Neighborhood satisfaction, physical and perceived naturalness and openness

Journal of Environmental Psychology 30 (2010) 52–59

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Journal of Environmental Psychology journal homepage: www.elsevier.com/locate/jep

Neighborhood satisfaction, physical and perceived naturalness and openness Misun Hur*, Jack L. Nasar, Bumseok Chun Department of City and Regional Planning, The Ohio State University, United States

a r t i c l e i n f o

a b s t r a c t

Article history: Available online 19 May 2009

This study examined neighborhood satisfaction in relation to naturalness and openness. It used Geographic Information System (GIS) and Landsat satellite imagery to physically measure the environmental attributes. Through path analysis it examined the relationship among the attributes, resident ratings of those environmental attributes, their satisfaction with them, and their overall neighborhood satisfaction (n ¼ 725). We expected overall neighborhood satisfaction to relate to the resident’s ratings of the environmental attributes and to the physical measures of them. The path model showed that overall neighborhood satisfaction was associated directly with the physical measure of building density and indirectly with the physical measure of vegetation rate through perception and evaluation of them. The perceptions and evaluations of the attributes related to one another. With refinements, GIS and Landsat data georelated to survey data can offer a powerful tool for understanding the complex nature of neighborhood satisfaction and behavior. Published by Elsevier Ltd.

Keywords: Physical measure of the attributes of the environment Perceived and evaluative attribute of the environment Overall neighborhood satisfaction Vegetation rate Building density Naturalness Openness Satellite image processing Spatial analysis Geographic Information System (GIS) Structural equation modeling (SEM) Path analysis

Laypersons differ substantially from experts in their appraisals of the environment (Bonnes & Bonaiuto, 1995; Bonnes, Uzzell, Carrus, & Kelay, 2007; Francescato, Weidemann, Anderson, & Chenoweth, 1979; Gifford, Hine, Muller-Clemm, Reynolds, & Shaw, 2000; Kaiser, Weiss, Barbey, & Donnelly, 1970; Michelson, 1968; Nasar, 1989). Because laypeople (e.g., residents) live in the neighborhood and have day-to-day experiences in it, decision makers need to know the residents’ points of view. Neighborhood satisfaction, which refers to residents’ overall evaluation of their neighborhood environment, has long been a major research subject in sociology, planning, and related disciplines (Amerigo, 2002; Amerigo & Aragones, 1997; Carvalho, George, & Anthony, 1997; Francescato, 2002; Hur, 2004; Lipsetz, 2000; Marans, 1976; Marans & Rodgers, 1975; Mesch & Manor, 1998; Weidemann & Anderson, 1985). Research suggests that it has a complex and multidimensional basis relating to both the

* Corresponding author. Department of City and Regional Planning, The Ohio State University, 275 West Woodruff Avenue, Knowlton School of Architecture, Columbus, OH 43210-1138, United States. Tel.: þ1 614 668 1491. E-mail address: [email protected] (M. Hur). 0272-4944/$ – see front matter Published by Elsevier Ltd. doi:10.1016/j.jenvp.2009.05.005

actual and perceived environment (Amerigo & Aragones, 1997; Francescato, 2002; Marans & Rodgers, 1975; Marans & Spreckelmeyer, 1981; Weidemann & Anderson, 1985). Physical attributes of the environment are filtered through perception and evaluation to affect satisfaction. Research has identified personal, social, and psychological factors associated with neighborhood satisfaction (Alvi, Schwartz, DeKeseredy, & Maume, 2001; Brower, 2003; Brown & Perkins, 2001; Brown, Perkins, & Brown, 2004; Chapman & Lombard, 2006; Galster, 1987; Hur, 2004; Kaplan, 1985, 2001; Kearney, 2006; Lipsetz, 2000; Morrow-Jones, Wenning, & Li, 2005; Nasar, 1998; Robinson, Lawton, Taylor, & Perkins, 2003; St. John & Clark, 1984; Talen & Shah, 2007; Taylor, Shumaker, & Gottfredson, 1985), but it has often overlooked the attributes of the physical environment. Moreover, when studies examined attributes of the physical environment, they often relied on ratings of the attributes rather than physical measures of them (Galster & Hesser, 1981; Kim & Kaplan, 2004; Patterson & Chapman, 2004). This study focused on two salient attributes of the physical environment and aesthetic evaluations of itdnaturalness and openness (Nasar, 1998). Research has identified aesthetics, or pleasingness to the eye, as one of the most important factors in neighborhood satisfaction (Francescato et al., 1979; Kaplan, 1985,

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Fig. 1. Conceptual model of neighborhood satisfaction.

