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Silicon Valley construction project web mapping application

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Silicon Valley construction project web mapping application

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Silicon Valley Construction Project Web Mapping Application

by

Erwin Abidog

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

August 2021

Copyright © 2021 Erwin Abidog
ii
Acknowledgements
I am thankful for my advisor, Doctor Sedano, for providing writing and technical assistance for
this thesis. I am also grateful for my company for providing the inspiration for this project.

iii
Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abbreviations ............................................................................................................................... viii
Abstract .......................................................................................................................................... ix
Chapter 1 Introduction .................................................................................................................... 1
1.1. Project Overview ................................................................................................................1
1.2. Motivation ...........................................................................................................................3
1.3. Thesis Overview .................................................................................................................4
Chapter 2 Related Work.................................................................................................................. 5
2.1. User Interface – User Experience .......................................................................................5
2.2. Open Government Data ......................................................................................................7
2.3. City Planning Department Construction Project Sites ......................................................10
Chapter 3 Methodology ................................................................................................................ 16
3.1. Requirements and Objectives ...........................................................................................16
3.1.1. Functional Requirements .........................................................................................16
3.1.2. Accessibility .............................................................................................................17
3.1.3. Software and Platform .............................................................................................17
3.2. Data ...................................................................................................................................18
3.2.1. Construction Project Data ........................................................................................19
3.2.2. Spatial Data ..............................................................................................................19
3.3. Software ............................................................................................................................19
3.3.1. Microsoft Excel ........................................................................................................20
3.3.2. ArcGIS Pro...............................................................................................................20
iv
3.3.3. Landvision................................................................................................................20
3.3.4. Atom ........................................................................................................................22
3.3.5. Mapbox ....................................................................................................................22
3.4. Data Preparation................................................................................................................22
3.4.1. Spatial Data ..............................................................................................................23
3.4.2. Tabular Construction Project Data...........................................................................24
3.4.3. GeoJSON .................................................................................................................27
3.5. Application Development .................................................................................................29
3.5.1. Mapbox API .............................................................................................................30
3.5.2. Filtering Code ..........................................................................................................32
3.5.3. CSS Filters ...............................................................................................................32
3.5.4. Construction Project Information Popups ................................................................33
3.5.5. Tips and Credits .......................................................................................................34
3.6. User Survey .......................................................................................................................34
Chapter 4 Results .......................................................................................................................... 37
4.1. Main Map View ................................................................................................................37
4.2. Checkbox Selection ..........................................................................................................39
4.3. Information Popup ............................................................................................................41
4.4. Tips and Credits ................................................................................................................42
Chapter 5 Discussion .................................................................................................................... 44
5.1. Challenges .........................................................................................................................44
5.1.1. Programming............................................................................................................44
5.1.2. City Updates.............................................................................................................45
5.1.3. Data Collection ........................................................................................................46
5.2. Future Work ......................................................................................................................46
v
5.2.1. Scaling Up ................................................................................................................47
5.2.2. Automation ..............................................................................................................48
5.2.3. New Program Features .............................................................................................49
References ..................................................................................................................................... 50
Appendix A CSS Examples .......................................................................................................... 55
Appendix B Checkbox Section ..................................................................................................... 57
Appendix C Major Map Functions ............................................................................................... 60
Appendix D Visibility Filter Code ................................................................................................ 63

vi
List of Tables
Table 1. Summary of data ............................................................................................................. 18
Table 2. Mass transit proimity vaxlues ......................................................................................... 24
Table 3. Size values ...................................................................................................................... 29

vii
List of Figures
Figure 1. Study Area ....................................................................................................................... 2
Figure 2. Santa Clara development map. Source: Santa Clara 2020 ............................................ 11
Figure 3. Sunnyvale development map. Source: Sunnyvale 2020................................................ 12
Figure 4. Cupertino project activity map. Source: Cupertino 2020 .............................................. 13
Figure 5. San Francisco development pipeline map. Source: San Francisco 2020 ...................... 14
Figure 6. Landvision Screenshot. Source: Landvision 2021 ........................................................ 21
Figure 7. Data Preparation Flowchart ........................................................................................... 23
Figure 8. GeoJSON ....................................................................................................................... 28
Figure 9. Klass example ................................................................................................................ 29
Figure 10. “Klass” key example ................................................................................................... 33
Figure 11. Survey .......................................................................................................................... 35
Figure 12. Initial map view ........................................................................................................... 38
Figure 13. Multifamily under construction ................................................................................... 39
Figure 14. Mixed development ..................................................................................................... 40
Figure 15. Search example ............................................................................................................ 40
Figure 16. Information popup ....................................................................................................... 41
Figure 17. Mixed property information popup ............................................................................. 42
Figure 18. Credits slide out menu ................................................................................................. 43

viii
Abbreviations
API Application programming interface
BART Bay Area Rapid Transit
CRE Commercial real estate
CSS Cascading style sheets
GeoJSON Geographic JavaScript object notation
GIS Geographic information system
JS JavaScript
JSON Javascript object notation
LLC Limited liability company
LP Limited partnership
OGD Open government data
OSM OpenStreetMap
R&D Research and development
RHNA Regional Housing Needs Allocation
SFR Single-family residences
SQFT Square feet
SQL Structured Query Language
UI User interface
UX User experience
VTA Valley Transit Authority

