This paper studies a land primary development project in Pinggu District as an example for analysis since the complete survey data and adequate data analysis are not available on the assessment of social impact from primary development projects of land. In this paper, we carry out regression analysis based on statistical analysis of survey data, explore the attitudes of stakeholders towards development projects, and find out the main factors and risk problems. Finally, the required policy changes based on the analysis are recommended and put forward to provide reference for impact assessment of social stability.

#### Keywords

- Primary land development
- social impact assessment
- resettlement
- policy recommendations

Primary land development is a business of capital-intensity, characteristic of long periods of development and operation, high land acquisition and removal expenses, involving multilateral interest groups and many uncertain factors [1]. Hence, a long-term problem of social stability is inherent, risky problem in the primary land development process. For current construction projects, strengthening social impact assessment is the guarantee for the idea of sustainable and stable development along with harmony throughout the construction project [2]. According to

From the perspective of the actual project, now decision-makers of projects are getting realised that engineering projects are directly related to the critical interests of the masses [8], and the state of risks of stakeholders also plays an important part on the social stability influence [9]. But in actual projects, the quality and effectiveness of social stability assessment are affected due to issues such as ambiguous regulations on public participation in social impact assessment and low public participation [10]. From an international perspective, social impact assessment has drawn so much attention in the early stages of project planning and construction in developed countries [11,12]. Just at the very time, Western scholars have analysed the benefits of social impact assessment in developing countries [13] and also have conducted social impact assessment research from the perspectives of conflict management [14], participation methods [15], project types [16] and simulation [17]. Nevertheless, as far as related projects of primary land development in China are concerned, the present social impact assessment is conducted directly using the most direct statistical analysis, that is, descriptive statistical analysis on some indicators such as housing conditions and removing attitudes; then identify potential risk factors of the project. The approach will become too simple and extensive for using the survey data, without establishing a correlation between the attributes and attitudes of relevant interest groups, and without fully exploring the value of survey data.

Risk factors in a primary development vary with different risk factors for each and every project. The common ones include obstacles to relocation by the elderly people, traffic congestion, and noise pollution and environmental pollution caused by construction works. This research is focussed on a primary land development project of some villages in Pinggu District, Beijing. At first, research data and direct opinions of relevant interest groups are obtained through field surveys and intuitive factors worthy of concerns are summarised. Then statistical analysis and regression analysis are employed to recognise the probable risks of such projects, and further analysed the factors that need to be focussed on. The project is comprised of all the key elements that may be involved in the primary development of the land and further, it also covers the demands of different kinds of residents. Therefore, it is an appropriate typical case to delve into the focal points of public opinion and underlying risks. This paper considers that the study methods and research contents can provide references for the social impact assessment of this kind and the policy recommendations are beneficial to policy decision-making.

The primary land development project chosen in this study is located at the west side of Xincheng, Pinggu District, Beijing. The total land use for land bank in the early stage is 58.44 hectares, in which 32.60 hectares is for total land consolidation, and the rest 25.84 hectares for relocation and resettlement. The total construction area is 15.17 hectares, while the building area is 25 million square meters and the living construction area is 213,700 m^{2}.

Aiming for a better understanding the opinions and demands of stakeholders on the project, and for a more accurate social impact assessment of the project implementation, the situation and attitudes of villagers are obtained through questionnaires. The research data in this paper are extracted from a random sample survey of residents in a village in Pinggu District. A sum of 284 non-collective dwellers were visited, and 217 valid questionnaires were altogether withdrawn.

The survey falls into three parts: the first part gives an introduction to the basic situation of the villagers, including gender, age and relationship with the householder, occupation, education level and sources of family income. The second includes the basic situation of the existing living conditions and living environment from the year of construction, the structure and the area of the house to the resident population, and whether they are satisfied with the existing living conditions or the existing living environment. The issues concerned with the implementation of this project comes the third. This section contains eight aspects such as house-removing concerns, requests for resettlement methods, opinions on current resettlement ways, compensations and other subsidies, opinions on present compensative standards, problems that may arise after relocation and rights protection methods that make waves. The statistic content and number of households in the third part are shown in Table 1.

