Analysis on Spatial Characteristics of Supply–Demand Relationship of Amenities in Expanding Central Urban Areas—A Case Study of Huai’an, China

Abstract

Supply and demand for amenities in expanding urban areas undergo continuous adjustment. In this study, we explored the relationship between supply of and demand for amenities in expanding urban areas to evaluate the rationality and effectiveness of amenity distribution, improve urban governance, and promote urban accessibility and justice, thereby facilitating sustainable urban development. We selected the central urban area of Huai’an City, China as the research area. We used point-of-interest data, Baidu population heatmap data, and residential housing prices to develop supply and demand indices for amenities and analyze the spatial characteristics of the supply of and demand for amenities in expanding urban areas. The results revealed that (1) the supply–demand relationship in the study area was mainly balanced, followed by relationships in which supply was lower than demand, and the relationships of supply exceeding demand is less, accounting for 68.35%, 26.06%, and 5.59%, respectively. (2) The core of the old urban area and the areas surrounding the old urban area had high supply–demand balance, the transitional and new urban areas had less supply than demand, and the developing urban areas had low supply–demand balance; the supply–demand relationship of amenities followed a clear circular distribution pattern. (3) The Chinese government’s continuous renewal of old urban areas has made the quantitative proportion of supply and demand components be positively correlated with the oldness of urban development, which has enlightening effects on the construction of other cities. Finally, we developed suggestions for adjusting the distribution of amenities in the central urban area of Huai’an to facilitate high-quality development in this area.

1. Introduction

Since the Industrial Revolution, cities have become the main centers of human life and productivity. However, the expansion of cities has been associated with major problems, including environmental pollution, traffic congestion, and high crime rates. Therefore, the quality of urban life has attracted considerable attention from researchers, especially since the rise of amenity research in Western developed countries in the 1950s. Ullman [1] defined urban amenities as an enjoyable living condition and reported that the establishment of amenities and their tendency to attract people were the key incentives for the development of the Sun Belt in the United States. Smith [2] defined urban amenities as the various facilities and environmental conditions (such as green spaces, arts and culture events, bars, restaurants, and historical architecture) that are unique to an urban area and attract people to live and work in that area by making them feel comfortable and pleasant. Gottlieb [3,4] regarded amenities as goods or services that are specific to local residents or pedestrians and cannot be exported. Luger [5] viewed urban amenities as composite “products” that cities produce by using public infrastructure and public sector workers as capital and labor. Feng [6] defined urban amenities as special commodities, services, and environments that are enjoyable and are essential factors for urban comfort in terms of environmental quality, public security, education quality, and health services. Zhou [7] divided urban amenities into infrastructure comfort, public health, and ecological and humanistic environment comfort. In brief, urban amenities are public products, including tangible facilities and commodities as well as intangible environmental conditions and public services, that satisfy people’s needs.

Urban amenities strongly affect where people decide to live and work [8,9] as well as where enterprises decide to establish production facilities [10]. It also affects housing prices [11,12], employment opportunities [11,13], and even population growth [14] in cities. Gaigné et al. [15] stated that household income sorting is driven by amenities. According to Naldi et al. [16], local amenities are key determinants of the rate of new firm formation. As cities have continued to grow, human-made urban amenities have replaced natural urban amenities and are playing a greater role in public policy development [17]. Urban amenities as related to leisure entertainment and consumption are receiving increasing attention from researchers [18,19]. Since the 1990s, with the rise of globalization and knowledge-based economies, human capital has gradually become the key productive force for urban and regional development [10,20]. The high quality of life and social welfare brought about by urban amenities have attracted creative and high-quality talent and indirectly promoted urban development [21,22,23].

