survival and the resilience of human society. The coexistence of agriculture and animal husbandry has played a crucial role in stimulating economic development and maintaining cultural prosperity within a relatively short period of time.

Second, steep slopes are a very good natural defense system [37]. The sites are usually located on hilltops near rivers, and stone walls were constructed on the top of natural cliffs to protect the settlements. The construction of these fortifications may have been the result of intergroup conflict, which was at least partially caused by climatic fluctuation and deterioration of the ecosystem, coupled with population pressure [1]. Shimao, a significant settlement center, encompassed a range of defensive structures in its eastern gate. These structures included a well-protected gate with guardhouses, two gate towers, and curtain walls with bastions and a corner tower [29]. The study in the Plateau-plain Transition Zone of NE Romania also indicated that prehistoric communities preferred to place their settlements for defensive purposes on hilltops, or in the close proximity of a steep slope [37]. It can be seen that the ecological transition zone was the forefront of conflicts between different groups of people, and wars occurred more frequently. Therefore, the landform of settlement location broke through the distribution rule of traditional agricultural sites.

The drivers of early urbanization and Shimao’s environment preference

The emergence of a mixed agriculture and animal husbandry economy has contributed to the process of urbanization. As the population in Inner Mongolia migrates southward, there has been an increase in population pressure on the NLP area [50, 51]. This population growth has resulted in intensified competition for resources, greater social complexity, and the development of cities with defensive functions. Previous studies have associated urbanization with advancements in agricultural technology [42, 52]. Improved agricultural techniques have led to increased food production, enabling the support of larger populations and driving urbanization. However, during that time, agricultural production in the NLP region was low and unstable. Based on ¹⁵N enrichment, research indicated a significant decline in millet farmlands in the study area from the late Neolithic to the early Bronze Age (5000–3000 BP) [48]. The introduction of animal husbandry has allowed the region to sustain a larger population and increased human resilience to natural disasters. This suggests that urbanization was not limited to the most developed agricultural regions. As long as there was sufficient means of subsistence, even ecological margins could undergo urbanization.

In terms of agricultural production, the Shimao site does not possess any environmental or resource advantages compared to smaller walled cities. However, archaeological excavations have revealed that the Shimao site held the highest social status, indicating that social stratification was more evident in social relations rather than in agricultural production. The significant proportion of grassland of Shimao provided a basis for the development of a mixed pastoral-agricultural economy, as the growing population faced the challenge of limited land resources. The introduction of cattle and livestock from the west further supported the animal husbandry industry, enabling the grasslands to produce meat and milk for human consumption. The discovery of a substantial number of bone needles in the city suggests that the inhabitants were skilled in crafting bone objects, such as needles for sewing animal hides [53]. This new subsistence strategy enhanced productivity by reducing dependence on agriculture and laid the economic foundation for the growth of major cities. It also implies that agricultural food in large cities or centers of civilization may not have been produced independently. During this period, the trade network likely played a crucial role in supplying the needs of the Shimao people. Many grassland communities in Chinese history adopted this model and relied on international trade for essential production materials. However, frontier cities faced greater challenges in achieving sustainable development compared to cities in the Central Plains, which enjoyed better environments and abundant resources.

Factors that impact the model accuracy

In archaeological landscape research, researchers typically focus on topographic and hydrological variables [22, 54, 55]. However, climate factors are rarely incorporated into the model due to the lack of available paleoclimate grid data. This paper aims to address this gap by utilizing quantitative paleoclimate data which was reconstructed from modern raster data by subtracting the difference between past and modern. Currently. This method is still relatively simple. In the future, collaboration with climate simulation scholars is necessary to develop regional paleoclimate grid data and enhance the model's accuracy. Verhagen et al. have identified various issues related to the predictive accuracy. These issues include the relevance of environmental input data, the lack of temporal and/or spatial resolution, the use of spatial statistics, the testing of predictive models, and the need to incorporate social and cultural input data [56]. Taken the environmental input data as an example, the relationship between the spatial resolution of environmental input data and predictive accuracy has yet to be thoroughly examined. Excessively high spatial resolution can impact overall operational efficiency and require more advanced computer hardware. On the other hand, if the resolution is too low, it may result in the smoothing out of spatial differences in environmental variables. In addition, due to the static nature of the point data, the model cannot take into consideration the small-scale temporal dimension of human behavior and movements [57]. In conclusion, to improve the predictive accuracy of the model, it is essential to enhance the research on the input data.

Conclusions

This study presents a statistical and predictive approach based on GIS for analyzing the relationship between humans and their surroundings. Using the BLR model, this research reveals that Temperature was the most significant factor in determining the presence or absence of sites. Distance to water, Precipitation, and Elevation were also found to be influential factors. Other variables were excluded from the analysis as they had limited ability to distinguish between sites and non-sites.

The Longshan period was characterized by high social complexity. In order to understand the hierarchical structure of this period, this paper analyzed the differences in environmental variables between walled cities and ordinary settlements using GIS and statistical tools. The analysis reveals that middle/small walled city sites were preferred over locations with more favorable terrain and land resources. This suggests that once the minimum threshold for site presence was met, climate conditions were no longer the primary factor for city site selection. However, unlike smaller walled cities, the Shimao site, which was one of the largest prehistoric city centers in north China during the Longshan period, did not have a larger amount of cropland available in its vicinity. This suggests that Shimao may have served as a center for military defense, rituals, or commerce, rather than as an agricultural production center.

Unlike the urbanization process supported by agriculture in the plain region, urbanization in border areas has emerged due to population pressure and the introduce of animal husbandry. Diversified subsistence strategies compensated for the shortage of agricultural production and ensured a stable economic income. This production mode has laid the foundation for the development of grassland cities throughout history.

In conclusion, this study conducted a quantitative analysis of the environmental context for site presence and city development. Additionally, the analysis of environmental variables offered valuable insights into the hierarchical structure of the social system. In the future, it is crucial to conduct more comprehensive investigations on the data of the input model. This will not only enhance the predictive accuracy but also deepen our overall understanding of the relationship between humans and nature.