Statistical table of stakeholders’ concerns

Compensative standards and its reasonableness | 100 | Property right exchange plus monetary compensation | 207 | Pension problems | 137 |

Implementation of compensation funds | 48 | Pure monetary compensation | 10 | Changes in living environment | 86 |

Follow-up security issues of demolition | 32 | Employment issues | 55 | ||

Openness and legality of removing information | 37 | Employment arrangement | 88 | Schooling problem of kids | 43 |

Requests for Resettlement Methods | Information of get-rich | 47 | Source of income | 2 | |

Resettlement nearby | 140 | Vocational skills training | 43 | No concerns | 3 |

Relocation | 49 | Microloan discount | 9 | ||

Obedient to arrangements | 28 | Without requests on this item | 30 | Report to government | 127 |

Self-negotiation | 63 | ||||

Very satisfied | 9 | Very satisfied | 9 | Legal solution | 56 |

Satisfied | 37 | Satisfied | 37 | Petition way | 16 |

Almost satisfied | 47 | Almost satisfied | 52 | Keep silent | 24 |

Unsatisfied | 35 | Unsatisfied | 28 | By means of media or network | 12 |

Unknown | 89 | Unknown | 91 | Protest by Uniting neighbouring residents | 6 |

It was finally found that 54% of the stakeholders were for the primary land development project, accounting for the largest proportion; 34% of villagers also gave conditional support; only 4% were against it; apart from that, 8% of the families did not express their attitudes towards this project.

Through direct surveys and statistical analysis of villagers’ questionnaires, some important findings are needed to be highlighted:

First, the basic situation of the villagers is quite consistent. Most of them are middle-aged farmers or elderly people, whose main sources of income are from migrant working and pensions. Households are mostly concerned about pensions. For instance, some villagers support for the project, but it is suggested that the oldage care of the elderly should be resolved completely. Some villagers are opposed to the implementation of the project just because they believe that it is inconvenient for the elderly to live in high-rise buildings and hopes it will be properly resettled. Naturally, attention will be mostly paid to the issue of pensions in the relocation process.

Moreover, the villagers have been used to living here for a long time, and enjoy high satisfaction with the existing environment and living conditions. This may partly get in the way of dwellers’ support for demolition.

Lastly, villagers do not have access to multiple sources of information and rely much on information dissemination from village committees and street offices. As a result, the project needs close communication with the village committee and the sub-district office to explain and publicise the project compensation standards and other issues so as to reduce the risks caused by the asymmetry of information.

Generally speaking, the direct survey tells that the villagers offer a high support for the project. Most stakeholders deem that the project will make a greater difference to local construction and development, but also hold different ideas on various issues. It can be seen from this point that there are only a few risk factors for implementation of this project.

In line with the content of the survey, the dependent variable in this article is set in the degree of villagers’ support for the primary land development project, that is, the overall attitude of the interviewed villagers to the proposed primary land development project. This survey investigated respondents’ support rate

This study prescribes a limit to some related variables, which includes: gender, age, occupation, education, year of construction, population, living conditions, living environment, resettlement compensation method, re-settlement satisfaction and compensative satisfaction. To clarify the relationship between varied variables, factor analysis, regression analysis and other methods are employed to further analyse the obtained data. There are descriptive statistics of some variables in Table 2.

Statistical analysis on variable descriptions of stakeholders (

Dependent variable | Overall attitude | 1=objection, 2=indifference, 3=conditional support, 4=support, 5= and others | 3.44 | 0.809 | 1 | 5 |

Control variables and independent variables | Sex | 0= male, 1=female | 0.65 | 0.478 | 0 | 1 |

Age | 1=30 off, 2= 30~45, 3=45~60, 4=above 60 | 3.10 | 0.897 | 1 | 4 | |

Occupation | 1= workers, 2=farmers, 3= commercial servicemen 4=civil servants, 5= the self-employed, 6=professionals, 7= enterprise and public institution staff, 8=students, 9=the retired, 10=others | 5.32 | 3.538 | 1 | 10 | |

Education | 1= high school or less, 2=college, 3=bachelor, 4=master and above | 1.20 | 0.557 | 1 | 4 | |