With the increasing attention paid to urban amenities, the scope of urban planning research has expanded to encompass analysis of the spatial accessibility of urban amenities and spatial differences in urban amenities and demand for amenities. Most studies of the spatial accessibility of urban amenities have aimed to facilitate reasonable allocation of various public facilities [24,25,26,27,28] to, in turn, facilitate the sustainable development of urban living environments. Most studies of spatial differences in urban amenities have been related to urban functions and structures [29]. Tivadar [30] reported that the evolution of the city’s structure is strongly related to the generation and the valuation of urban amenities. Wang [31] inspected the relationship between urban polycentricity and the supply of urban amenities in 309 cities in China and discovered that cities with higher polycentricity had greater numbers and a greater diversity of urban amenities. Studies of supply–demand for amenities within cities have focused mostly on facility distribution, spatial equity, and social welfare standards under market conditions [32,33] (e.g., differences in amenity demand due to resident attributes such as gender, age, and income [34]).

The development of information technology and big data with higher spatial and temporal granularity has enabled researchers to effectively examine the spatial characteristics of the supply–demand relationship as related to urban amenities [35,36]. Microscale urban amenities from the perspective of supply and demand will be the focus of future research [37]. Thus, it is necessary analysis of urban amenities deeply by applying big data. Today, the populations and urban lands of many cities in Asia, Africa, and Latin America continue to grow, and it is also necessary to analyze the relationship of supply–demand of amenities in expanding cities. Therefore, in the present study, we used multisource big data and developed a supply–demand index to investigate the spatial characteristics of the supply and demand of amenities in expanding urban areas at different stages of development, using the central urban area of Huai’an City, China as an example. The results of this study may serve as a reference for the layout of facilities.

2. Materials and Methods

2.1. Study Area

Huai’an, which is located in eastern China on the Grand Canal, is a city with a rich history of over 600 years. Its total land area is 10,030 km², 4.56 million people live here, and a gross national product of 402.54 billion (CNY) was created in 2020. Over the past 20 years, Huai’an has developed into an ecotourism destination, the key city in the northern region of the Yangtze River Delta, a transportation hub, and a base for advanced manufacturing. The city has continued to develop rapidly in recent years, and urban construction in the area is still expanding. From 2000 to 2020, Huai’an’s built-up area increased approximately 5-fold from 40 to 204 km². Over the past 5 years, the growth rate of Huai’an’s built-up area remained above 3%. To scientifically control the development of the city, the Huai’an Municipal Government formulated the Huai’an Master Plan (2017–2035), which defined the scope of the central urban area. In this study, we selected the designated central urban area as the research area.

Huai’an is the city traversed by four waterways (Salt River, Ancient Huai River, the Li Canal, and the Grand Canal from north to south, see Figure 1). We divided the central urban area into the following four sections according to the age at which they were developed and constructed: (1) The section developed and constructed before 2000, which accounted for 16.04% of the study area. Because this section includes the old urban area centered on Huaihai Road and the old urban area of Chuzhou District centered on the ancient prefecture, we referred to this section as the old urban section. (2) The section developed and constructed from 2000 to 2010, which accounted for 21.27% of the study area. This section is mainly at the periphery of the old urban section, and we therefore referred to it as the transitional urban section. (3) The section developed and constructed after 2010, which accounted for 30.74% of the study area. This section is at the periphery of the transitional urban section and connects Huai’an City and Chuzhou District. We referred to this section as the new urban section. (4) The expandable urban section, which accounted for 31.95% of the study area. This section is expected to be developed in the future.

2.2. Data Acquirement and Preparation

(1) Baidu POI. According to the connotation of amenities and the basis of previous studies [38], we choose Baidu POI (Point of Interest) data from Baidu Map (https://map.baidu.com/, accessed on 15 August 2020) to represent the spatial distribution of amenity supply. We compiled a crawler program and collected the POI data by using the free interface provided by the Baidu application programming interface (API). A total of 354,496 POIs in Huaian were obtained. Among the Baidu POIs, there were 21 primary categories, 183 s categories; Baidu POI data include four main pieces of information: name, category, longitude, and latitude. According to the category of Baidu map POI data, 8 primary categories and 27 secondary categories (

Table 1. Classification of urban amenities.

