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CROWDSOURCING Using geotagged photos to explore the relationship between cultural ecosystem service and site-specific features of urban green space in San Francisco DINING LIU | GRADUATE THESIS COMMITTEE MEMBERS| IRYNA DRONOVA | JOHN RADKE | PAUL WADDELL

ACKNOWLEDGMENTS This thesis cannot be completed without the guidance and encouragement from my thesis committee members, Iryna Dronova, John Radke, and Paul Waddell. I would also like to thanks Louise Mozingo, Chair of Landscape Architecture & Environmental Planning and my instructor of Final Project Preparation Studio, who dedicated a lot of time to review my thesis. I would specially thank Amir Gohar and Danika Cooper for constantly motivating me to work harder and keep confident. I also sincerely thank my friends and classmates who gave me highly useful feedback.

CONTENTS INTRODUCTION

DATA & METHODS

Site Selection and Description

Data Collection

Data Analysis

RESULTS

Spatial Variation of CES Indicated by Geotagged Photos

Temporal Variation in Geotagged Photo Counts

Photo Content and Classification

Regression Analysis

DISCUSSIONS

Mapping and Assessing City-wide CES Provided by UGS

Relationships between Site-specific Features and CES

Caveats and Limitations

CONCLUSIONS & FUTURE RESEARCH

REFERENCES

ABSTRACT

urban green space (UGS) is one of few places people in

cities can get in touch with nature and has provided multiple benefits for the public. Cultural ecosystem services (CES), known as non-material benefits human obtain from ecosystems including aesthetic, recreational value, and sense of identity, is the dominant category of benefits provided by UGS. However, lack of spatial explicit data, the mapping and assessment of city-wide CES is underdeveloped. Crowdsourced data from mobile devices, especially geotagged photos collected from photo-sharing platforms, cost-effective without spatial and temporal limitation, tend to be attractive source of information to evaluate CES. Geotagged photos in UGS can help understand peopleâ€™s interaction with landscape and the site-specific features related to CES provision. This study used geotagged photos from Instagram and Flickr to investigate the spatial and temporal pattern of CES provided by UGS in San Francisco during 2017 Summer. The final aim was to analyze the relationship between CES provision and site-specific features of UGS. To achieve this goal, firstly, I mapped

CES in three ways for Flickr and Instagram two groups and compare the results across different approaches and groups. Then I examined the UGS daily photo counts time series to detect anomalies, and used the tags of photos to understand the meaning of anomalies in terms of CES. After that I performed an automated content analysis of 5,034 and 10,000 photos from Flickr and Instagram using an online image recognition API - Clarifai, and applied Hierarchical Clustering Algorithm (HCA) to figure out visitorsâ€™ main interest in UGS. Based on the results of these analyses and literature review, I made hypotheses about the relationship between CES provision and five categories of site-specific features of UGS, and tested them using multivariate ordinary least squares (OLS) linear regression models and zero-inflated negative binomial regression models. Findings showed that the spatial patterns of CES quantified by photo counts and photo counts per hectare in UGS were quite different, but both of them were meaningful and necessary for assessing CES. UGS located in less dense area with a larger size, more disaggregated shape, more landmarks, waterbody, cultural and recreation spots, as well as a potential sea view were probably to get more photo counts, while UGS with waterbody, higher building and population density in surrounding neighborhoods could have more photo counts per hectare in San Francisco. The CES composition across all UGS in San Francisco varied a lot due to different site-specific features, which was reflected by diverse subjects of photos. These results could be used by environmental planners to understand city dwellersâ€™ preferences and behaviors in UGS at a large scale with lower costs compared to traditional approaches, and compare the trade-offs across different options of site-specific feature changes in UGS.

INTRODUCTION | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

INTRODUCTION

Urbanization has changed humans’ relationship with the nature a lot resulted in citizens’ less exposure to biodiversity (McIntyre, 2000; Turner et al., 2004). Urban green spaces (UGS) are the main places where people in cities can experience nature (Maller et al., 2009; Dickinson and Hobbs, 2017). There are many benefits human gain from UGS based on a rich literature (MacKerron and Mourato, 2013; Rehdanz and Maddison, 2008; Shanahan et al., 2015), among which, cultural ecosystem service (CES) is an important category (Summers et al., 2012). Cultural ecosystem services (CES) are defined as “non-material benefits people obtain from ecosystems through spiritual enrichment, cognitive development, reflection, recreation and aesthetic experience” (MEA, 2005). For example, public engagement with activity in UGS is considered to facilitate social cohesion, and mitigate exclusion and isolation of certain groups (Riechers et al., 2016; Zhou and Parves Rana, 2012). It has also been found that green spaces have a significant role in developing a sense of place, community identity and ownership of space (Milcu et al., 2013). The interaction between human and nature can also help people reduce stress and have some educational meaning (Riechers et al., 2015).

Figure 1.1. Approaches for assessing CES.

GRADUATE THESIS BY DINING LIU | CROWDSOURCING | INTRODUCTION

However, measurement and mapping of city-wide CES provided by UGS is underdeveloped. Crossman et al. (2013) pointed out that CES are least studied with only 18% of mapping studies among all types of ecosystem services, and quality indicators for assessing CES are still unclear though there are more and more publications on CES in recent years (Maes et al., 2016; Hernández-Morcillo et al., 2013). Most studies about CES are based on economic valuation such as the concept of willingness to pay (Barkmann et al., 2007; Rewitzer et al., 2017) and stated social-cultural preferences through interviews or specific surveys (Casado-Arzuaga., 2013; van Zanten et al., 2016). Nevertheless, such interviews or surveys are often costly and time-consuming due to massive effort required for the preparation and data cleaning process (Fig. 1.1). Another shortcoming of this approach is spatial and temporal limitation, because researchers in most cases do head counting and surveys at specific area only for a couple of hours or days. In recent years, the rapid development of social networking services (SNS) such as Facebook, Twitter and Instagram (Fig. 1.1), through which text messages and photos of landscapes, portraits, and so on are shared every minute on the Internet, provides the opportunity to improve traditional approaches to assess CES (Goodchild, 2007; Yoshimura and Hiura, 2017). Particularly, photos with volunteered geographic information, namely geotagged photos, have great potential to map CES as more details about human interests in site-specific features can be extracted from photo contents (Tenerelli et al., 2016; Richards and Friess, 2015; Casalegno et al., 2013). Two main improvements of using geotagged photos compared to traditional approaches to map and assess CES are: (1) providing substantial and cost-effective information on activities and preferences of people visiting UGS; (2)

overcoming limitations related to sample size, time and location constraints (Casalegno et al., 2013; van Zanten et al., 2016), non-response bias, and self-reported errors (Mayer-Schönberger and Cukier, 2013). Regarding the outlined research gap, the paper aims to study urban CES provided by UGS in San Francisco using geotagged photos from social media such as Instagram and Flickr. More specifically, I seek to answer the following research questions:

• How to map and assess city-wide CES provided by UGS?

• What’s the relationship between CES provision and site-specific features of UGS?

Since there are few resource of ecosystem services in urban context, such findings about regional specific preferences in UGS become even more important for decision-makers (Kabisch and Haase, 2014). Mapping city-wide CES provided by UGS can facilitate sustainable development including conservation planning, land-use management, and tourism planning (Plieninger et al., 2013; Brown and Fagerholm, 2015; Maes et al., 2012). Recreation and tourism as a key component of CES, has potential high economic value and is strongly related to market price. For example, residential price can be affected by the amount of ocean or lake views (Benson et al., 1998; Crossman et al., 2013). The results of mapping CES can help identify both popular and unexploited area and allow planners to devise strategies to enhance their value. Understanding how different site-specific features of UGS contribute to the provision of diverse CES help environmental planners evaluating trade-offs between different options of landscape change (Oteros-Rozas et al., 2017).

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

DATA & METHODS

To answer the first research question, static and dynamic mapping methods were applied and the results of different approaches and photo groups (Instagram and Flickr) were compared to get conclusions. Site-specific features of UGS were described by different categories of objective indicators to answer the second question. These objective indicators are measurable and mappable, more straightforward compared to subjective data such as revealed preferences from surveys or interviews, therefore used widely to support environmental planning and conservation (Lakes and Kim, 2012; Fontana et al., 2011). Figure 2.1. Methodology of the study.

GRADUATE THESIS BY DINING LIU | CROWDSOURCING | DATA AND METHODS

Kothencz and Blaschke (2017) found that both human perceptions and objective environmental indicators revealed different aspects of the same reality, thus were collectively needed to understand the role of UGS in quality of life. Hypotheses about the relationship between each indicator and CES provision were made based on observations, literature review, as well as the results of CES mapping and photo content analysis. Then I applied regression analysis to figure out the driven factors of the spatial pattern of CES, and compared the results with hypotheses to get conclusions (Fig. 2.1).