Year of building | Numerical variables | 1985.38 | 13.865 | 1903 | 2009 | |

Population | Numerical variables | 4.13 | 2.467 | 0 | 11 | |

Living conditions | 1= unsatisfied, 2= general, 3= satisfied | 2.94 | 0.341 | 1 | 3 | |

Living environment | 1= unsatisfied, 2=general, 3=satisfied | 2.95 | 0.285 | 1 | 3 | |

Resettlement and compensative ways | 1=monetary, 2= house property right exchange, 3=monetary combined with house property right | 2.55 | 0.584 | 1 | 4 | |

Resettlement satisfaction | 1=unsatisfied, 2= unknown, 3=almost satisfied, 4=obedient to arrangement, 5= satisfied, 6=very satisfied | 2.75 | 1.428 | 1 | 6 | |

Compensation satisfaction | 1=unsatisfied, 2= almost satisfied, 3=satisfied, 4= very satisfied | 2.77 | 1.382 | 1 | 6 |

In order to have effective control on variables, many factors such as gender, age, occupation are selected and factor analysis is used to eliminate some variables. The principle of factor analysis is to display fewer independent factors to reflect most of the information of the original variables. Suppose that there are _{1},_{2},...,_{p}x_{1},_{2},...,_{p}_{1}, _{2},..., _{k}

Sampling moderation values and Bartlett's test

Kaiser-Meyer-Olkin metric of sampling sufficiency | 0.726 | |

Bartlett's test of sphericity | Approximate to chi-square | 964.658 |

66 | ||

Sig. | 0.000 |

Prior to performing factor analysis, whether there is a certain linear relationship between the original variables and whether it is suitable to use factor analysis to extract factors are checked. This paper utilises the correlation coefficient matrix of variables, Bartlett's test of sphericity and KMO test to analyse.

Based on the correlation coefficient matrix test, it is found that most of the correlation coefficients are high, which implies strong linear relationship between the variables, and so the common factors can be extracted suitable for factor analysis. The corresponding probability value of the Bartlett sphericity test gets close to zero. If the significance level

Based on results of Table 4, six components were extracted from the initial solution. Thus, the total variance of the original variable can merely explain six components only and the cumulative contribution rate is up to 74.79%.

Explicatory analysis of total variance

1 | 2.253 | 18.778 | 18.778 | 2.253 | 18.778 | 18.778 | 2.140 | 17.832 | 17.832 |

2 | 1.936 | 16.130 | 34.907 | 1.936 | 16.130 | 34.907 | 1.926 | 16.053 | 33.885 |

3 | 1.392 | 11.601 | 46.509 | 1.392 | 11.601 | 46.509 | 1.397 | 11.638 | 45.523 |

4 | 1.249 | 10.411 | 56.919 | 1.249 | 10.411 | 56.919 | 1.250 | 10.416 | 55.939 |

5 | 1.121 | 9.340 | 66.259 | 1.121 | 9.340 | 66.259 | 1.195 | 9.960 | 65.898 |

6 | 1.023 | 8.527 | 74.785 | 1.023 | 8.527 | 74.785 | 1.066 | 8.887 | 74.785 |

7 | 0.898 | 7.483 | 82.268 | ||||||

8 | 0.760 | 6.331 | 88.599 | ||||||

9 | 0.702 | 5.850 | 94.449 | ||||||

10 | 0.503 | 4.192 | 98.641 | ||||||

11 | 0.123 | 1.023 | 99.664 | ||||||

12 | 0.040 | 0.336 | 100.000 |

Extractive method: Principal component analysis.

Table 5 shows that the gender, age, occupation, education level of the villagers, the year of house built and the number of residents, which demonstrate remarkable correlations.