2001; Kearney, 2006; Langdon, 1988, 1997; Sirgy & Cornwell, 2002). Moreover, research has found naturalness (vegetation) as a principal component of neighborhood attachment (Bonaiuto, Fornara, & Bonnes, 2003) and as a factor affecting use of space, sense of safety and adjustment, and informal social contact among neighbors (Kuo & Sullivan, 1998). Research has also found openness to be a key factor associated with evaluative appraisals, such as neighborhood satisfaction (Lansing & Marans, 1969; Nasar, 1988). Openness refers to vistas with open views and the lack of spatial enclosure (Lansing & Marans, 1969; Nasar, 1998). It may also be reflected in density of housing. Hur (2004) found satisfaction with density of housing as an important factor associate with neighborhood satisfaction. On-site measures of physical attributes can take time. Remote sensing and GIS may offer a more efficient approach if it gets meaningful data. Remote sensing, which takes numerical reflectance of surface materials from the earth’s surface, can provide a physical measure of naturalness (the vegetation rate) anywhere on earth in longitudinal series of time. The vegetation rate is generated by the Normalized Differential Vegetation Index (NDVI) method. This rate correlates with ecological patterns of environment (Pettorelli et al., 2005), ecological landscape structures for comparisons with neighborhood satisfaction (Lee, 2002; Lee, Ellis, Kweon, & Hong, 2008), demographic characteristics for environmental quality (Fung & Siu, 2000), and quality of life (Li & Weng, 2007). People define density differently. As Churchman (1999) and Alexander (1993) pointed out, a universal measure of density is difficult due to its complex characteristics, differences in the way it is defined and used in different countries, and differences across disciplines. Two most commonly used definitions are population density based on the number of people per given area (Fulton, Pendall, Nguyen, & Harrison, 2002; Kasanko et al., 2006; Nelson, Foley, O’Gorman, Moyna, & Woods, 2008; Sundstrom, 1978) and residential density based on the number of dwelling units (Morrison & McMurray, 1999; Song & Knaap, 2004) or floor area per given area (Gao, Asami, & Katsumata, 2006; Machida, Sugiura, Tamimoto, Kiyota, & Takamizawa, 1990; Song & Knaap, 2004). We used residential density based on the ratio of floor area per given area because it may show the physical density of buildings that may affect perceived openness, aesthetic quality, and the overall neighborhood satisfaction. While not directly connected to openness, it should have a more direct connection than would population density. In terms of analysis, research has often used linear regression to define factors associated with neighborhood satisfaction. Lu (1999) suggested the ordered logit model (OLM) as a more appropriate analysis due to the ordinal nature of the dependent variables representing satisfaction. However, neither approach shows the multidimensional characteristics of neighborhood satisfaction