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

2.1 Site Selection and Description San Francisco is suitable for testing this methodology for three reasons. Firstly, there is a great amount of UGS in San Francisco. The total area of urban green space is over 5,500 acres owned by different agencies, among which the San Francisco Recreation & Parks Department (SFRPD) owned 3,433 acres of more than 220 parks. In this study, 251 UGS owned by SFRPD, state and federal government were investigated. Secondly, San Francisco urban green space includes a variety of types of spaces, providing various kinds of CES. Some are common places for recreation such as recreation centers, playgrounds, playing fields, some are unprogrammed or unstructured open areas, trails and natural areas, others are cultural arts and landmarks. Based on the report named The Economic Benefits of San Francisco’s Park and Recreation System (The Trust for Public Land, 2014), these UGS have provided multiple CES, including recreation and tourism value, aesthetic value, health value and community cohesion value. For example, the sales tax receipts from tourism spending by outsiders who came to San Francisco primarily for its UGS, had the value of nearly $47 million for the city of San Francisco, which demonstrated the rich tourism value provided UGS in San Francisco. The residents also benefit a lot from the UGS, getting physical activity in UGS which resulted in more than $49 million savings in medical costs. The community cohesion benefit of people banding together to save and improve their neighborhood parks, helps avert all kinds of antisocial problems that would otherwise cost the city more in police, fire, prison, counseling, and rehabilitation costs, was estimated to have the value at over $66 million. Thirdly, there is a great deal of geotagged photos available because San Francisco is a world-famous tourist destination. It is known for its cool summers, fog, steep rolling hills, eclectic mix of architecture, and landmarks, including the Golden Gate Bridge, cable cars, the former Alcatraz Federal Penitentiary, Fisherman’s Wharf, and its Chinatown district. Several of San Francisco’s parks and nearly all of its beaches form part of the regional Golden Gate National Recreation Area, one of the most visited units of the National Park system in the United States with over 13 million visitors a year.

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

Recreation & Open Space Element | San Francisco General Plan

BAY

101

RD ST

WAY ST

AVE

DIVISA

DERO

SS VAN NE

ST Franklin Square

AVE

BLV D

19TH

SUNSET

CESAR CHAVEZ

Bernal Heights Park

Glen Canyon Park

3R D S T

Heronâ€™s Head Park

B LV D

Mt. Davidson Park

Stern Grove

Lake Merced Park

Balboa Park

OC EAN AVE

IS SI ON

O C E A N

280

Potrero Hill Rec Center

P A C I F I C

24TH

AVE

Twin Peaks

Jackson Playground

POTRERO

Dolores Park

MISSION

Mt. Sutro Open Space

LA PORTO

SLOAT

16TH

CASTRO

TARAVAL

101

West Sunset Playground

Buena Vista Park

WAY

NORIEGA

B A Y

FELL

Golden Gate Park LINCOLN

Yerba Buena Gardens Civic Center

Alamo Square

C O

Rossi Playground

Lafayette Park

B LV D

GGNRA

BROAD

Alta Plaza Park

F U LT O N

Presidio

GEARY

F LU

LO M B A

N ID

S A

Fort Mason

Marina Green

McLaren Park

280

Fort Funston

Crocker Amazon G E Playground NEV A

C I TY A ND C OUNTY OF S A N FR A NC I S C O

101

Bay View Park

AV E

Candlestick Point SRA

S A N M A TE O C OUN TY

Existing Open Space

SFRPD Land in San Francisco City Limits: 3,433 acres State-Owned Land: 255 acres Candlestick, Mount Suwtro Federal-Owned Land: Ocean Beach, Fort Funston, Fort Mason, Lands End, Sutro Heights, China Beach: 642 acres Presidio: 1000 acres Other: 560 acres Campuses, pilot program schoolyards, SFPUC lands, SFRA parks, SF Port parks, linear open spaces such as boulevards and parkways, and privately owned, publicly accessible open spaces in the Downtown.

Figure 2.2. Urban Green Space in San Francisco (source: Recreation & Open Space, San Francisco General Plan). SAN FRANCISCO PLANNING DEPARTMENT

Miles

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

2.2 Data Collection 2.2.1 Developing Photo Database Geotagged photos have shown great potential to map and assess CES, while the choice of the photo-sharing platforms for investigation is critical because of differences in the represented users (Tufekei, 2014). In this study, I finally selected Instagram and Flickr due to their popularity, availability and quality of data, as well as ease of analysis. Instagram and Flickr are all worldwide photo-sharing social media, and they both provide Application Programming Interface (API) which allows public photos to be searched by metadata such as location and taken time. The good quality of photos and high accuracy of GPS almost within 10m (Zandbergen and Barbeau, 2011) enable the recent use in multiple disciplines of study (Figueroa-Alfaro and Tang, 2017; Wang et al., 2017; Brandt et al., 2017). However, the represented populations are quite different. Instagram now has over 800 million users, about seven times more than that of Flickr, 120 million users (Smith, 2018). Instagram is more popular among women than men, on the contrary, Flickr has 12% more men users than women (Anon., 2015). In terms of age, 59% of Instagram users are 18 to 29 year old (York, 2017) while less than 25% of Flickr users are in this age group. Flickr, with more percentage of older and high income users (Anon., 2015), is popular among amateur or professional photographers and has shared a great number of photos taken by professional cameras (Hausmann et al., 2017). Using geotagged photos from both Instagram and Flickr, this paper could get more insights about the discrepancy between the preferences of different represented groups, and have a more complete understanding of the CES provided by UGS in San Francisco. I created two datasets of geotagged photos using Instagram and Flickr API separately. Considering the wide variations in the size of UGS, people who visited those small UGS may take photos not strictly within the administrative boundary, probably on the road. Therefore, I collected geotagged photos taken within 20m buffer of UGS clipped by the boundary of San Francisco. The UGS shapefile was from California Protected Areas Database (CPAD), which contains

GIS data about lands that are public owned in fee and protected for open space purposes (Anon., 2017). Focusing on highly visited period â€“ Summer (6/21 to 9/22), I retrieved 5,034 Flickr photos and 174,112 Instagram photos taken in the summer of 2017. It is not surprising to see the huge difference of the amounts of photos after realizing the big difference of their user group sizes. Using Flickr API, I set Where On Earth Identifier (WOIED) to identify the location, the earliest and latest taken date, the media type as photos, and positional accuracy to be street-level which is the highest level Flickr can provide to filter the search. However, Instagram API only supports the photo search by setting search radius at most 5km. Therefore, I generated centroids for each UGS, changed search radius based on its size, and requested data with two-hour taken time interval. It may have some underestimation in highly popular area, because Instagram API has a rate-limit rule for free users, which restricts 20 items per request. Both datasets have metadata of retrieved photos including photo ID, the number of likes, coordinates, taken time, tags, and the link to image. 2.2.2 Describing UGS by Site-specific Features Site-specific features such as biophysical features, accessibility, and recreational facilities of UGS are highly related to the capacity of UGS to deliver cultural ecosystem services (Giedych and Maksymiuk, 2017). According to surveys and interviews about park visitation in terms of CES, sports facilities, accessibility, and aesthetics are of relative importance though the results varied by age (Veitch et al., 2017; Timperio et al., 2008; Kaczynski et al., 2008). Also, there is a link between spatially explicit indicators such as surrounding building height and density, and visitorâ€™s perceptions of UGS (Kothencz and Blaschke, 2017). Therefore, I used multiple categories of site-specific features including biophysical features, accessibility, attractions, demographics, and surrounding built-up structure to describe UGS. Biophysical Features In this study, I chose indicators of biophysical features from three aspects: size, shape and land cover. Usually, large size, compact shape, and water bodies can

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

promote the delivery of regulating ecosystem services, but the relationship with CES is unclear (Giedych and Maksymiuk, 2017). There is some evidence saying larger parks can promote greater feelings of connection with nature (Scopelliti et al., 2016; Bolund and Hunhammar, 1999). Vegetation structure and biodiversity can as well influence the perceived CES benefits (McGinlay et al., 2017; Breuste et al., 2013). Lack of the data about detailed species composition in UGS, I focused on land cover instead.

compared with that of other common classifiers such as Maximum Likelihood Classifier (MLC) and Neural Network (NN). However, the overlapping of shadows with other classes is a major concern for urban land classification (Dare, 2005; Sarabandi et al., 2004; Wu and Bauer, 2013). In this case, an effective way to remove shadow areas was to reclassify shadow objects based on its relative border to other image objects (Yuan and Bauer, 2006). So I used Polygon Neighbors tool (ArcMap 10.3) to find all neighbors of each shadow object, detected the neighbor class with longest shared edge length, and assigned shadows to new suitable class. In this way, the land cover of San Francisco was classified into six types including woody vegetation, grass/meadows, bare soil/sparse vegetation, water, sandy beach, impervious (building roofs, parking lots, paved roads). Additional, I downloaded layer of building footprints derived by Light Detection and Ranging (LiDar) data from San Francisco city GIS data portal (Quinn, 2017) to differentiate building roofs and other impervious area (Fig. 2.4).