Componential matrix^{a} of related variables

Gender | 0.227 | 0.020 | −0.145 | −0.459 | 0.579 | 0.154 |

Age | −0.139 | 0.150 | −0.764 | −0.064 | 0.034 | −0.338 |

Occupation | −0.027 | −0.164 | 0.130 | −0.200 | −0.687 | −0.019 |

Education level | 0.181 | 0.069 | 0.776 | −0.331 | 0.149 | −0.052 |

Year of house built | −0.199 | −0.111 | −0.139 | 0.129 | −0.085 | 0.908 |

Number of inhabitants | −0.172 ++++ | 0.144 | 0.040 | 0.689 | 0.277 | 0.023 |

Satisfaction of existing living conditions | −0.100 | 0.948 | 0.062 | −0.068 | −0.068 | 0.065 |

Satisfaction of current living environment | −0.133 | 0.947 | 0.057 | −0.027 | −0.038 | 0.071 |

Ways of compensation and resettlement | −0.034 | −0.084 | 0.344 | 0.555 | 0.166 | −0.182 |

Whether villagers are satisfied with methods and standards of compensation and resettlement | 0.922 | 0.140 | −0.089 | 0.191 | −0.193 | 0.016 |

Whether the subsidy standard is satisfactory | 0.920 | 0.144 | −0.088 | 0.192 | −0.177 | 0.044 |

Overall attitude | 0.596 | −0.068 | −0.071 | −0.085 | 0.324 | 0.110 |

Extraction method: Principal component analysis.

Six components have been extracted.

Therefore, the stakeholders of different gender, age, occupation, education level, the year of house construction and the number of residents give evident distinction on project attitudes. These factors can be used as the basis for the following steps.

For illustrating deeply the diversity of the influential factors such as gender, age, occupations, education levels, years of house construction and the number of residents on the attitude of the project among Baigezhuang villagers, the multiple linear regression analysis is performed on the data extracted by the above factor analysis. The multiple linear regression analysis is applied to disclose the linear relationship between the variables explained and many other explanatory variables. The mathematical model is

Anova^{a}

1 | Regression | 7.171 | 6 | 1.195 | 1.868 | 0.008^{b} |

Residual | 134.359 | 210 | 0.640 | |||

Total | 141.530 | 216 |

Dependent variables: Overall attitude.

Predictive variables: (constant), number of inhabitants, age, year of house construction, occupation, gender, education level.

The outputs of multiple linear regression indicate that the observed value of the

Table 7 manifests and presents the regression equation between each variable and the degree of project support: approval rate= 0.184 * gender-0.002 * age-0.056 * occupation + 0.075 education level - 0.026 * year of house built - 0.038 * residential population.

Coefficients^{a} of relevant social influence factors

1 | Constant | 6.237 | 8.046 | 0.775 | 0.439 | |

Sex | 0.311 | 0.116 | 0.184 | 2.679 | 0.008 | |

Age | −0.002 | 0.066 | −0.002 | −0.030 | 0.976 | |

Occupation | −0.013 | 0.016 | −0.056 | −0.822 | 0.412 | |

Education level | 0.109 | 0.109 | 0.075 | 1.000 | 0.318 | |

Year of house construction | −0.002 | 0.004 | −0.026 | −0.376 | 0.708 | |

Residential population | −0.012 | 0.022 | −0.038 | −0.555 | 0.580 |

Dependent variables: Overall attitude.

Based on the obtained correlations between each variable and the project, the main six points of the study are concluded:

The important points are (1) women have higher support for the project than men; (2) the higher the education level is, the higher the support rate for the project; (3) occupations of different kinds are supporters for the project and to our surprise, workers and business servicemen give a higher supporting rate; (4) the longer you live here and the older you are, the lower your support rate will be, because the older the villagers are, the more adaptive they are to the existing living environment and moreover the elder people's legs and feet may be growing less flexible as the time goes on; (5) the longer the time the house is built, the more its owner will support the project and during the investigation, many old houses collapsed severely, and some of them in bad condition that makes difficult to move in again. So, these housekeepers expressed support for the project for safety reasons; and (6) larger the number of residents is, the lower the support rate is, which is mostly because dwellers like these live here for a long time, and have been accustomed to the current living environment. ’More importantly, they are worried that due to the large population, housing resettlement after demolition cannot be successfully resolved; hence they do not approve the project. The first three points are mainly observed in different interest groups. The fourth point emphasis the much worry on the convenience of elder's life. The fifth point shows that the living environment can objectively promote the launching of this project. The last is ascribed to the problem of living habits and the distribution of family benefits. The three conclusions mentioned at the end require immediate information gathering and resolve them via good communication.