Primary Category	Secondary Category	Quantity	Primary Category	Secondary Category	Quantity
Food	Chinese/Western/fast food restaurants	19,657	Education	Kindergarten/primary schools/secondary schools/training institutions	5536
Shopping	Shopping malls/markets/supermarkets/convenience stores	29,791	Life Services	Beauty salons/business offices/post offices	18,055
Finance	Banks/ATMs	647	Leisure	Parks/public squares/sports and leisure centers/entertainment venues	1871
Transportation	Bus stops/parking lots	3713	Health	Hospitals/clinics/pharmacies	3316

) were selected, with a total of 82,586 pieces of amenities data (see Figure 2a for the spatial distributions of the amenities).

(2) Baidu Heatmap. Baidu Heatmap data (from Baidu Online Network Technology Co., Ltd., Beijing, China) is the data presented for the number of online users using its products. Because of Baidu search, the Baidu map and other products have a huge amount of online users, and it can reflect the distribution of residents and was used for the study [39]. To determine residents’ mean demand for amenities, we extracted the Baidu heatmap data from between 10:00 a.m. and 10:00 p.m. on 15–17 April 2021 (April 15 and 16 were weekdays, and April 17 was a Saturday). The data reflected residents’ work, home, and leisure demands. All of the Baidu heat maps were acquired loaded into ArcGIS10.5 (GeoScene Information Technology Co., Ltd., Beijing, China), and the average value was calculated and divided into 7 categories by natural breaks (Jenks) (Figure 2b).

(3) Residential housing prices. Due to the differences in residents’ demand for amenities, we choose residential housing prices to reflect residents’ demand for amenities [37]. Octoparse software (Shenzhen Shijie Information Technology, Shenzhen, China) was used to retrieve the housing prices in each community area in Huai’an, and 1325 pieces of housing price data were obtained after data cleaning (Figure 2c).

2.3. Methodology

2.3.1. Supply Index and Demand Index

According to the concept of a 5-min living circle proposed in the “Standard for Planning and Design of Urban Residential Areas (GB50180-2018) “ issued by the Chinese government in 2018, combined with the walking speed of residents, 500 × 500 m grids are used as the basic unit of analysis. We constructed a grid of 500 × 500 m grids in ArcGIS10.5 (GeoScene Information Technology Co., Ltd., Beijing, China) and calculated the number and types of amenities, population (Baidu heatmap), and mean residential housing prices in each grid.

Sun [37] pointed out that the supply index is positively correlated with quantity of amenities, the number of types of amenities, and the resource energy level; the demand index is positively correlated with the population and consumption level. Wu [21] pointed out that the resource energy level is inversely proportional to the number of resources, which can be reflected by the reciprocal of the number of resources. Zhou [7] and Su [12] pointed out that house prices can reflect the consumption level of regions. Thus, the equations for the supply index and demand index for amenities were established as follows:

$$S_i=K_i{\displaystyle \sum }\left(Q_i\ast L\right)$$

(1)

$$D_i=H_i\ast P_i$$

(2)

In Equation (1), S_i is the supply index of grid i, Q_i is the quantity of amenities in grid i, Ki is the number of types of amenities in grid i, and L is the resource energy level (L = 1/Q_sum, where Q_sum is the total quantity of an amenity type). In Equation (2), D_i is the demand index of grid i, H_i is the population of grid i, and P_i is the mean housing price in grid i.

2.3.2. Supply and Demand Relations Matrix

To identify the spatial distribution of supply of and demand for amenities, we performed range normalization on the supply index S_i and demand index D_i to obtain values between 0 and 1. Grids with values were equally divided into low, medium, and high levels according to the order of the supply and demand indices. Grids without values were classified into the low level. The supply and demand levels were paired, and nine supply–demand relationships were obtained (

Table 2. Classification of supply and demand relations matrix.