Figure 2.3. The workflow of Object Based Image Analysis (OBIA).

Before calculating landscape metrics of land cover, I applied Object Based Image Analysis (OBIA), which provides over per-pixel analysis and has been increasingly used in remote sensing research since 2000 (Blaschke, 2010), to classify the land cover. The workflow is showed in Fig. 2.3. Firstly, I obtained high resolution National Agriculture Imagery Program (NAIP) satellite image of San Francisco from U.S. Geological Survey (USGS). NAIP acquires aerial imagery at a resolution of one-meter ground sample distance (GSD) for the United States during the agricultural growing season, or â€œleaf onâ€? conditions. Secondly, in ArcGIS for desktop, I used the Segment Mean Shift tool (ArcMap 10.3) to segment the site image based on infrared, red, and green bands, setting spectral detail as 16.5 and spatial detail as 8 after doing some small segmentation tests. Thirdly, I drew polygons in segmented raster dataset to collect at least 20 training samples for each expected class to train classifiers, and used the result of Support Vector Machine (SVM) classifier

Figure 2.4. San Francisco urban green space land cover map.

Based on this land cover map, I calculated the area of each UGS and all seven types of land cover in each UGS in ArcGIS. Then I generated the percentage of woody vegetation area, the percentage of vegetation area including woody vegetation and grass/meadow area, the percentages of impervious surface area with and without building footprints for each UGS, and a binomial variable indicating whether there is a waterbody in each UGS. To measure the diversity of land cover, I used Simp-

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

son’ Diversity Index (SIDI) which is borrowed from community ecology (Simpson 1949): ,

(1)

Where is the proportion of the landscape occupied by land cover type i. SIDI ranges from 0 to 1, indicating the probability that any two pixels selected at random would be different land cover types (He and Hu, 2005). When the UGS only have one land cover type, i.e., no diversity, SIDI equals to 0. SIDI approaches 1 when the number of different land cover types increases and the proportional distribution of area among different land cover types becomes more equitable. As for shape metrics, I used Landscape Shape Index (LSI) which has been widely used in landscape ecology and urban geography (Gyenizse et al.). It indicates the deviation of the shape of an landscape patch from the circle which is considered ideal ecologically (Patton, 1975; Cintra, 2014): ,

tors in influencing UGS visitation (Xu et al., 2017; Tan and Samsudin, 2017). Moreover, easy access was considered to be more important for the utilization of local parks rather than large national parks (Byrne et al., 2009). Public transportation including Bay Area Rapid Transit (BART) and Alameda Contra Costa Transit District (AC Transit) bus is highly utilized in San Francisco bay area, therefore nearest network distance to BART station and the number of bus stops within 300m buffer were represented as two indicators related to accessibility in this study. The map (Fig. 2.5) showed the process to find the nearest network distance to BART station described in table 2.1. Bicycle is another popular transport mode, and San Francisco Municipal Transportation Agency (SFMTA) has provided secure bike parking facilities on the sidewalk or in the parking lane, over 5200 bike racks at 3000 locations so far. In this study, I used a binomial variable indicating whether there is bike parking facility within 100m buffer around the UGS.

(2)

Where is the perimeter and is the area. LSI gets the minimum value of 1 when the shape of UGS is circular, i.e., maximally compact. LSI increases without limit as the shape of UGS becomes more fragmented.

Figure 2.6. The map of partial attractions in San Francisco urban green space.

Figure 2.5. Network analysis to find the nearest distance from urban green spaces to BART stations.

Accessibility UGS accessibility was known as one of the main fac-

Attractions It was generally true that UGS with a variety of amenities and features can meet visitors’ multiple needs (Jacobs, 1961; Kaczynski et al., 2011). Sports facilities, such as tennis courts, basketball field, recreation centers/pools, stadiums, children’s play area, dog play area and picnic area, is an important indicator to describe the amenities in the UGS. Besides, San Francisco has plenty of iconic landmarks such as the Golden Gate Bridge and Fisherman’s Wharf, which attract tourists from all over the world and uncover a richer history for the visitors. Some other cultural and recreational

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

facilities located in and around the UGS, such as museums, art galleries, public libraries, shopping center/ mall, and popular restaurants/bars/beer gardens, also attract a large number of people and provide recreation and cultural value. Moreover, the beauty of the seascape is another main attraction in San Francisco, thus the closeness to shoreline was also taken into consideration. The map (Fig 2.6) showed the 500m buffer area with potential sea view and locations of sport facilities and landmarks. Surrounding Built-up Structure Spatial features especially surrounding architectural features was one of the main neighborhood evaluative aspects in environmental psychology (Canter, 1983). Bonaiuto et al. (2003) confirmed building volume and density around UGS influenced human perceptions of the UGS. Here I used two objective indicators to describe the surrounding built-up structure: average building height in a 50 m buffer zone around each UGS and percentage of building area in a 50m buffer zone around each UGS (Kothencz and Blaschke, 2017). The map (Fig. 2.7) showed briefly the process to generate these indicators described in table 2.1. Demographics Neighborhood demographic characteristics such as population density, race and ethnicity, and household income could affect the utilization of UGS (Cohen et al., 2010), though the factors predicting visitation varied across different contexts (Andersson et al., 2015). In this study, population density in 300m buffer of the UGS was selected as the demographic indicator.

Figure 2.7. The map of buildings in 50m buffer of urban green space in San Francisco.

2.3 Data Analysis 2.3.1 Mapping and Assessing City-wide CES Provided by UGS Choropleth maps were used to visualize the geotagged photos counts, the geotagged photos counts normalized by the size of UGS, and the number of likes gained by geotagged photos in San Francisco UGS (Fig. 3.2). All the three ways assign each UGS average values, which can be the indicators of its CES at such a big scale. Compared with the first method, the second one takes the size, one of the important site-specific features of UGS into consideration, which can be interpreted as the average photos possibly be taken per unit area in each UGS. The third way tries to get rid of the limitation of geographic boundary, incorporating the influence of diffusion in social network into the assessment of CES, which can fully reflects the preferences of represented groups. For example, Flickr and Instagram tourists showed preferences to different elements of biodiversity in Kruger National Park, South Africa (Hausmann et al., 2017). Social event, such as the musical festival and the bike race, is an indispensable part of recreational value in CES (Zhou and Parves Rana, 2011). Timestamps and tags of photos make it possible to perform temporal analysis to detect social events. Data mining algorithms combined with interactive geo-visualization have been used to do spatio-temporal analysis (Kisilevich et al., 2010). In this study, I used Instagram photo datasets to do a simple test. After inspecting the time series and box plots of daily photo counts in a few parks, I extracted suspect outliers which had photo counts more than the upper inner fence, 1.5 times the Interquartile Range (IQR) above the third quartile. For each outlier date, I sorted unique tags for all the photos to find the top 5 tags (Fig. 2.8). In this way, I mapped the number, scale, and types of possible big social events in all UGS during the summer of 2017.

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU Table 2.1. Site-specific feature of urban green space in San Francisco.