In summary, the main factors of the social impact assessment in the primary land development projects after thorough research are supposed to cover the issues of meeting the requirements of various groups, environmental adaptation and convenience for the elderly together with reasonable distribution of interests.

Given the direct analysis of opinions from stakeholder groups and the study of major social influence factors, it can be noticed that the unimpeded flow of information and good communication are indispensable for land-level development projects. Furthermore, the requirements of various stakeholders should be considered, especially the elderly, and ensuring the most reasonable distribution of benefits. Subsequently, the paper here comes up with the following three policy recommendations to government regulators:

Open information. As for the problem of inadequate risk communication among stakeholders in the social impact assessment of major public policies, risk communication may as well be acted as the kernel of social impact assessment and racing through the whole process of policy formulation and enforcement [18]. The public ‘concern on the elderly and the adjustment to the new environment can be dealt immediately by effective information spreading. We should keep eyes on the entire process of risk assessment and risk communication, strengthen the negotiation and dialogue among plural subjects, and give full play of the media and by publicly responding to negative comments. Relevant government departments also ought to treat the negative emotions of the minority specifically on basis of their own reasons. As soon as there are negative voices, quick responses and effective guidance should be given.

Diversity of subsidies. Studies have indicated that differences in the age, occupation, family background, and education of residents differentiated their demands also. Therefore, while ensuring openness, fairness and no violation of relevant rules and regulations, the government can take advantage of the available resources to meet the various needs of residents as much as possible. Diversified subsidy options for the public may be utilised to try to solve some family interests, disputes and other issues. Relevant government departments may also form a coordinating group for public disputes to drive the development of the project.

Democratisation of decision-making. In spite of paying more attention to the importance of citizen participating in the current practice of social impact assessment, the channels for citizens are still waiting to be widened. Continuous improvement of field participation is on the road, while netizens should be admitted to get more access to decision-making. Correspondingly, it should be clear that the role of the government in risk assessment should not be ‘parental’ to guide citizens in favour of an administrative decision, but to allow citizens to fully understand the decision-making information and the results of ‘stable evaluation‘ from a neutral place. Determine whether the decision can be brought into force by the people themselves [19]. Procedural mechanisms such as prior investigations, public hearings, public participation, validity check, collective discussion and decisions, tracking decision-making effects, and error correction are the legal institutional basis for major administrative decision-making procedures [20].

The three policy measures mentioned above are all closely related to real interest rates for residents and scalable. All policy measures can be applied according to the characteristics of the project, and preference could be given to one or both of these measures. But for the current stable reviews on great decision-making projects, there are main steps to determine the project awaiting assessment, draw up an evaluation plan, fully give a hearing to opinions, make a comprehensively analytic demonstration, rank the risk level, set out precautionary measures, fix the evaluation report and use the evaluation results. Relevant government departments can take the forms of consulting documents, such as field surveys, questionnaires, opinion polls, interviews, internet public opinions, hearings, and public notices at the stage of hearing opinions and so on. Make sure that there are a variety of ways for residents to obtain information, and that the content of survey has a large amount of data and immediacy. The three types of policy recommendations above can be used in conjunction with existing processes.

The primary land development projects cannot be harmoniously and smoothly developed until risks and accidents get decreased or avoided in construction projects, accompanied by preventing and resolving project social impact and social conflicts. The foundation for the effective development of project is the social impact assessment, which is attributed to the close combination of theoretical analysis and practical application. This paper takes the primary land development project involved in a village in Pinggu District of Beijing as a backdrop, and extracts research data through questionnaire design and field survey. The opinions of stakeholders are statistically and intuitively analysed, and then a profound study on potential risk factors of the project is undertaken by making use of regression analysis. On one hand, this research can provide a reference for the techniques of social impact assessment of such kind. On the other hand, the risk factors identified in the research conclusions are the focuses of opinion polls as well and so it can lend some insight into similar projects. In the meantime, this paper puts forward some corresponding policy suggestions, with a humble eagerness to furnish referential for the social impact assessment of primary land development projects and for correlated government decision-making.