Level	Low Supply	Medium Supply	High Supply
Low demand	Low supply/low demand (L–L)	Medium supply/low demand (M–L)	High supply/low demand (H–L)
Medium demand	Low supply/medium demand (L–M)	Medium supply/medium demand (M–M)	High supply/medium demand (H–M)
High demand	Low supply/high demand (L–H)	Medium supply/high demand (M–H)	High supply/high demand (H–H)

).

2.3.3. Average Nearest Neighbor

In addition, to assess the spatial agglomeration of amenities, we performed the Average Nearest Neighbor analysis and obtained the mean distance between amenities. We measured the distance between the centroid of each amenity and that of its nearest neighbor amenity. If the average measured distance $\overline{D_O}$ was less than the mean distance $\overline{D_E}$ in a hypothetical random distribution, the distribution of the corresponding amenities was determined to reflect spatial agglomeration. The equations used to calculate the average nearest neighbor are as follows [40]:

$$\mathrm{R}=\overline{D_O}/ \overline{D_E}$$

(3)

$$\overline{D_O}={{\displaystyle \sum }}_{i=1}^n{d}_i/n$$

(4)

In Equation (3), $\overline{D_O}$is the average distance between the centroid of the measured amenity and that of its nearest neighbor, and $\overline{D_E}$ is the average distance between random amenities. In Equation (4), $d_i$ is the distance between an amenity i and its nearest neighbor, and n is the quantity of amenities in the research area.

3. Result Analysis

3.1. The Spatial Distribution Characteristics of Amenities

The supply index reflected the quantity of amenities and the availability of different types of amenities. According to the supply index for all amenities and those for different types of amenities (Figure 3), the two agglomerations centered on the old urban areas of Huai’an City and Chuzhou District had high supplies of amenities. Yingzhong, Shuidukou, Shanyang, Eco-city, and College Town also had high supplies of amenities. The high-supply areas were arranged in both clustered and scattered distributions. Notably, although we identified dotted areas with high amenity supply levels between the two old urban sections, they did not form a linear amenity corridor connecting the two old urban sections. The medium-supply areas were mainly located at the periphery of the high-supply areas; and the low-supply areas were mainly located at the periphery of the medium supply areas in an annular distribution. We identified considerable differences in supply among different sections. In the old urban section, both the overall supply index and the supply indices for different types of amenities were high. The medium-supply areas were mostly located in the transitional and new urban sections; however, the number of low-supply grids in the grid was higher than the number of medium-supply grids, and the number of medium-supply grids was higher than the number of high-supply grids. The expandable section consisted primarily of low-supply grids.

The spatial distributions of different types of amenities varied. Life services amenities, which were distributed across 811 grids, had the widest distribution. Shopping and transportation amenities also had relatively wide distributions, distributed across 794 and 771 grids, respectively. Food and educational amenities, of which the distribution ranges were generally moderate, were distributed across 698 and 627 grids, respectively. The distributions of health, leisure, and financial amenities were concentrated. Financial amenities were distributed across just 164 grids, and 87.27% of them were located in the old urban section. The distributions of the supplied amenities were related to their characteristics. Residents had relatively high demand for small-scale shopping and food amenities (e.g., convenience stores and breakfast shops), educational amenities (e.g., kindergartens and training institutions), and transportation amenities and tended to seek out such amenities near their residences. Therefore, these amenities were widely distributed. The demand for leisure and financial amenities was relatively low, and residents were more likely to travel long distances to access such amenities. Therefore, the distributions of these amenities were more concentrated.