Indicator

Type Size (hectare)

Data Source

Continuous

Meth

Calcu CPAD (California Protected Area Database)

Biophysical Features

Landscape Shape index (LSI)

Continuous

Percentage of woody vegetation

Continuous

Percentage

vegetation

(woody

vegetation and grass/meadow) Percentage of impervious surface (with

Accessibility

Calcu

Continuous

OBIA

and without building footprints)

Continuous NAIP Image, CPAD, and the layer of building footprints derived from LiDar data (city GIS data portal)

Waterbody

Binomial

Simpsonâ€™s Diversity Index (SIDI)

Continuous

The number of bus stops

Continuous SFMTA routes and stops data (city GIS data portal)

Crea

Nearest distance to BART Stations

Continuous SF Bay Area BART Stations (county GIS data portal)

Crea

Bike Parking Facilities

Binomial

The number of landmarks

Continuous Landmarks dataset (city GIS data portal)

Sports facilities

Binomial

Cultural & entertainment spots

Binomial

Google Map

Shore View

Binomial

San Francisco boundary (city GIS data portal)

Calcu

Bike Parking database (city GIS data portal)

Crea

Cultural and recreation facilities owned by the city (city Filter GIS data portal) and Google Map

play

Attractions

Average height of surrounding buildings

Man

garde

Crea

Continuous

Surrounding

(meter)

Built-up

Density of surrounding buildings

Structure

(100 square meters of building area per Continuous

Building footprints derived from LiDar data (city GIS data portal)

Crea

hectare) Demographics

Population Density (people per hectare)

Continuous

2016 Planning Database and San Francisco boundary (city GIS data portal)

Calcu

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

hod

ulating geometry in ArcGIS

ulating

in Jupyter Notebook

A Classification in ArcGIS and Calculation in Jupyter Notebook

ulating

in Jupyter Notebook

ating a 300m buffer for each UGS to see how many bus stops are in the buffer in ArcGIS

ating a 15m buffer for each UGS to figure out all street junctions, and then finding nearest BART station using network analysis in ArcGIS

ating a 100m buffer for each UGS to see whether there is bike parking facilities in ArcGIS

ating a 50m buffer for each UGS to see the number of landmark in ArcGIS

ring facilities within the group including tennis courts,

basketball field, recreation centers/pools, stadiums, children's play area, dog

area, picnic area, and so on. Manually identifying whether there are sports facilities for other UGS not owned by the city

nually checking whether there are museums, art galleries, public libraries, shopping center/mall, or popular restaurants/bars/beer

ens in 50m buffer of UGS

ating a 500m buffer inside the coast to see which UGS are overlapped in ArcGIS

ating a 50m buffer for each UGS in ArcGIS and calculating all the variables in Jupyter Notebook

ulating the average population density in 300m buffer of UGS based on the estimation of population at census group level

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

“Outside Lands”

“Summer of Love”

Figure 2.8. The example for Golden Gate Park big event detection in 2017 Summer using Instagram geotagged photos. (left: daily geotagged photo counts time series in Golden Gate Park; right: the box-plot for geotagged photo counts in Golden Gate Park; highly repeated tags were annotated on the left figure).

For better visualization, I also created interactive maps showing the static and dynamic spatial patterns of geotagged photos using Carto, an online platform which provides GIS and web mapping tools (Scott, 2014). This is a good way to communicate with the public and specific audience, showing graphic summary of data attached with the map. 2.3.2 Photo Content Analysis To understand the relationship between CES provision and site-specific features, the indices such as photos counts and density used in mapping city-wide CES in 2.3.1 are not enough. Photo content analysis can help understand human interactions with different features in the site and the specific activities people engaged in. For example, photo pairs were successfully used in survey to investigate landscape preferences (Atauri et al., 2000; Dorwart et al., 2010). Recently, Guerrero et al. (2016) manually categorized geo-tagged photos based on elements and types of nature defined by Buijs (2009). However, manually categorization of photos is arduous and can result in some errors when the image

dataset is extremely large (Richard and Tuncer, 2017). Therefore, in this study, I used Clarifai, an image recognition tool helping rapidly analyze the content of photos (Clarifai, 2017). I applied the “General” model (Fig. 2.10) automatically predicted concepts in the photos with the values of their probabilities. All Flickr photos and 10,000 out of 174,112 Instagram photos (confidence interval < 1) were assigned top 5 labels according to the result of content analysis and then clustered based on their similarity in labels using Hierarchical Clustering Algorithm (HCA), a widely used algorithm for both spatial and text clustering analysis (Richard and Tuncer, 2017; Oteros-rozas et al., 2017).

Figure 2.9. Clarifai pre-built “General” model demonstration.

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One outstanding advantage of HCA in this case was that I did have to know the number of clusters of photos, i.e. the number of main subjects of photos in advance. All I need to prepare was an n × m distance matrix Χ , where n meant the number of photos I collected, m meant the number of unique labels. For each photo, it was described by a long binary vector, with 0 value for labels they did not have and 1 value for labels they had. Then I generated the linkage matrix Z using ‘ward’ method which can be used to calculate the dissimilarity between newly formed clusters, in other words, comparing the proportion of the labels assigned to one photo that did not match the labels assigned to another photo. After checking the Cophenetic Correlation Coefficient (CCC) , which very briefly compared the actual pairwise distances of all samples to those implied by the hierarchical clustering, I knew in this project the clustering preserved the original distances well (CCC > 0.5, the closer to 1 the better). The dendrogram (Fig. 2.10) showed the order and dissimilarity of merges during the hierarchical clustering of Flickr photos. Then I retrieved 6 clusters for Flickr photos and 7 clusters for Instagram photos based on the self-determined maximal dissimilarity (y = 32). At last, I interpreted these clusters based on the bar-plots of top 5 labels (Fig. 2.12) and sample photo check.

Figure 2.10. The dendrogram of hierarchical clustering of 5034 Flickr photos into 6 clusters.

Figure 2.11. Interpretation of 6 retrieved clusters of Flickr photos based on top 5 labels in each cluster.

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

Table 2.2. Hypotheses of the relationship between si

Independent Variables

Size (hectare) Landscape Shape index (LSI)

Biophysical Features

2.3.3 Statistical Analysis Firstly, based on the observation and literature review, I proposed a series of hypotheses about the relationship between the site-specific features (Table 2.1) and CES provision in UGS quantified by photo counts and photo counts per hectare (Table 2.2). To find the driven factors of the spatial patterns of CES, I constructed multivariate ordinary least squares (OLS) linear regression models with different sets of indicators (Table 2.1) in Jupyter Notebook (Project Jupyter, 2014). Photo counts and photo counts per hectare as proxies of CES provided by UGS were two dependent variables in the models. Only Instagram geotagged photos were used in statistical analysis for fewer zero photo counts in UGS contributed by the larger number of photos. Before making statistical models, I checked the distribution of dependent and independent variables and transformed part of them to better meet the assumptions of OLS models. Then I ranked these models using the small-sample-size corrected version of Akaike Information Criterion (AICc) following (Burnham and Anderson, 2002):

Percentage of woody vegetation

Percentage of vegetation (woody vegeta grass/meadow) Percentage of impervious surface (with footprints) Waterbody

Simpsonâ€™s Diversity Index (SIDI)

(3)

Accessibility

The number of bus stops

Nearest distance to BART Station Bike Parking Facilities

Attractions

The number of landmarks Sports facilities

Cultural & entertainment spots

Built-up Structure

Shore View

hics

Where denotes the model log-likelihood, n is the number of observations and k is the number of parameters used in the model. However, with 17 independent variables, the number of possible combinations becomes extremely large with high risk of multi-collinearity. Therefore, I only estimated regression parameters and AICc for models with 5 or fewer indicators, and for the final set of candidate models only the models within 4 AICc units from the minimum AICc were taken into consideration (Burnham and Anderson, 2002). Regarding there were still 56 out of 251 UGS with zero photo, I also estimated a zero-inflated negative binomial regression using the package pscl (Version 1.5.2, 2017) in RStudio (Version 1.0.143; RStudio Inc., 2017) with photo counts in each UGS as the dependent variable. This approach is suitable for modeling overdispersed count outcome variables, assuming that two different processes generated true and excessive zeros (Erdman et al., 2008). In this study, the UGS with zero photos may have both true zero counts generated by logit model and excessive zeros generated by count model, e.g., someone took photos but not geotagged in the UGS during 2017 Summer. The output models were ranked and selected similarly as what I did for output OLS models, and selected independent variables in the final sets were compared with those in OLS results.

Surrounding

Demograp

Average height of surrounding buildings

Density of surrounding buildings (100 square meters of building area per

Population Density (people per hect

DATA AND METHODS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

ite-specific features and CES provision indicators.