#### Statistical analysis on variable descriptions of stakeholders (n = 217)

Dependent variable | Overall attitude | 1=objection, 2=indifference, 3=conditional support, 4=support, 5= and others | 3.44 | 0.809 | 1 | 5 |

Control variables and independent variables | Sex | 0= male, 1=female | 0.65 | 0.478 | 0 | 1 |

Age | 1=30 off, 2= 30~45, 3=45~60, 4=above 60 | 3.10 | 0.897 | 1 | 4 | |

Occupation | 1= workers, 2=farmers, 3= commercial servicemen 4=civil servants, 5= the self-employed, 6=professionals, 7= enterprise and public institution staff, 8=students, 9=the retired, 10=others | 5.32 | 3.538 | 1 | 10 | |

Education | 1= high school or less, 2=college, 3=bachelor, 4=master and above | 1.20 | 0.557 | 1 | 4 | |

Year of building | Numerical variables | 1985.38 | 13.865 | 1903 | 2009 | |

Population | Numerical variables | 4.13 | 2.467 | 0 | 11 | |

Living conditions | 1= unsatisfied, 2= general, 3= satisfied | 2.94 | 0.341 | 1 | 3 | |

Living environment | 1= unsatisfied, 2=general, 3=satisfied | 2.95 | 0.285 | 1 | 3 | |

Resettlement and compensative ways | 1=monetary, 2= house property right exchange, 3=monetary combined with house property right | 2.55 | 0.584 | 1 | 4 | |

Resettlement satisfaction | 1=unsatisfied, 2= unknown, 3=almost satisfied, 4=obedient to arrangement, 5= satisfied, 6=very satisfied | 2.75 | 1.428 | 1 | 6 | |

Compensation satisfaction | 1=unsatisfied, 2= almost satisfied, 3=satisfied, 4= very satisfied | 2.77 | 1.382 | 1 | 6 |

#### Explicatory analysis of total variance

1 | 2.253 | 18.778 | 18.778 | 2.253 | 18.778 | 18.778 | 2.140 | 17.832 | 17.832 |

2 | 1.936 | 16.130 | 34.907 | 1.936 | 16.130 | 34.907 | 1.926 | 16.053 | 33.885 |

3 | 1.392 | 11.601 | 46.509 | 1.392 | 11.601 | 46.509 | 1.397 | 11.638 | 45.523 |

4 | 1.249 | 10.411 | 56.919 | 1.249 | 10.411 | 56.919 | 1.250 | 10.416 | 55.939 |