3.2. Spatial Distribution Characteristics of Demand for Amenities

The demand index reflected the number of residents in each section and their level of demand for amenities. Shanyang and Eco-city, which connected the old urban areas of Huai’an and Chuzhou District, constituted a high-demand area. In recent years, large-scale public amenities, such as the Huai’an Municipal Government, libraries, theaters, and high-quality primary and secondary schools, were built in Eco-city, have resulted in population agglomeration in the middle between the two old urban areas, thus promoting a strong connection between the areas. Chengnan and College Town, located in the area along the south bank of the Grand Canal, as well as Aoti and Egret Lake were also high-demand areas. The medium-demand areas were primarily located at the peripheries of the high-demand areas, whereas most of the low-demand areas were at the periphery of the central urban area. Residents’ demand for amenities followed a circular distribution pattern (Figure 4).

Demand for amenities differed significantly among different sections. High-demand grids comprised 79.53% of the grids in the old urban section. The middle- and low-demand grids were mainly distributed near the periphery of the old urban section. The areas to the west and south of the ancient prefecture and the northwest part of the main urban area were the peripheral areas of the city, these areas had slow urban renewal rates, old facilities, numerous low-rise buildings and relatively low housing prices; therefore, these areas had medium-to-low levels of demand. The area along the Grand Canal in the southeast of the main urban area had several factories, moderate living conditions, few residents, and relatively low housing prices, so this area had medium-to-low levels of demand. After 10–20 years of development, the areas in the transitional urban section were equipped with comprehensive amenities and thus fell in high demand. The high-demand areas were mainly distributed near the old urban section. The transitional urban section consisted of a developing zone and an industrial zone. Because the two areas were dominated by industrial activities, the numbers of residents in these areas were relatively small; these areas had moderate levels of demand. Most of the new urban sections had a medium level of demand; however, Eco-city and Aoti had high demand, especially Eco-city in Huai’an, which has a high population density and high housing prices. The areas to the south of the Grand Canal and Huanggang had medium demand, whereas Tonghuai and Huanlian, industrial areas, had low demand. Most of the expandable section consisted of low-demand areas.

3.3. Spatial Characteristics of Supply–Demand Relationship of Amenities

We discovered that spatial distributions of amenities varied substantially in terms of their supply–demand relationships. The areas centered on Huaihai Road, such as the main urban area (Chaoyang, Shuidukou, and Shanyang), the ancient prefecture, the urban area of Chuzhou District, Eco-city, Chengnan, and College Town, had high supply–demand balance. At the peripheries of these areas, demand exceeded supply. Urban–suburban areas and suburbs far from the urban areas had low supply–demand balance. Therefore, the supply–demand relationship of amenities followed a circular distribution pattern (Figure 5). In terms of quantity, the overall supply and demand for amenities were consistent with the supply of and demand of various types of amenities. The largest proportion of the sections had a high supply–demand balance, followed by sections in which supply was lower than demand. Balanced relationships and relationships in which supply was lower than demand accounted for 68.35% and 26.06%, respectively, of all the supply–demand relationships identified. Few sections had more supply than demand. Because the distribution of financial amenities was concentrated, the proportion of financial amenities with a supply–demand balance was relatively low (44.59%). Financial amenities were the only type of amenities for which the demand exceeded the supply in most of the studied sections.

The levels of supply–demand balance were categorized as high, medium, or low. Most of the areas had low supply–demand balance; these areas were mainly located in the urban-suburban areas and the expandable urban section in an annular distribution. Areas with a medium level of supply–demand balance accounted for the lowest proportion of areas in the grid; most of these areas were arranged in a dotted distribution pattern and located in the new urban section. Areas with a high balance were arranged in a clustered distribution and primarily located in the old urban section. We further categorized the relationships in which supply was lower than demand as low supply/medium demand, low supply/high demand, and medium supply/high demand relationships. Areas with low supply/medium demand were more widely distributed than those with the other two types of relationships; they were mainly located in the new urban section and at the periphery of the transitional urban section. The second most common type of relationship was medium supply/high demand, and areas with medium supply/high demand were concentrated in the transitional urban section. Low supply/high demand was the type with the large difference between supply and demand; areas with low supply/high demand were concentrated in Eco-city.