Relationship with Dependent Variables (+/-) References Photo counts

Photo counts

per hectare

Result 3.1 and Scopelliti et al., 2016; Bolund and Hunhammar, 1999; Kaczynski et al., 2008

ation and

Frank et al., 2013

+ + Kaczynski et al., 2008; McGinlay et al., 2017; Breuste et al., 2013

building

)

de la Fuente de Val et al., 2006; Frank et al., 2013

Veitch et al., 2017; Timperio et al., 2008; Kaczynski et al., 2008; Xu et al., 2017; Tan and Samsudin, 2017

s (meter)

s hectare)

tare)

Giedych and Maksymiuk, 2017

Veitch et al., 2017; Timperio et al., 2008; Kaczynski et al., 2008

Giedych and Maksymiuk, 2017

Result 3.1

+ Canter, 1983;

Bonaiuto et al. , 2003

Cohen et al., 2010; Andersson et al., 2015

RESULTS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

RESULTS

3.1 Spatial Variation of CES Indicated by Geotagged Photos

Figure 3.1. The interactive map of Flickr geotagged photos taken in San Francisco urban green space during the summer of 2017. (https://deenaliu.carto.com/builder/47046dc7-8366-411c-a3c3-7d9e55ab6b12/embed)

The interactive map (Fig. 3.1) showed the spatial pattern of geotagged photos taken in San Francisco UGS during the summer of 2017. Each point stood for a geotagged photo, and the color shade of the point illustrated the number of likes on this photo. When users clicked on the point, the photo and the title would pop out. This map made easier to understand and communicate the collected data. The geotagged photos were neither evenly distributed across different UGS, nor evenly distributed within each UGS. UGS close to shoreline and near downtown tended to have much more photos. Comparing the maps created in the three ways (across rows in Fig. 3.2), it was obviously to see the discrepancy in results. In the maps of the Instagram group (the left column in Fig. 3.2), Golden Gate Park and Golden Gate National Recreation Area (NRA) were the best two in terms of the photo counts, while they turned to be lower than the average when normalized by area. By contrast, some small parks performing under the average in respect of photo counts and number of likes on photos had much higher photo counts per hectare, such as Agua Vista Park and Patriciaâ€™s Green. The maps of photo counts and the maps of number of likes on photos were relatively consistent, with a slight fluctuation in the rankings. Comparing results of Flickr and Instagram groups, the spatial patterns of

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cultural ecosystem services mapped in the three ways appeared to be similar. The photo counts and number of likes on photos in small size of UGS were mostly low, while in medium and large size of UGS close to the north and west shoreline, the numbers were all relatively high. Some UGS such as Golden Gate Park, Presidio, Golden Gate NRA, and Fort Mason were all on top 10 lists of photos counts and number of likes on photos regardless of data source. However, the unmatched part, for example, Embarcadero Plaza and Civic Center Park performing well in the Flickr group while poor in the Instagram group with respect to photo counts, implied the different preferences of the two represented groups. Also, when concerning photo counts normalized by area, small UGS in downtown San Francisco performed better in Flickr group than that in Instagram group.

3.2 Temporal Variation in Geotagged Photo Counts

Figure 3.3. The dynamic map showing the animation of spatial distribution of Flickr geotagged photos by day in the summer of 2017.

Figure 3.4. Daily Instagram and Flickr geotagged photo counts time series in the summer of 2017

Figure 3.5. Box-plots for Instagram and Flickr geotagged photo counts for weekdays and weekends.

The dynamic map (Fig 3.3) was beneficial for visualizing the spatial-temporal variation of geotagged photo counts in San Francisco UGS. The bar plot attached to the dynamic map displayed the degree of fluctuation in 2017 summer daily photo counts, from 0 to 293 in the Flickr group and from 0 to 2478 in the Instagram group. Juxtaposing Flickr and Instagram daily geotagged photo counts time series (Fig 3.4), the correlation coefficient of the two groups was 0.13 (p = 0.22 > 0.05), which indicated there was inconclusive evidence about the significance of the association between the change rules of photo counts for the two groups. When summarizing the photo counts for weekdays and weekends (Fig 3.5), it was evident that photo counts on weekends were much higher than on weekdays in both Instagram and Flickr groups. The outliers in box-plots were potentially due to the effect of big social events.

RESULTS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU 19

Figure 3.2. Three ways to map cultural ecosystem services provided by urban green space in San Francisco using geotagged photos. The first row are choropleth maps showing Instagram and Flickr geotagged photo counts in San Francisco UGS during 2017 Summer. The second row are choropleth maps showing Instagram and Flickr geotagged photo counts per hectare in San Francisco UGS during 2017 Summer. The third row are choropleth maps showing number of likes on Instagram and Flickr geotagged photos in San Francisco UGS during 2017 Summer. The top 10 UGS are annotated in each map. Maps on the left are based on Instagram geotagged photos, and the right part are based on Flickr geotagged photos.

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Figure 3.6. The map of the numbers of big event days in San Francisco urban green space during the summer of 2017 based on Instagram geotagged photos. On the right are top 4 urban green space annotated with some highly repeated tags the users posted.

Using box-plots for geotagged photo counts and tags of photos, I created the map showing how many big event days in San Francisco UGS (Fig 3.6). The results indicated 26 out of 251 UGS had big events in the summer of 2017, among which the top four UGS were Sigmund Stern Grove, Golden Gate Park, Yerba Buena Garden, and Bernal Heights Park. Apparently, big events took place not only in some big size UGS such as Golden Gate Park and Lake Merced Park, but also in some small and medium size UGS, for example, Yerba Buena Garden which is almost 1 hectare and Excelsior Playground which is only 0.6 hectare, had 5 and 4 big event days in 2017 Summer respectively. Nonetheless, the scale of events was affected by the size of UGS. For instance, the number of photos posted at Golden Gate Park (about 414 hectare) in extreme event days exceeded over 600, while at Excelsior Playground the number of photos was over 40 in extreme event days. Both the capability of holding big events and the types of events in UGS can reflect the partial recreational and social value, an important component of

CES. According to the top tags attached to days with extreme value, Events were diverse but mostly about art, music and culture. Annual events such as Outside Lands in Golden Gate Park and Stern Grove Festival in Sigmund Stern Grove provided free concerts, art exhibitions, food, and drinks. Pistahan in Yerba Buena Garden were also related to art and culture, but specifically for Filipino and Filipino-American. Some festivals had historical and religious meaning, like The Sound in McLaren Park, a nation-wide Jesus Festival. Other events such as the race held by Colon Cancer Coalition in Lake Merced Park were sport related. Besides, there were some temporal spontaneous small events, for example, communities had BBQ to celebrate Independence Day on July 4th, 2017.

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Figure 3.7. Bar charts of subjects of clustered Flickr and Instagram geo-tagged photos (left: Instagram; right: Flickr).

Table 3.1. Interpretation of subjects of clustered Flickr and Instagram geotagged photos

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3.3 Photo Content and Classification The subjects of clustered Flickr and Instagram photos in summer 2017 (Table 3.1) were quite similar, though there was a little difference about small subjects (Fig. 3.7). “People” was the most popular subject, about 30% in both Flickr and Instagram groups. “streetscape, art and food” “surroundings of buildings” “vegetation landscape” “water landscape” and “animals” all presented in two groups, while in Flickr group “animals” subject was less conspicuous and mixed with “vegetation landscape” subject. There was a special small subject “sky landscape” in Instagram group which related to sunset, dusk, dawn and nightscape. “Ball game” was a particular subject in Flickr group which was mostly about people playing baseball game. The subjects of posted photos reflected what types of landscape might have more aesthetic and recreation value. The spatial pattern of different subject photos indicated types of CES provision by each UGS were different due to different site-specific features (Fig. 3.8 and Fig. 3.9). For example, some UGS in downtown San Francisco such as Union Square and Civic Center had no “water landscape” photos because they were in lack of water features and away from the sea, but they had much more photos about “surroundings of buildings” due to the impressive look of surrounding architecture. An extreme example is Alamo square (Fig. 3.10), a 5 hectare UGS on the top list of all three ways of mapping (Fig. 3.2) in both Instagram and Flickr groups. It had no photos about “water landscape”, few photos of “vegetation landscape”, but a great amount of photos about “surroundings of buildings” and “people” contributed by the world-famous “Painted Ladies”, a row of Victorian houses facing the park on Steiner Street. This historic square offers quintessential and photogenic scenes of the city. Here is another example of high performance. Fort Point National Historical Site, a waterfront green space overlooking the spectacular Golden Gate and San Francisco Bay, also had a lot of photos about “surroundings of buildings” because of Fort Point, a masonry seacoast fortification with beautifully arched casemates defending the San Francisco Bay against hostile warships for long. Addi-

Figure 3.8. Instagram and Flickr geotagged photos of “wa

Figure 3.9. Instagram and Flickr geotagged photos of “su

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ater landscape” subject.

urroundings of buildings” subject.

Figure 3.10. The example of comparing CES contributed by different site-specific features at individual UGS scale. The compositions of geotagged photos of different subjects were different in Alamo Square Park and Fort Point National Historical Site.