5 | 1.121 | 9.340 | 66.259 | 1.121 | 9.340 | 66.259 | 1.195 | 9.960 | 65.898 |

6 | 1.023 | 8.527 | 74.785 | 1.023 | 8.527 | 74.785 | 1.066 | 8.887 | 74.785 |

7 | 0.898 | 7.483 | 82.268 | ||||||

8 | 0.760 | 6.331 | 88.599 | ||||||

9 | 0.702 | 5.850 | 94.449 | ||||||

10 | 0.503 | 4.192 | 98.641 | ||||||

11 | 0.123 | 1.023 | 99.664 | ||||||

12 | 0.040 | 0.336 | 100.000 |

#### Coefficientsa of relevant social influence factors

1 | Constant | 6.237 | 8.046 | 0.775 | 0.439 | |

Sex | 0.311 | 0.116 | 0.184 | 2.679 | 0.008 | |

Age | −0.002 | 0.066 | −0.002 | −0.030 | 0.976 | |

Occupation | −0.013 | 0.016 | −0.056 | −0.822 | 0.412 | |

Education level | 0.109 | 0.109 | 0.075 | 1.000 | 0.318 | |

Year of house construction | −0.002 | 0.004 | −0.026 | −0.376 | 0.708 | |

Residential population | −0.012 | 0.022 | −0.038 | −0.555 | 0.580 |

#### Sampling moderation values and Bartlett's test

Kaiser-Meyer-Olkin metric of sampling sufficiency | 0.726 | |

Bartlett's test of sphericity | Approximate to chi-square | 964.658 |

66 | ||

Sig. | 0.000 |

#### Anovaa

1 | Regression | 7.171 | 6 | 1.195 | 1.868 | 0.008^{b} |

Residual | 134.359 | 210 | 0.640 | |||

Total | 141.530 | 216 |

#### Componential matrixa of related variables

Gender | 0.227 | 0.020 | −0.145 | −0.459 | 0.579 | 0.154 |

Age | −0.139 | 0.150 | −0.764 | −0.064 | 0.034 | −0.338 |

Occupation | −0.027 | −0.164 | 0.130 | −0.200 | −0.687 | −0.019 |

Education level | 0.181 | 0.069 | 0.776 | −0.331 | 0.149 | −0.052 |

Year of house built | −0.199 | −0.111 | −0.139 | 0.129 | −0.085 | 0.908 |

Number of inhabitants | −0.172 ++++ | 0.144 | 0.040 | 0.689 | 0.277 | 0.023 |

Satisfaction of existing living conditions | −0.100 | 0.948 | 0.062 | −0.068 | −0.068 | 0.065 |

Satisfaction of current living environment | −0.133 | 0.947 | 0.057 | −0.027 | −0.038 | 0.071 |

Ways of compensation and resettlement | −0.034 | −0.084 | 0.344 | 0.555 | 0.166 | −0.182 |

Whether villagers are satisfied with methods and standards of compensation and resettlement | 0.922 | 0.140 | −0.089 | 0.191 | −0.193 | 0.016 |

Whether the subsidy standard is satisfactory | 0.920 | 0.144 | −0.088 | 0.192 | −0.177 | 0.044 |

Overall attitude | 0.596 | −0.068 | −0.071 | −0.085 | 0.324 | 0.110 |

#### Statistical table of stakeholders’ concerns

Compensative standards and its reasonableness | 100 | Property right exchange plus monetary compensation | 207 | Pension problems | 137 |

Implementation of compensation funds | 48 | Pure monetary compensation | 10 | Changes in living environment | 86 |

Follow-up security issues of demolition | 32 | Employment issues | 55 | ||

Openness and legality of removing information | 37 | Employment arrangement | 88 | Schooling problem of kids | 43 |

Requests for Resettlement Methods | Information of get-rich | 47 | Source of income | 2 | |

Resettlement nearby | 140 | Vocational skills training | 43 | No concerns | 3 |

Relocation | 49 | Microloan discount | 9 | ||

Obedient to arrangements | 28 | Without requests on this item | 30 | Report to government | 127 |