Among the various types of amenities, financial and leisure amenities followed clustered distribution patterns; therefore, the demand for these amenities exceeded their supply, and the supply of health amenities was also typically less than the demand for such amenities. Few grids were the type of more supply than demand, although the overall of amenities was the largest in this type, only accounted for 5.58%. Shopping amenities were the type for which supply exceeded demand in the largest number of areas; however, such areas only accounted for 1.41%. There was non-existent of supply exceeding demand in finance. In terms of spatial distribution, the areas in which supply exceeded demand were mostly distributed across the expandable and new urban sections. Typically, supply exceeded demand for two reasons. First, the government arranged the distribution of amenities in advance to increase the population in a certain area. Second, some certain areas have a strong potential to attract residents; therefore, amenities are established in such areas in advance to occupy the market share.

Supply and demand differed significantly among different sections. The supply of and demand for all types of amenities in the old urban section were highly balanced. Many residential communities in the old urban section have a history of more than 50 years. Nevertheless, the government has continued to renew and improve public infrastructure, which made the old urban section to turn into a livable area. Few areas in the old urban section had less supply than demand; these areas were primarily located by the Grand Canal and in the east of the ancient prefecture. In the transitional urban section, the greatest proportion of the areas had less supply than demand, followed by areas with high supply–demand balance, areas near the old urban section, such as Chengnan, Shanqian, Cherry Garden, and Fuqian Road had high supply–demand balance, the developing zone, the industrial zone, and the areas surrounding these zones had less supply than demand; few residential communities were located in these areas, and workers have less demand for amenities than do residents, which minimized supply–demand imbalance in these areas. In the new urban section, demand generally exceeded supply. Eco-city has numerous large-scale amenities such as new government office buildings, libraries, urban planning museums, high-quality schools, and hospitals as well as shopping malls, was a high supply/high demand area. Many high-quality communities in Aoti, Haunggang, and areas surrounding the high-speed railway were now under construction during the study period, and amenities were being established in these areas simultaneously. The supply of and demand for amenities in these areas are expected to increase considerably in the future. Tonghuai and Huailian were industrial areas and therefore had both low supply and low demand. The expandable urban section also had low supply and low demand.

4. Discussion

4.1. Spatial Distribution of Different Types of Amenities

Different types of amenities lead to differences in their spatial distributions. In terms of the characteristics of amenities [41], residents expected to travel different distances for different types and levels of amenities. For example, the residents generally had greater demand for transportation amenities that were nearby but were more willing to travel long distances for financial and leisure amenities. They required amenities such as breakfast stores and convenience stores to be nearby but were willing to travel longer distances for high-end shopping, food, and life services amenities. Therefore, financial and leisure amenities were clustered around city centers or densely populated areas, whereas transportation amenities were distributed more evenly. Shopping, food, and life services amenities were widely distributed, even within city centers.

We analyzed the distribution characteristics of the amenities by using Average Nearest Neighbor (

Table 3. The score of average nearest neighbor.

Type	Nearest Neighbor Ratio	Z-Score	Average Observation Distance	Expected Observation Distance	Type	Nearest Neighbor Ratio	Z-Score	Average Observation Distance	Expected Observation Distance
Finance	0.1765	−40.07	62.21	354.99	Health	0.3103	−75.97	58.21	187.60
Shopping	0.2203	−97.74	14.61	66.29	Education	0.3342	−94.77	48.92	145.77
Food	0.2379	−204.40	17.54	71.33	Leisure	0.4096	−48.85	93.10	227.31
Life Services	0.2749	−186.98	22.69	82.56	Transportation	0.5359	−54.09	96.13	178.27

) and discovered that different types of amenities had different spatial distribution patterns (Figure 6).