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tionally, there were many “vegetation landscape” indicating high aesthetic value of natural landscape in the UGS. These site-specific features collectively attract the influx of large numbers of people, which contributed to many “people” photos. In this case, the multiple site advantages resulted in a rich supply of CES (Fig. 3.10). Though the overall performances of both Alamo Square Park and Fort Point National Historical Site were excellent with the respect of CES (Fig. 3.2), the CES contributed by different site-specific features could be quite different based on the results of photo content analysis (Fig. 3.10). Even geotagged photos with some specific labels can be really useful for environmental planners and landscape designers with regard to their geo-locations and contents. The relationship between nature and CES is widely accepted, but there are few studies about how CES provision responds to variations in species group (Hooper D. U. et al., 2005). In this case, photos with labels such as “bird”, “flower” and “tree” can be extracted and studied further. The “bird” photo map (Fig. 3.11) indicates people’s preference and their behaviors to some degree. Golden Gate Park, for example, is an important stop for many birds on their main migration route, the Pacific Flyway, and there is also a large quantity of interesting resident birds. Visitors in response took much more “bird” photos than any other UGS. The geo-locations of photos clustering around open water shows where people usually observe birds, and the photo contents (Fig. 3.12) shows what kind of birds people could and would like to observe in Summer. Common species such as Anna’s Hummingbird, sparrows, and Black Phoebe were repeated in photos a few times. August is the start month for migratory birds, and vast number of birds will rest and feed in the city and by the bay till December. In response, the spectacular scenes of shorebirds present in hundreds of thousands with spillovers into parklands near the coast were frequently recorded by visitors. However, Lake Merced Park, with wide open water and approximately 48 year-round bird species, had no photos about bird in both Instagram and Flickr groups partly because of its less convenient location. It is also unexpected to find some bird photos in highly urbanized area, including flocks of migratory birds and common birds such as robins, pigeons and hummingbirds. In

this way, planners can compare the supply and demand of CES provided by biodiversity, and designers can learn from popular compositions of species and enrich the biodiversity in their design. The insights about how CES provision responds to variation in biodiversity across different UGS, can thus engage public in conservation to enhance ecosystem services (McGinlay et al., 2017).

Figure 3.11. The map of Instagram and Flickr geotagged photos with label “bird”.

Figure 3.12. Partial collection of Instagram and Flickr geotagged photos with label “bird”.

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3.4 Regression Analysis Given two metrics of CES provided by UGS including photo counts and photo counts per hectare in each UGS, all the independent variables in Table 2.1 showed positive effects in univariate OLS models, except that “the number of landmarks” had high p-value (p > 0.1) for photo counts per hectare, which meant there was weak evidence to reject that “the number of landmarks” had no effects on photo counts per hectare (Table 3.2). However, the relative importance of independent variables in univariate OLS models between two groups was fairly different. “Density of surrounding buildings” was the most important indicator for photo counts per hectare while the least important one for photo counts, which was similar in the cases of

“population density”, ”percentage of impervious surface” and “average height of surrounding buildings”. “Size”, ”the number of landmarks” and ”waterbody” with positive effects were the top 3 strongest indicators for the photo counts group, which also combined together to be the lowest AICc multivariate model for the same dependent variable (Table 3.3). However, none of the univariate models in Table 3.2 was within 4 AICc units from the lowest-AICc multivariate models in Table 3.3. Obviously, univariate OLS models in Table 3.2 could only provide limited interpretation for the changes of photo counts per hectare (maximum R²= 0.07) respectively comparing with that for photo counts in

Table 3.2. Relative importance of independent variables with p-value > 0.05 in univariate ordinary least squares (OLS) regression models of two metrics of cultural ecosystem service (CES) provided by urban green space (UGS), sorted by Akaike Information Criterion (AICc) in ascending order. Dependent Variable:

Photo Counts in each UGS

Independent Variable

AICc p-value R²

size

4592.8 0.00 0.54

the number of landmarks

Positive/

Photo Counts per Hectare in each UGS Positive/

Independent Variable

AICc p-value R²

density of surrounding buildings

4100.9 0.00 0.07

4658.6 0.00 0.40

Sqrt (population density)

4101.4 0.00 0.06

waterbody

4715.4 0.00 0.25

Simpson’s Diversity Index (SIDI)

4102.8 0.00 0.06

shore view

4747.5 0.00 0.15

Sqrt (% impervious surface - buildings included)4103.1 0.00 0.06

Sqrt(the number of bus stops)

4752.3 0.00 0.13

average height of surrounding buildings

4104.1 0.00 0.05

Landscape Shape Index (LSI)

4760.4 0.00 0.10

Sqrt(the number of bus stops)

4104.2 0.00 0.05

cultural & entertainment spots

4762.4 0.00 0.10

sports facilities

4105.5 0.00 0.05

nearest distance to Bart stations

4771.7 0.00 0.06

bike parking facilities

4105.9 0.00 0.05

Simpson’s Diversity Index (SIDI)

4775.0 0.00 0.05

nearest distance to Bart stations

4106.4 0.00 0.04

Sqrt (% woody vegetation)

4776.1 0.00 0.05

cultural & entertainment spots

4107.3 0.00 0.04

bike parking facilities

4777.7 0.00 0.04

waterbody

4107.4 0.00 0.04

% grass

4778.2 0.00 0.04

Sqrt (% woody vegetation)

4109.4 0.00 0.03

average height of surrounding buildings

4778.7 0.00 0.04

% grass

4110.3 0.01 0.03

Sqrt (population density)

4779.9 0.00 0.03

Landscape Shape Index (LSI)

4112.3 0.02 0.02

Sqrt (% impervious surface - buildings included)4781.3 0.01 0.03

shore view

4113.3 0.03 0.02

Negative Effect

sports facilities

4781.4 0.01 0.03

density of surrounding buildings

4784.1 0.05 0.02

Negative Effect

RESULTS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

Table 3.3. Multivariate ordinary least squares (OLS) regression models for two metrics of cultural ecosystem service (CES) provided by u space (UGS) within 4 units of AICc from the lowest AICc OLS model.

Number Models Dependent variable:

AICc

RÂ˛

RaÂ˛

Photo Counts in each UGS

size + waterbody + the number of landmarks

4487.7

0.68

0.67

size + waterbody + the number of landmarks + LSI + sports facilities

4488.9

0.68

size + waterbody + the number of landmarks + Sqrt(the number of bus stops) + density of surrounding buildings

4489.7

0.68

size + waterbody + the number of landmarks + Sqrt(the number of bus stops) + Sqrt(population density)

4489.9

0.68

size + waterbody + the number of landmarks + Sqrt(the number of bus stops) + sports facilities

4490.0

0.68

Dependent variable:

Photo Counts in each UGS (without "size" as an independent variable)

waterbody + the number of landmarks + LSI + cultural & entertainment spots density of surrounding buildings

4564.6

0.57

waterbody + the number of landmarks + LSI + cultural & entertainment spots Sqrt(population density)

4566.6

0.57

0.56

waterbody + the number of landmarks + LSI - density of surrounding buildings

4567.1

0.56

waterbody + the number of landmarks + LSI + cultural & entertainment spots Sqrt (% impervious surface - buildings included)

4567.3

0.57

0.56

waterbody + the number of landmarks + shore view

4567.6

0.55

waterbody + the number of landmarks + LSI - Sqrt(population density)

4568.2

0.56

0.55

waterbody + the number of landmarks + LSI + cultural & entertainment spots average height of surrounding buildings

4568.2

0.57

0.56

waterbody + the number of landmarks + cultural & entertainment spots

4568.3

0.55

waterbody + the number of landmarks

4568.5

0.55

0.54

Dependent variable:

Photo Counts per Hectare in each UGS

waterbody + density of surrounding buildings

4072.0

0.09

waterbody + Sqrt(population density)