noted by Francescato et al. (1979), Galster and Hesser (1981), Newman and Duncan (1979), and Bonaiuto, Aiello, RPerugini, Bonnes, and Ercolani (1999). Thus, we opted for a multidimensional and multistage analysis. This study used path analysis, a method of Structural Equation Modeling (SEM). Path analysis is a comprehensive statistical approach that tests hypotheses about relations among observed and latent variables (Hoyle, 1995) and enables one to determine whether the suggested theoretical model successfully accounts for the actual relationships observed in the sample data (Hatcher, 1994). Fig. 1 illustrates our conceptual model of neighborhood satisfaction.1 For the physical measure of the attributes of the environment (box 1 in Fig. 1), this study focused on naturalness and openness measured by vegetation rate and building density respectively. The perceived attributes of the environment (box 2 in Fig. 1) were obtained by having residents rate their perceptions of the vegetation (naturalness) and open views (openness) of their neighborhood. For the evaluation of the attributes of the environment (box 3 in Fig. 1), we obtained ratings of the resident’s satisfaction with the physical attributes (i.e., satisfaction with presence of trees, amount of open spaces/parks, and density of housing within neighborhood). The physical attribute, perception and evaluation of them, and person characteristics should affect resident ratings of overall neighborhood satisfaction (box 4 in Fig. 1). The study did not include resident characteristics or social characteristics of the neighborhood in the model, as we were more concerned with the physical characteristics of the neighborhood associated with overall neighborhood satisfaction. This study raised three questions: 1) How well do the physical measures predict resident overall neighborhood satisfaction; 2) How well do the physical measures and perceptions of them affect satisfaction with them; and 3) How well do the physical measures, perceptions of and satisfactions with them affect overall neighborhood satisfaction? 1. Method 1.1. Sample The study took place in Franklin County in central Ohio. In 2004, the Ohio State University Survey Research Center mailed out a Homeowner Satisfaction and Mobility Decisions questionnaire with a cover letter to a random sample of 2600 homeowners in Franklin County. A reminder post card followed one

1 This research is part of a broader dissertation study that looks at the relation of physical attributes of the environment (such as naturalness, openness, complexity, order, upkeep, density of development, and housing style) to perception of those attributes, satisfaction with them, and overall neighborhood satisfaction.

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Table 1 Research attributes. Attributes Physical measure of the attributes of the environment

Perceived attributes of the environment

Definition

Attributes

Vegetation rate

Percentage of vegetationcovered area per setting (number of cells)

Evaluation of the attributes of the environment

Building density

Percentage of building footprint per setting (area)

Naturalness

The degree to which the neighborhood has vegetation and water (1–7 Likert-scale, where 1 ¼ has none at all, 4 ¼ has a medium amount, and 7 ¼ has a lot)

Openness

The degree to which the neighborhood has open views and open space (1–7 Likert-scale, where 1 ¼ has none at all, 4 ¼ has a medium amount, and 7 ¼ has a lot)

Definition Satisfaction with presence of trees

The level of satisfaction with presence of trees in the neighborhood (1–7 Likert-scale, where 1 ¼ very dissatisfied, 4 ¼ neutral, and 7 ¼ very satisfied)

Satisfaction with amount of open spaces

The level of satisfaction with amount of open space, parks, or bike paths in the neighborhood (1-7 Likertscale, where 1 ¼ very dissatisfied, 4 ¼ neutral, and 7 ¼ very satisfied)

Satisfaction with density of housing

The level of satisfaction with density of housing in the neighborhood (1-7 Likert-scale, where 1 ¼ very dissatisfied, 4 ¼ neutral, and 7 ¼ very satisfied)

Resident ratings of overall neighborhood satisfaction

week after to those who had not yet responded; and another copy of the questionnaire survey and cover letter were mailed out two weeks after to those who had not yet responded. The questionnaire had a response rate of 32.2 percent (N ¼ 837). Our study used 725 of the 837 responses. We had to exclude responses located outside the street network Geographic Information System (GIS)2 datasets in Franklin County, Ohio.3 The ages of individuals in the sampled households ranged from 17 to 86, with a mean 49.3 years (SD ¼ 13.1). Average total family income of the sampled households ranged from $60,000 to $100,000 before taxes. The sample had more females (47.7 percent) than males (34.9 percent), more Caucasians (85.7 percent) and more married people (69.2 percent). Since the questionnaire was mailed to homeowners in Franklin County, Ohio, all respondents were homeowners.

2 A Geographic Information System (GIS) is a collection of computer hardware, software, and geographic data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. A GIS data contains a geographic reference – either an explicit reference such as a latitude and longitude coordinate, or an implicit reference such as an address or road name. Geocoding is an automated process that a GIS creates explicit references from implicit references for example, an address to a specific point on the earth. 3 This study relies heavily on GIS analysis that requires a good geographic database. However, in reality, a GIS street network database often fails to support the rapid development of suburban area. Although we used the most updated dataset for the street network, which was downloaded from ESRI (Census 2000 TIGER/Line Data at http://arcdata.esri.com/data/tiger2000/tiger_download.cfm), the road network was still incomplete and therefore, the researchers had to drop some of the outer city sample.