Financial amenities had the lowest nearest neighbor ratio but the highest expected observation distance and middle average observation distance. This indicated that financial amenities were concentrated in the central urban area yet were scattered on a microscale. The nearest neighbor ratios, average observation distances and expected observation distance of the shopping, food, and life services amenities were relatively low, indicating that these amenities were concentrated on a small scale and scattered in the city. The nearest neighbor ratios, average observation distances, and expected observation distance of the health and educational amenities were at the medium level. Most large-scale health and educational amenities are planned and constructed by the government; therefore, the distributions of these amenities were relatively even. By contrast, small-scale health amenities such as pharmacies were concentrated in densely populated areas and were therefore spaced out within such areas to minimize competition. Training institutions were more likely to be clustered around schools. Therefore, health amenities were more concentrated in the central urban area than were educational amenities. On a microscale, the agglomeration radius of educational amenities was smaller than that of health amenities. The average observation distances of leisure and transportation amenities had the highest average observation distance. In addition, they really need to be more scattered both in the central urban area overall and on a microscale.

4.2. Quantitative Proportion of Supply and Demand Components in Various Urban Sections

We estimated the proportions of amenity quantity and population (Figure 7) and discovered that the proportions of amenities and population were highest in the old urban section. The proportions decreased from the old urban section to the transitional, new, and expandable urban sections. The decrease in proportion from the old urban section to the transitional urban section was the largest. The slopes of the decreases differed according to the types of the amenities and decreased in the following order: finance, health, shopping, leisure, food, life services, educational, and transportation amenities. The curve was similar to the rent curve [42]. Thus, quantitative proportions of supply and demand components were positively correlated with the oldness of urban development.

With the development of the city, the old urban section has not become a synonym for dirty and messy, but a viable residential area. This is closely related to the Chinese government, which has invested in the planning and renovation of the old urban section [43]. The transformation of the old urban section into a livable area was also closely related to the distribution of high-quality schools [44,45]. High-quality primary and secondary schools were mainly located in the old urban section, which was the key factor driving the population agglomeration and high housing prices in the old urban section. To promote the development of the new urban section, the government urged high-quality schools to establish branch campuses in the new urban section, which was one of the important driving forces behind the rapid population agglomeration and increase in housing prices in the new urban area.

4.3. The Advice for Cultivating Urban Amenities

First, we should regularly evaluate the supply–demand relationship of urban amenities. Due to the great mobility of urban residents, the supply–demand relationship of urban amenities will continue to change. The government needs to dynamically monitor the distribution of residents, make accurate assessments of areas where supply and demand do not match, and formulate new facilities’ allocation plans.

Second, the supply–demand balance of amenities should be improved using a differentiation strategy. In areas where supply and demand are balanced, mainly in the old urban section, the government should continue to promote organic renovation and micro renovation in the central urban area by protecting the old urban area; renovating the ancient prefecture, squatter settlements, urban villages, and old and dilapidated houses; upgrading amenities; and beautifying the landscapes along the Li Canal, Ancient Huai River, Salt River, and the Grand Canal, thereby improving the livability of the central urban area. In areas where supply is less than demand, mainly in the transitional urban section, we should increase the layout of amenities to meet the living needs of residents. In areas where supply exceeds demand, mainly in the new urban section, policies should be introduced to attract residents to live.

Third, integrated development in the central urban area should be strengthened by centering urban development on Eco-city; encouraging the construction development in the Li Canal cultural corridor; and connecting the old urban areas of the Huai’an and Chuzhou District. Large-scale public amenities, medical institutions, and high-quality primary and secondary schools can strongly affect population distribution, but the government should promote equal access to basic public services to achieve a balanced supply–demand relationship in the central urban area. For example, enrollment reform in high-quality primary and secondary schools, particularly that focused on enrolling students from a larger area or from different areas, may help solve the problem of high population agglomeration and rising housing prices.