4075.0

0.08

0.07

density of surrounding buildings

4075.4

0.07

0.06

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urban green

Δ AICc

0.00 1.16 1.97 2.15 2.30

0.00 1.98 2.53 2.74 2.99 3.57 3.60 3.67 3.95

0.00 2.98 3.32

each UGS, which was consistent in candidate multivariate OLS regression models (Table 3.3). All of the candidate multivariate OLS regression models within 4 AICc units from the minimum-AICc multivariate models (Table 3.3) for photo counts could interpret about 68% of variability in the response, while the candidate set for photo counts per hectare was only able to interpret at most 9% of changes in photo counts per hectare in each UGS. Still, it was noteworthy that “waterbody” was included in both candidate sets, which meant the mean of photo counts and the mean of photo counts per hectare in UGS would increase if the UGS had open water. “Density of surrounding buildings”, ranked the first in Table 3.2 with a positive effect, was included in the candidate multivariate OLS regression model for photo counts per hectare. This implied when there was 100 more square meters of building area per hectare of the surrounding 50m-buffer area, the mean of photo counts per hectare in UGS would gain 6 more controlling for “waterbody” (Table 3.4). Similarly, “population density” which seemed to be important for photo counts per hectare in univariate models (Table 3.2) was also in the candidate set of multivariate OLS regression models, positively associated with photo counts per hectare in UGS. It was remarkable that “size” dominated the candidate models for photo counts based on its high performance in the univariate model (R²= 0.54). Since the goal of regression analysis was not building an optimal predicting model but exploring the driven factors for city-wide CES spatial pattern, I set “size” and “the number of bus stops” (collinearity issue) aside and found another 9 candidate multivariate OLS regression models for photo counts, surprisingly, with relatively high r-squared value (Table 3.3). Both “waterbody” and “the number of landmarks” with positive effects presented in all the candidate models for photo counts in UGS no matter whether “size” was taken into consideration, and “LSI” presented 6 times in the new candidate set of multivariate OLS regression models with a positive effect which meant when the shape of UGS became less compact or circular, and more disaggregated, the mean of number of photos would probably increase with other independent variables held constant. Some other factors including “shore view” and “cultural & entertainment spots” were also positive and statistically significant. Nevertheless, “density of surrounding buildings”, “population density”, “average height of surrounding buildings” and “percentage of impervious surface” which appeared to have positive effects in univariate OLS models (Table 3.2), were negatively associated with photo counts in candidate multivariate OLS models (Table 3.3). Variables selected by the highest-rank zero-inflated negative binomial models for photo counts in UGS were accordant with the OLS results (Table 3.3), including “size”, “waterbody”, “shore view” and “the number of landmarks” with positive effects and “LSI” with a negative effect, on the contrary to that in OLS models, in the count model component. The first one and “population density” were also selected in the zero-inflated model component with a positive effect and negative effect respectively (Table 3.5). In addition, I tested the interactions among indicators, nonetheless, most of them seemed to be insignificant and contributed no explanatory power comparable to the best-fit multivariate sets in Table 3.3. The degree of positive effects of binomial variables was comparable in candidate models. Comparing the coefficients of “waterbody” and “shore view” in model 5 (Table 3.4), the mean of photo counts would increase much more when the UGS had “waterbody” rather than “shore view” after adjusting for other variables. Similarly in model 1,2,4,7 and 8, “waterbody” had a bigger positive effect than “cultural & entertainment spots” with other variables held constant.

RESULTS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU Table 3.4. Summary of candidate multivariate ordinary least squares (OLS) regression models listed in table 3.3 with photo counts in each UGS (without “size” as an independent variable) as the dependent variable. Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

waterbody

2929.72*** 3042.42*** 3129.25*** 3389.63*** 3191.58*** 3218.16*** 3172.15*** 3532.81*** 3728.19*** (475.98) (467.48) (474.83) (455.97) (482.00) (467.19) (465.20) (429.10) (421.44)

the number of landmarks

1758.51*** 1767.87*** 1809.34*** 1699.89*** 1728.44*** 1816.99*** 1726.59*** 1704.02*** 1759.05*** (138.56) (139.03) (138.66) (138.25) (138.08) (139.14) (138.80) (140.11) (138.50)

Landscape Shape Index (LSI)

352.52*** (121.38)

cultural & entertainment spots

990.92** (386.74)

density of surrounding buildings (m²/ hectare)

- 0.22*** (0.07)

356.30*** (122.73) 970.92** (385.92)

260.82** (109.44)

399.62*** (134.53)

1006.37** (392.86)

1114.15*** 776.37*** (406.91) (373.76)

- 0.18*** (0.06) -78.39*** (23.50)

Sqrt(population density)

316.65** (126.08)

-66.61*** (23.28)

Sqrt (% impervious surface - buildings included)

-107.23*** (36.54) 882.87** (394.63)

shore view

-63.25*** (22.73)

average height of surrounding buildings R²

0.57

0.56

0.57

0.55

0.56

0.57

0.55

Ra²

0.57

0.56

0.55

0.56

0.55

0.54

Standard error in parenthesis Significance codes: * < 0.1, ** < 0.05, *** < 0.01

Table 3.5. Variables selected by the highest-rank zero-inflated negative binomial regression models for photo counts in UGS within 4 units of AICc from the lowest AICc model. Count Model

Zero-inflation Model

AICc

Δ AICc

Ln(size) - LSI + waterbody + shore view

Ln(size)

2482.16

0.00

Ln(size) + waterbody

Ln(size)

2484.05

1.89

waterbody + the number of landmarks

Ln(size) - Sqrt(population density)

2485.52

3.36

GRADUATE THESIS BY DINING LIU | CROWDSOURCING | DISCUSSIONS

DISCUSSIONS

4.1 Mapping and Assessing City-wide CES Provided by UGS Geotagged photos from social media as a proxy of CES were mapped in three ways in the results (Fig. 3.2). The sharp difference between the spatial patterns of photo counts and photo counts per hectare with the supplementary understanding of the cultural importance of UGS ranked top in either way demonstrated the necessity to map CES in multiple ways. For example, Patriciaâ€™s Green, a popular pocket park featuring temporary art installations providing picnic area and a playground for Hayes Valley neighborhood, performed pretty well from the respect of photo counts per hectare though it was low-ranking in terms of photo counts. The maps created based on different social media, Instagram and Flickr, appeared to have similar spatial pattern (r = 0.81, p < 0.01), which meant the preferences of different represented user groups were much the same. One of the main findings that UGS of large size and close to shoreline tended to have more photo counts was verified by the results of regression analysis. Moreover, it was worth noting that some UGS performing excellent in either Flickr or Instagram group, such as Golden Gate Park, Presidio, Fort Mason and Patricianâ€™s Green, could be the best UGS regarding CES provision. However, the categories of CES are diverse and hard to be separated with each other due to its intangibility and subjectivity (Dickinson and Hobbs, 2017). The city-wide UGS big event detection resulted from dynamic mapping was an tentative experiment, which helped understand part of recreation and social value provided by UGS. The capability of UGS holding events including the number of big event days and the scale of events, and the types of events were showed in the results. The Pearson correlation coefficient between big event days (Fig. 3.6) and photo counts (Fig. 3.2) in UGS were moderate with high p-value (r = 0.34, p > 0.1), indicating weak evidence about the significance of the positive association between the two variables. It was partially because that some medium or small size of UGS with average or poor performance in photo counts and photo counts per hectare such as Sigmund Stern Grove and Corona Hts. & Randall Museum held many big events in the summer of 2017. This proves my viewpoint that the individual CES may have very different spatial pattern from that of total CES. Therefore, the study of relationship between site-specific features and CES provision is indispensable to understand the composition of CES at both city and UGS scales.

DISCUSSIONS | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

4.2 Relationships between Site-specific Features and CES The results of photo content analysis indicated the subjects of Flickr and Instagram geotagged photos were also similar, which reconfirmed that the preferences of the two user groups were similar. The content of photos reflected how site-specific features affected CES provision. “Water landscape”, one big photo subject in Flickr and Instagram photos, was related to site-specific features such as waterbody and beautiful sea view in UGS. The hypotheses were verified in the results of regression analysis that “waterbody” ”shore view” were positive and statistically significant for photo counts, and the first one was also positive and statistically significant for photo counts per hectare. “Surroundings of buildings” as another main photo subject for both groups, had something to do with site-specific features including “landmarks”, “cultural and entertainment spots” and “density of surrounding buildings” listed in Table 2.1, and the first two of them were also proved to have positive effects for photo counts while the last one was positive and statistically significant for photo counts per hectare in UGS. The results of regression analysis and photo content analysis corroborated with each other. However, “density of surrounding buildings” and “population density” which were positively associated with photo counts per hectare, were negatively associated with photo counts in UGS. This looked paradoxically but could be true in this case. UGS with high photo counts while low photo counts per hectare such as Presidio, Fort Mason and Golden Gate NRA, had lower population and building density in surrounding neighborhoods, compared to UGS with low photo counts while high photo counts per hectare such as Union Square and Howard Mini Park in downtown San Francisco. “Average height of surrounding buildings” and “percentage of impervious surface” were also negatively and significantly associated with photo counts. These factors related to surrounding built-up environment and demographics indicated that UGS located in less dense area with lower average height of buildings, population and building density in surrounding neighborhoods tended to have more photo counts, though the photo counts per hectare were low because such UGS were usually of big size in San Francisco. Although people took a lot of “vegetation landscape”