Self-negotiation | 63 | ||||

Very satisfied | 9 | Very satisfied | 9 | Legal solution | 56 |

Satisfied | 37 | Satisfied | 37 | Petition way | 16 |

Almost satisfied | 47 | Almost satisfied | 52 | Keep silent | 24 |

Unsatisfied | 35 | Unsatisfied | 28 | By means of media or network | 12 |

Unknown | 89 | Unknown | 91 | Protest by Uniting neighbouring residents | 6 |

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education activities in colleges based on differential equation RETRACTION NOTE Calculation of tourism development income index based on finite element ordinary differential mathematical equation Adoption of deep learning Markov model combined with copula function in portfolio risk measurement Radar system simulation and non-Gaussian mathematical model under virtual reality technology Comparison of compression estimations under the penalty functions of different violent crimes on campus through deep learning and linear spatial autoregressive models Research and application of constructing football training linear programming based on multiple linear regression equation Research on management evaluation of enterprise sales cash flow percentage method based on the application of quadratic linear regression equations Mathematical simulation analysis of optimal detection of shot-putters’ best path Determination of the minimum distance between vibration source and fibre under existing optical vibration signals: a study Mathematical modelling of enterprise financial risk assessment based on risk conduction model Nonlinear differential equations based on the B-S-M model in the pricing of derivatives in financial markets Nonlinear Differential Equations in the Teaching Model of Educational Informatisation The evaluation of college students’ innovation and entrepreneurship ability based on nonlinear model Institutional investor company social responsibility report and company performance Mathematical analysis of China's birth rate and research on the urgency of deepening the reform of art education First-principles calculations of magnetic and mechanical properties of Fe-based nanocrystalline alloy Fe _{80}Si_{10}Nb_{6}B_{2}Cu_{2}Educational reform informatisation based on fractional differential equation Has the belt and road initiative boosted the resident consumption in cities along the domestic route? – evidence from credit card consumption 3D Mathematical Modelling Technology in Visual Rehearsal System of Sports Dance Attitude control for the rigid spacecraft with the improved extended state observer Sports health quantification method and system implementation based on multiple thermal physiology simulation Research on visual optimization design of machine–machine interface for mechanical industrial equipment based on nonlinear partial equations Research on the normalisation method of logging curves: taking XJ Oilfield as an example Information technology of preschool education reform of fine arts based on fractional differential equation Information Teaching Model of Preschool Art Education in Colleges and Universities Based on Finite Element Higher-Order Fractional Differential Equation College Students’ Mental Health Climbing Consumption Model Based on Nonlinear Differential Equations Cognitive Computational Model Using Machine Learning Algorithm in Artificial Intelligence Environment Research on tourism income index based on ordinary differential mathematical equation Application of Higher-Order Ordinary Differential Equation Model in Financial Investment Stock Price Forecast System dynamics model of output of ball mill Dynamics of infectious diseases: A review of the main biological aspects and their mathematical translation Optimisation of Modelling of Finite Element Differential Equations with Modern Art Design Theory Mathematical function data model analysis and synthesis system based on short-term human movement Sensitivity Analysis of the Waterproof Performance of Elastic Rubber Gasket in Shield Tunnel Human gait modelling and tracking based on motion functionalisation Analysis and synthesis of function data of human movement Energy-saving technology of BIM green buildings using fractional differential equation Study on the training model of football movement trajectory drop point based on fractional differential equation Financial Accounting Measurement Model Based on Numerical Analysis of Rigid Normal Differential Equation and Rigid Functional Equation User online consumption behaviour based on fractional differential equation Differential equation model of financial market stability based on Internet big data Multi-attribute Decision Method Based on Normal Random Variable in Economic Management Risk Control Children’s cognitive function and mental health based on finite element nonlinear mathematical model Value Creation of Real Estate Company Spin-off Property Service Company Listing Digital model creation and image meticulous processing based on variational partial differential equation Dichotomy model based on the finite element differential equation in the educational informatisation teaching reform model Nonlinear Dissipative System Mathematical Equations in the Multi-regression Model of Information-based Teaching The policy efficiency evaluation of the Beijing–Tianjin–Hebei regional government guidance fund based on the entropy method Stock price analysis based on the research of multiple linear regression macroeconomic variables Fractional Linear Regression Equation in Agricultural Disaster Assessment Model Based on Geographic Information System Analysis Technology The transfer of stylised artistic images in eye movement experiments based on fuzzy differential equations Deformation and stress theory of surrounding rock of shallow circular tunnel based on complex variable function method A mathematical model of the fractional differential method for structural design dynamics simulation of lower limb force movement step structure based on Sanda movement Numerical calculation and study of differential equations of muscle movement velocity based on martial articulation body ligament tension Research on the Psychological Distribution Delay of Artificial Neural Network Based on the Analysis of Differential Equation by Inequality Expansion and Contraction Method Study on Establishment and Improvement Strategy of Aviation Equipment Research on Financial Risk Early Warning of Listed Companies Based on Stochastic Effect Mode The Model of Sugar Metabolism and Exercise Energy Expenditure Based on Fractional Linear Regression Equation Constructing Artistic Surface Modeling Design Based on Nonlinear Over-limit Interpolation Equation Optimal allocation of microgrid using a differential multi-agent multi-objective evolution algorithm Numerical Simulation Analysis Mathematics of Fluid Mechanics for Semiconductor Circuit Breaker Characteristics of Mathematical Statistics Model of Student Emotion in College Physical Education Human Body Movement Coupling Model in Physical Education Class in the Educational Mathematical Equation of Reasonable Exercise Course