The level of overall neighborhood satisfaction (1–7 Likert-scale, where 1 ¼ very dissatisfied, 4 ¼ neutral, and 7 ¼ very satisfied)

1.2. Instruments and attributes This study used data from GIS data and resident surveys to assess the environment and resident responses to it. Table 1 shows the attributes measured, their definition and measurement. To physically measure the attributes of the environment, we used the mapping method (Geocoding) in GIS. After mapping, we defined settings for each subject. We adopted a-quarter-mile distance buffer based on walkable neighborhoods defined by previous work (Colabianchi et al., 2007; Hoehner, Ramirez, Elliott, Handy, & Brownson, 2005; Jago, Baranowski, Zakeri, & Harris, 2005; Lund, 2003; Pikora et al., 2002; Western Australian Planning Commission, 2000) and applied it to a road network environment (‘‘a-quarter-mile network buffer’’ from each subject’s house). A quarter-mile distance reflects the distance that a person could walk at a 3 mph pace in 5 min. A network buffer is much realistic than the straight-line buffer (Frank, Schmid, Sallis, Chapman, & Saelens, 2005) in that it captures the actual walking environment along the streets. The size of the network buffer varies with the connectivity of the road network; for example, more intersections allow a greater area to be covered on the ground. Network buffers tend to be smaller than the straight-line buffer and consist of irregular shapes. A buffer does not have to include an immediate neighboring area if it is blocked by a perceived or actual barrier such as highway, railway, or river. We assume that a setting indicates a resident’s perceived neighborhood unit boundary. The satellite image processing and spatial analysis in GIS involved two steps. Of the satellite images taken between 2004 and 2005, we chose Landsat Thematic Mapper data (30 m  30 m

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For the density attribute, we used Spatial Analysis in GIS to measure building density. We defined building density as the percentage of building footprints per setting (area). The average of building density in this study was .13. It did not have much variability as evidence by the small standard deviation (.0391). The original 2004 survey asked homeowners about their opinions on residential and neighborhood environments. It had four different sections with a total of 69 items, plus a section on the participants’ socio-demographic characteristics. Sections included questions about satisfaction with various aspects of their homes and neighborhoods, perceptions of physical attributes in the neighborhoods, the perceived sense of community, and suppose you were moving situational questions. We chose five questions from the survey that relate to the study subject: resident’s ratings of naturalness and openness; ratings of the resident’s satisfaction with presence of trees, satisfaction with amount of open space, and satisfaction with density of housing in neighborhood; and ratings of their overall neighborhood satisfaction. Respondents made these ratings on a 1–7 Likert scales (Table 1). 2. Results

Fig. 2. Example of a setting (Normalized Differential Vegetation Index (NDVI) image for vegetation rate).

resolution) captured on August 1, 2005.4 First, we manipulated the satellite image to best fit the research (so called, Image Enhancement). Then, using the Normalized Differential Vegetation Index (NDVI) method and supervised classification5 in ERDAS image processing software, we classified these multi-spectral satellite images into two land cover typesdnon-vegetation and vegetationcovered areas. The Normalized Differential Vegetation Index (NDVI) is a radiometric attribute of the amount, structure, and condition of vegetation (Huete, 1988), which is calculated based on the visible wavelength band (RED) and the Near Infra-Red band (NIR) of the multi-spectral image.6 NDVI is related to vegetation coverage (areas with trees, shrubs and grass), with a value ranging 1 to 1. The typical range of NDVI is between about 0.1 for a not very green area to 0.6 for a very green area (Jackson, Slater, & Pinter, 1983; Lee, 2002; Tucker, 1979; Tucker, Newcomb, Los, & Prince, 1991). This study classified the land cover type as either a non-vegetation area (1  NDVI<0.1) or a vegetation-covered (0.1  NDVI  1). We defined vegetation rate as the percentage of vegetationcovered area per setting. Fig. 2 shows an example of an NDVI image of a residential-dominated suburban neighborhood with wellmaintained front and back yards in Franklin County. Dark gray cells represent non-vegetation areas while light gray cells represent vegetation-covered areas. Based on the categorization and suggested vegetation rate equation, this study found that the average vegetation rate of selected sample was 0.35 (SD ¼ 0.2273).