photos, neither “percentage of woody vegetation” nor “percentage of grass” were found statistically significant. Some UGS with a high proportion of vegetation got few photos such as Interior Green Belt and Park Presidio Boulevard, and some with sparse vegetation got a great amount of photos such as Embarcadero Plaza and Union Square, suggesting vegetation coverage was not a driven factor comparable to those in Table 3.3, even if people liked “vegetation landscape”. The visual quality of vegetation landscape instead of simply the vegetation coverage could be the potential factor (Frank et al., 2013). For instance, there were massive and dense “vegetation” photos in Golden Gate Park, which is famous for species richness and high aesthetic value in landscape, particularly in Japanese Tea Garden and Conservatory of Flowers. Though vegetation coverage was found insignificant in regression analysis, “percentage of impervious surface” was negative and statistically significant for photo counts. This verified the proposition in Table 2.2 that UGS with less impervious surface could provide more CES. Besides, diversity of land cover, an important index for environmental heterogeneity, was considered to be positively associated with aesthetic value in natural area (de la Fuente de Val et al., 2006; Frank et al., 2013). However, in such a highly urbanized context, it was tested to be insignificant. Accessibility indicators including the “nearest distance to BART station” and “bike parking facilities”, initially supposed to be positively associated with CES provision (Table 2.2), were not statistically significant. Poor public transportation, in this study, was not be a big constraint for taking photos in San Francisco UGS. The “sports facility”, which was considered as the main feature influencing park visitation and physical activities in many cases (Cohen et al., 2010; Kaczynski et al., 2008), was statistically insignificant for CES provision in this study. The findings clarified the driven factors of the spatial pattern of CES provided by UGS in San Francisco, and highlighted the place-specific nature of CES (Dickinson and Hobbs, 2017).

4.3 Caveats and Limitations There are several methodological caveats and limitations in the interpretation. First and foremost, geotagged photos are not a perfect indicator of CES. The intangibility and subjectivity suggested by numerous definitions of CES, especially some underlining the sense of space

GRADUATE THESIS BY DINING LIU | CROWDSOURCING | DISCUSSIONS

and spiritual inspiration, is one of the most commonly cited reason (Dickinson and Hobbs, 2017; Tengberg et al., 2012). The CES quantified by photo density is only a subset, mostly about aesthetic, recreation and social value (Casalegno et al., 2013; Casado-Arzuaga et al., 2013; Yoshimura and Hiura, 2017; van et al., 2016; Zhu and Newsam, 2016). Another reason is that CES refers to multi-disciplines including ecological, social and behavioral studies. The metrics of different CES are not consistent or interchangeable (Dickinson and Hobbs, 2017). In addition, it should be noted that the motivations for people to take photos in UGS vary a lot, hence both positive and negative attributes of the environment could be recorded in the photos (Dorwart et al., 2009). Secondly, the geotagged photo dataset itself can have some quality issues. One big concern is that the represented groups are biased (Tufekei, 2014). It is found that well-educated people with easy access to information technology are more likely to use photo-sharing platforms (Girardin et al., 2008; Oteros-Rozas et al., 2017). Such demographic bias also exists in revealed preference approaches that older people are prone to be survey respondents (Ives et al., 2017). Another important constraint is that users may have biased behavior on social media. Some take a photograph but not geo-tag it, others do not geo-tag it instantly after taking in the site, which may result in spatial and temporal accuracy issue. Tourists and locals can have different behaviors as well (Mukhina et al., 2017). The results of mapping CES that most mini or neighborhood park have few or zero photo confirm that the CES provided by many small size UGS are under-represented because the residents living nearby would not like to take or share photos in such UGS. Moreover, social media privacy policy restricts the access to demographic data about users for protection, which is an obstacle to study the preferences and behaviors of different populations. Thirdly, the timing of geotagged photos taken in the summer of 2017 represented only one season spatial pattern of CES provided by UGS in San Francisco. Neither intra- nor inter-annual changes of CES was considered in this case. The results of dynamic mapping and assessment also assumed that the distribution of daily photo counts for all UGS in San Francisco during the summer of 2017 were approximately symmetric if there was no social event. For event detection, some other methods developed in recent years such as moving windows

(Zhou and Xu, 2016) and T-pattern analysis (Peuquet et al., 2015) can tell more details about spatial-temporal evolution and event types of the detected events with higher accuracy compared with the basic box-plot. As for photo content analysis, there are two main concerns. Photographic orientation is rarely recorded, so photos taken at the same position may have quite different contents, and vice versa. This might cause slight error in the analysis of the relationship between site-specific features and CES provision. For example, some photos taken on borders of the UGS may not focus on the scenery in the UGS but the streetscape including vehicles, people and roads near the UGS. The other limitation is that the image recognition tools including Clarifai and Google Cloud Vision (Google Cloud Vision API, 2017) so far can only tell the elements in the photo content, but the proportions of different elements and other visual attributes, such as the complexity of landscape structure and coherence with the rest of the landscape, are unknown. Such information is useful to understand how landscape heterogeneity could affect visual aesthetic quality (de la Fuente de Val et al., 2006) and diverse human activities in UGS (Oteros-Rozas et al., 2017). The result of regression analysis, especially the low R value for photo counts per hectare (Table 3.3), suggests that the site-specific indicators (Table 2.1) used to explore the relationship with CES provision are not comprehensive. Some other factors describing landscape heterogeneity such as biodiversity, vegetation structure and topographic complexity (McGinlay et al., 2017; Breuste et al., 2013; Dronova, 2017) which are in need of detailed records or fine scale measurements, could affect CES provision. For example, parks characterized by distinct topographical relief such as Twin Peaks Park and Pioneer Park, appeared to get more photo counts with vantage points overlooking the cityscape (Fig. 3.2). Golden Gate Park with an amazing richness of biodiversity due to the variety of habitats had a great number of photos about “vegetation landscape” and “birds” (Fig. 3.11).

CONCLUSIONS & FUTURE RESEARCH | CROWDSOURCING | GRADUATE THESIS BY DINING LIU

CONCLUSIONS & FUTURE RESEARCH

Geotagged photos from social media have increasingly been used for mapping and assessing landscape values and CES that in turn can support environmental planning and landscape design. Landscape planning and design at various scales could benefit from a deeper knowledge on what people portray and share because such uploaded photographs can act as an indicator of what site-specific features and CES matter in different contexts. The analysis of geotagged photos can also be used as a complementary technique of traditional revealed preferences approaches such as interviews and surveys to assess CES. Dynamic mapping is beneficial to detect CES changes and anomalies at different time scales. Detecting the big event in UGS, for example, helps planners know quantitatively about recreation and social value in the field of CES. The inconsistency between the spatial pattern of CES and extreme event days in UGS, as well as various compositions of different subjects of photos in UGS demonstrated the CES composition across all UGS in San Francisco varied a lot due to different site-specific features in UGS. UGS located in less dense area with a larger size, more disaggregated shape, more landmarks, waterbody, cultural and recreation spots, as well as a potential sea view were probably to get more photo counts, while UGS with waterbody, higher building and population density in surrounding neighborhoods could have more photo counts per hectare in San Francisco according to the results of regression analysis. The effects of different site-specific features on CES provision were measured quanti-

GRADUATE THESIS BY DINING LIU | CROWDSOURCING | CONCLUSIONS & FUTURE RESEARCH

tatively and could be compared with each other, which help landscape planners to know the trade-offs across different options of landscape changes. Given the caveats and limitations identified above, some critical questions need to be answered in future research. Firstly, the less tangible category of CES such as sense of place and spiritual values are often the ones that are valued the most but remain inconspicuous in social media. It is in need of understanding about what type of individual CES are under-represented by social media specifically to which groups of people. Secondly, the biased user groups as well as the lack of demographic data about users due to privacy policy of social media results in uncertainty about the social-economic representativeness of geotagged photos, thus reducing the accuracy of the assessment of CES. Two tentative approaches can be used to gain more demographic data in the future: (1) using advanced image recognition tech-

nique to detect faces in photos, predicting demographic information including the number of people, gender, age, race and ethnicity (Clarifai, 2017); (2) analyzing usersâ€™ behavior in social media to tell their characteristics, for example, Mukhina et al. (2017) distinguish visitors from residents using time windows based identification method. Surveys and interviews are considered to provide supplementary demographic data about visitors, however, how to incorporate the new method into such traditional methods for strengthening the both merits is unclear and need more research. Furthermore, it is true that CES are often generated interdependently with other ecosystem services including supporting, provisioning and regulating services. Nevertheless, spatial covariance between different ecosystem services remains poorly understood which is key to increase awareness of a larger group of the non-material benefits and engage people in conservation planning.

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