4.4. Prospects for Future Research

In the future research, further research can be carried out from the following aspects. In our research, the supply–demand relationships of amenities overall and those of different types of amenities were highly balanced in the old urban section. In the transitional and new urban sections, demand generally exceeded supply. The suburb of the expandable urban section had low supply–demand balance, and the supply–demand relationship in this area generally followed a circular distribution pattern. However, the proportion of amenities in the old urban section was higher than that of the number of residents (Figure 7). In the old urban section, supply generally exceeded demand. The proportions of the number of residents in the transitional, new, and expandable urban sections were higher than those of amenities. In these sections, demand generally exceeded supply. These observed trends were inconsistent with findings reported in the literature. A possible reason for this inconsistency was that the demand index was positively correlated with the number and purchasing power of residents. In the present study, we used housing prices as coefficients to represent residents’ purchasing power and, in turn, demand. In addition, we categorized the supply–demand relationships by using only three levels (high, medium, and low), which limited the precision of our analysis. Therefore, the following research can directly use the numerical comparison between the supply index and the demand index to reflect the supply–demand relationship, rather than using three levels as now, which can more accurately reflect the supply–demand relationship of amenities.

The 500 × 500 m grid was used to realize the unity of the size of the analysis unit, which increased the comparability between the grids, and embodied the concept of the 5-min life circle. However, there were such situations in which the boundary of the grid did not correspond to the actual geographical boundary, and the same geographical space was divided into two or more grids. Therefore, in future research, we can try to use the community unit as the basic unit of analysis.

The Baidu heatmap is one of the big data sources reflecting the spatial distribution of the population. However, because Baidu map is an important product of Baidu, many people are using Baidu map navigation on the road, which makes Baidu heatmap have large values on the road, which reduces the accuracy of Baidu heatmap reflecting the spatial distribution of population. Therefore, in future research, we can consider big data sources with higher accuracy, such as mobile phone signaling data.

5. Conclusions

In this study, we developed a supply–demand measurement system for urban amenities and selected the central urban area of Huai’an City as the research area. The spatial characteristics of amenities in the research area were explored through the analysis of multisource big data from the perspective of supply and demand. Our conclusions are as follows:

The supply of and demand for amenities in the central urban area were mostly balanced; some sections had less supply than demand, but few sections had more supply than demand, accounted for 68.35%, 26.06%, and 5.59%, respectively. Significant differences in the spatial distribution of the supply–demand relationships of amenities overall and of different types of amenities were discovered. The old urban section and the areas surrounding the old urban section had high supply–demand balance. In the transitional and new urban areas, demand exceeded supply. The expandable urban section had low supply–demand balance, and the supply–demand relationship in this section followed a clear circular distribution pattern. The spatial distributions of supply of and demand for different amenities were highly consistent, suggesting that the associations between different types of amenities formed a compact amenity system.

Amenities were positively correlated with population size and the oldness of urban development. The proportions of all types of amenities and population were highest in the old urban section and gradually decreased from the old urban section to the transitional, new, and expandable urban sections. The spatial distributions were affected by the types of amenities. Financial amenities were concentrated in the central urban area but scattered on a microscale. Shopping, food, and life services amenities were concentrated in the central urban area and on a microscale. The distribution of health and education amenities reflected a moderate level of agglomeration both in the central urban area overall and on a microscale. Leisure and transportation amenities were relatively scattered within the central urban area and on a microscale.

The government efforts related to the planning and renovation made the older urban section a livable residential area. In addition, the distribution of large public facilities, such as high-quality schools, was another key factor affecting the distribution of the population and amenities. Most of the high-quality primary and secondary schools were located in the old urban section, which contributed to the population agglomeration. To promote balance between the supply of and demand for amenities in the central urban area of Huai’an, we propose the following recommendations. First, we should regularly evaluate the supply–demand relationship of urban amenities. Second, the supply–demand balance of amenities should be improved using a differentiation strategy. Third, integrated development in the central urban area should be strengthened by centering urban development on Eco-city.