4 There was a longitudinal difference between objective and subjective attributes. Since we used the survey data attributed in 2004, the optimal satellite image would be the one captured at the same time (month and year). However, images from 2004 were in poor condition and covered by clouds. The best alternative was one captured on August 1, 2005. 5 Supervised classification is a type of automatic multi-spectral image interpretations. In a supervised classification, the identify and location of some of the land cover types, such as urban, agriculture, or wetland, are known a priori through a combination of fieldwork, analysis of aerial photography, maps and personal experience (Jensen, 1996, p.197). 6 NDVI ¼ (NIRRED)/(NIR þ RED).

The path model had physical measures of vegetation rate and building density as the exogenous variables predicting resident perception of naturalness and openness, satisfaction with them (presence of trees, amount of open space, and building density),7 and overall neighborhood satisfaction. As hypothesized, overall neighborhood satisfaction was associated directly with the physical measure of the attributes of the environment and indirectly with the attributes through perception and satisfied with them. Overall neighborhood satisfaction was associated with perception of and satisfaction with the attributes of the environment, which in turn related to one another. Table 2 summarizes the correlation matrix with standard deviations for all variables (Pearson Correlation Coefficients, n ¼ 664).8 All correlation coefficients that were significant at a 95% confidence level showed positive correlations between variables. We conducted path analysis with the CALIS, Covariance Analysis and Linear Structural Equations (Kline, 2005), using the maximum likelihood method of parameter estimation. We used the variance-covariance matrix as inputs. This model had a high and statistically significant likelihood ratio chi-square (c2 ¼ 25.03, df ¼ 7, n ¼ 664, p ¼ .0007). Because the over-identified characteristics of the model9 and the large sample size could lead to statistical significance, we also ran additional goodness-of-fit indices as suggested by previous research (Bagozzi & Baumgartner, 1994; Browne & Cudeck, 1993; Byrne, 2001; Hatcher, 1994; Hoyle, 1995; Kline, 2005; MacCallum & Austin, 2000; McDonald & Ho, 2002). The Root Mean Square Error of Approximation (RMSEA ¼ .06) suggested reasonable error of approximation; the Comparative Fit Index (CFI ¼ .99) and the Goodness-of-Fit Index (GFI ¼ .99) showed good fit between model and data as well. Fig. 3 shows the resulting path model with paths significant at 95% confidence level. The direction of prediction runs mostly from

7 The existing 2004 Homeowner Satisfaction and Mobility Decisions survey measured satisfaction with presence of trees instead of satisfaction with naturalness and two measures  satisfaction with amount of open space and satisfaction with building density–that might related to the perceived openness. 8 We treated ‘‘not applicable’’ or ‘‘do not know’’ answers as missing variables (total of 61). 9 The suggested path model was identified with 29 free parameters and 36 observations, which were calculated based on the equation, p(p þ 1)/2, where p ¼ the number of the observed variables.

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Table 2 Correlation matrix with standard deviations (Pearson Correlation Coefficients, n ¼ 664). Physical Vegetation rate Physical

Perceived

Evaluation (satisfaction with)

Vegetation rate (Std. Dev. ¼ .23) Building density (Std. Dev. ¼ .04)

Perceived Building density

Naturalness

Evaluation (satisfaction with) Openness

Satisfaction with presence of trees

Satisfaction with amount of open space

Satisfaction with building density

1.00 0.03

1.00

Naturalness (Std. Dev. ¼ 1.70) Openness (Std. Dev. ¼ 1.70)

0.27**

0.04

1.00

0.24**

0.06

0.55**

1.00

Satisfaction with presence of trees (Std. Dev. ¼ 1.26) Satisfaction with amount of open space (Std. Dev. ¼ 1.73) Satisfaction with building density (Std. Dev. ¼ 1.39)

0.29**

0.03

0.47**

0.36**

1.00

0.25**

0.00

0.47**

0.48**

0.46**

1.00

0.27**

0.06

0.40**

0.52**

0.44**

0.54**

1.00

0.25**

0.03

0.35**

0.45**

0.51**

0.55**

0.63**

Overall neighborhood satisfaction (Std. Dev. ¼ 1.24)

Overall neighborhood satisfaction

1.00

Note: Double asterisks indicate that the correlation coefficient is significant at 95% significant level.

left to right. The directional associations appear on single-headed straight arrows. The physical measures of the attributes of the environment (as exogenous variables) are on the left. The endogenous perceived attributes and evaluation of them are in the middle. Overall neighborhood satisfaction, the critical outcome attribute of the study, is on the far right. Values on the arrows show the standardized parameter estimates. R2 values are above each endogenous variable. Fig. 3 shows that overall neighborhood satisfaction has the largest association with resident satisfaction with building density, followed by resident satisfaction with presence of trees, and resident satisfaction with amount of open space. By one magnitude less, it is also associated with the perceived openness and the physical measures of building density.

As expected, satisfaction with building density (bottom of the third column) is predicted by satisfaction with amount of open space and perceived openness. Satisfaction with presence of trees (top of the third column) is predicted by perceived naturalness, satisfaction with amount of open spaces, and satisfaction with building density. It is also predicted to a lesser extent by the physical measure of vegetation rate. Satisfaction with amount of open space (middle of the third column) is predicted by perceived openness and perceived naturalness. Unexpectedly, perceived naturalness (top of the second column) is predicted primarily by the perceived openness and to a lesser extent by vegetation rate. The perceived openness had the largest overall association with overall neighborhood satisfaction through the combination of its direct and indirect associations with it amongst all attributes in the

Fig. 3. Path model of neighborhood satisfaction. 1) Vegetation Rate was correlated with openness (.24) and building density (.03). Openness was correlated with building density (.06). 2) Paths show the statistically significant associations between attributes. Standardized path coefficients appear on arrows indicating the strength of associations.

M. Hur et al. / Journal of Environmental Psychology 30 (2010) 52–59

study. Among physical and perceived attributes of the environment that planners and designers may find useful, perceived openness (.45) had the largest effect size and perceived naturalness (.18) had the moderate effect size by one magnitude over vegetation rate (.05) and building density (.06). 3. Discussion This study confirmed the multidimensional nature of neighborhood satisfaction (Bonaiuto et al., 1999; Francescato et al., 1979; Galster & Hesser, 1981; Newman & Duncan, 1979). We examined two physical, two perceived and three evaluative attributes in relation to overall neighborhood satisfaction. Results found significant factors, direct and indirect relationships to the dependent variable, and their strength of associations. The findings point to mixed success on the physical measures of attributes of the environment. Vegetation rate did relatively well. It predicted perceived naturalness and satisfaction with presence of trees directly, and overall neighborhood satisfaction indirectly. However, perceived naturalness had a stronger association with perceived openness than it did with the remote sensing measure of vegetation rate. Perhaps openness and naturalness covary at least in home-owned neighborhoods like the ones tested; or perhaps having people do the two ratings leads one to be associated with the other through ratings or a semantic effect. The physical measure building density did not work well in predicting perceived openness. Density differs from openness, which has more to do with the configurations of buildings. In addition, the physical measure of building density used the building ratio (footprints) in the area. It did not incorporate height and volume of buildings, which might affect the perceived density and satisfaction with it. Future research might try to integrate height and volume into the physical measure. Openness was an exogenous variable with a positive correlation with vegetation rate and a negative correlation with building density. The perceived measures also had mixed success. Openness emerged as the most important factor in relation to overall neighborhood satisfaction among all attributes (through the combination of its direct and indirect associations with it). Although it may relate to a sample of homeowners, the finding agrees with the previous studies, showing that people prefer single-family suburban homes (Audirac & Smith, 1992). Surprisingly, we only found indirect relations of perceived naturalness on neighborhood satisfaction. This may result from a possible moderating effect of resident’s ratings of perceived naturalness and overall neighborhood satisfaction (e.g., as Ellis, Lee, and Kweon (2006) suggested, high ratings on naturalness could relate to ratings on resident’s overall neighborhood satisfaction only for respondents who are satisfied with presence of trees in their neighborhood). This effect may arise from an artifact of multiple ratings or from other environmental attributes, which affect the satisfaction. For example, the perceived upkeep of trees and natural elements or the degree to which they block views of poor upkeep might affect neighborhood satisfaction. As suggested by Kaplan (1985), well-maintained naturalness may increase satisfaction with trees and eventually enhance overall neighborhood satisfaction. Poorly maintained naturalness (dilapidated naturalness) may evoke fear of crime (Nasar & Jones, 1997) and thus decrease overall neighborhood satisfaction. It may be arguable that satisfaction with amount of open space causes satisfaction with presence of trees and satisfaction with building density. We see that as perceived openness increases the perceived naturalness increases; and that as perceived naturalness increases, satisfaction with the presence of trees increase (people are more satisfied when there are more trees). Satisfaction with open space may lead to satisfaction with the presence of trees. However, the opposite direction may not work because satisfaction with trees

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may relate to not only trees in open space but also trees elsewhere (street trees, for example). Satisfaction with trees may not lead to satisfaction with amount of open space. Similar explanation may apply for the association between satisfaction with open space and satisfaction with building density. When perceived openness increases, people get less building density. Consider the demographic characteristics of sampledmajority were suburban homeowners who probably like their low-dense neighborhood environment–we may suggest that if people feel satisfied with the amount of open space, they would feel satisfied with the building density. Satisfaction with building density derives not only from open space but also from building heights, ratio, and building distribution; and as a result, we suggest that satisfaction with building density may not lead to satisfaction with the amount of open space. In focusing on the physical environment, the model did not include personal or social factors, such as neighborhood cohesion, or network, that presumably would affect overall neighborhood satisfaction. Nor did it include other personal attributes (age, gender, socio-economic status, general feeling or outlook on life) that might influence satisfaction and overall satisfaction ratings (Alvi et al., 2001; Bruin & Cook, 1997; Chapman & Lombard, 2006; Okun, 1993; Talen & Shah, 2007; Westaway, 2007). Presumably adding such measures to the model would increase its level of prediction. Verbal responsesdsuch as questionnaire surveys and interviewsdare the most widely used source of data in social science to learn about other people’s beliefs, attitudes, behaviors, feelings, perceptions, motivations, or plans (Judd, Smith, & Kidder, 1991). Although surveys may include respondents’ geographic location information to link to the questions on spatial references, the visualization of the survey data and its potential applications for spatial analysis fits well with the socio-ecological model in environment and behavior research (King, Stokols, Talen, Brassington, & Killingsworth, 2002). Refinement in our approach (GIS and satellite measures of environment attributes, resident perceptions and evaluations of them tied by geocoding to verbal measures) has potential to better understanding spatial and environmental factors related to human response. However, some improvements are needed. First, the questionnaire response rate of 32.2 percent leaves us uncertain of how well the findings would apply to all homeowners. Further work could try to test the model using in-person interviews to get a better response rate. Second, this study relied on single-item measure for each attribute, which might result a lower level of reliability than composite scores (Kline, 2005). Further study could develop tests and scales that include a group of indicators chosen to measure the same latent construct with a high level of validity and reliability. Third, high-resolution satellite imagery data availability is critical (Ellis et al., 2006; Lee, 2002). Our use of satellite images with 30 m  30 m resolution for vegetation rate often yielded cells with a building (non-vegetation) and a front/back yard (vegetation) combined. This would have lowered the vegetation rate values and the relations of vegetation with on overall neighborhood satisfaction. Forth, this study used updated and reliable GIS database. Unfortunately, reliance on GIS data from organizations such as ESRI and Census Bureau may miss information on new developments in rapidly growing areas. Among 837 survey responses, we excluded 112 sample due to the lack of street network information in the GIS shape file. The newer developments may differ from the others in systematic ways, which researcher might want to capture. As a test of the GIS and satellite, we limited the scope of this study to naturalness and openness. Consider residents who prefer living in a low-dense residential environment but also likes

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