ix
Abstract
The Santa Clara County economy has fueled demand for commercial real estate (CRE)
developments. CRE brokers capitalize on this demand by attracting tenants to newly constructed
buildings or by helping property owners sell to developers for future redevelopment
opportunities. City planning departments disclose construction project information within their
boundaries. Each city has its own property methodology and data release schedule with few
sources recording these sources regionally. The main objective is to create a CRE construction
web application that tracks construction projects within the major cities of Santa Clara County.
This helps save time by standardizing the project data and aggregating all the information into
one program. The progress of the CRE development is tracked throughout the pipeline as it goes
through the following statuses: pending approval, approved, and under construction. The
database also focuses on the following property types: office, hotel, multifamily, retail, and
industrial. This construction data is organized and presented to brokers via a web GIS application
to enable spatial searches in an easy-to-use interface.

1

Chapter 1 Introduction
Santa Clara County has enjoyed a strong economy that has explosively grown over this decade,
with the labor force increasing from 957,392 in 2010 to 1,035,396 in 2020 (BLS 2020).
Economic and demographic pressures have increased demand for commercial real estate (CRE).
Developers and investors scour the county, proposing and constructing new developments to
satisfy this demand. In this environment, brokers facilitate new relationships between developers
and investors that supply new CRE properties and tenants looking to occupy them. Brokers also
guide developers towards land parcels for redevelopment opportunities. The thesis objective is
developing a CRE construction web application to help brokers track construction projects
within this region.
The chapter introduces a project overview, motivation, and methodology for building the
construction project web application. Section 1.1 provides an overview of the CRE construction
web application, and Section 1.2 describes the motivation behind its creation.
1.1. Project Overview
This thesis project aims to create a web application that helps brokers track CRE
construction projects in Santa Clara County. This area is also known as Silicon Valley and serves
as the main sales territory for the author’s current job at a CRE brokerage. The web application
follows the construction project through all steps of the construction pipeline. The initial step is
when the development application is proposed. Next, the city planning department must approve
the construction project. Afterwards, the developers start construction on the project. Lastly, the
construction project is complete.

2

Figure 1. Study Area
The following property types are tracked through this pipeline: office, industrial, research
and development (R&D), retail, hotel, and multifamily. Offices range from multiple high-rise
complexes to short 1-story buildings. Industrial properties consist of manufacturing buildings
and distribution warehouses. R&D property types are in-between type properties that combine
the qualities of 1 to 2-story office buildings with industrial property types (Datastar 2020). The
property areas may be 50% office and 50% warehouse. Retail properties include mega-malls,
grocery centers, or the first-floor restaurants of new apartment or office buildings. Hotels within
these counties tend to be business or extended stay hotels geared towards workers attending
conferences or branch offices. Multifamily properties include condominiums, townhomes, and
apartment complexes. Mixed developments combine two or more property types in one
3

development. Common examples include mixed developments that combine retail space on the
first floor with multifamily or office buildings.
1.2. Motivation
The motivation to create a CRE construction web application stems from working for a
CRE brokerage as a research analyst. Brokers request to track leases, sales, existing buildings,
and construction projects. This thesis project focuses on satisfying the need to track construction
projects within the Silicon Valley region. Brokers desire to know what construction projects are
in their sales territories and to track their progression along the construction pipeline. These
projects attract tenants demanding top-of-the-line, Class A properties. Proximity to these new
developments may also enhance the desirability of existing properties. For example, an obsolete
property zoned for multifamily development may look more desirable if multifamily or other
construction projects are nearby (Hooper and Rowe 2020). The property can rise in value if it is
redeveloped into one better suited for the economy or neighborhood.
Obtaining this information can be quite cumbersome. In the United States, zoning is left
to local cities with little state or country regulation (Kim 2020). City planning departments
define acceptable and planned land usage spatially through these zoning laws (Brown and Glanz
2018). Each city has their own planning department with a construction project database, rules,
and varying levels of transparency. The city of Newark does not disclose CRE construction
projects on their city planning websites (Newark 2020). However, the cities of Mountain View
and Sunnyvale are very transparent in reporting CRE construction projects with project lists and
maps available to search (Mountain View 2020; Sunnyvale 2020). Data releases may be released
weekly, monthly, quarterly, or intermittently. COVID-19 worker safeguard measures have also
delayed CRE construction project updates. Cities may have their own interpretation of R&D and
4

multifamily property types. New industrial buildings that cater to high tech manufacturing may
be labeled as R&D properties. Office buildings that also offer biotech labs may be labeled as
R&D properties. City planning departments may differ in multifamily property classification.
Single-family residences (SFR) may be recorded with condominiums, townhomes, and other
multifamily properties. CRE brokers have little interest in SFR’s, so this property type needs to
be removed. Cities may also only focus on developments above a certain size (San Jose 2020).
This data needs to be standardized, collated, and presented to brokers in an easy to understand
medium.
Another goal is creating a regional CRE construction project web application. Cities only
focus on construction projects within their borders. Plus, these jurisdictions have their own
property type definitions, update schedules, and disclosure standards. CRE brokers may save
time by using this CRE construction project web application instead of searching through
multiple city planning departments. Other benefits are in tracking developers with multiple
construction projects over different counties within Silicon Valley.
1.3. Thesis Overview
This thesis outlines the background, methodology, and decisions made to develop the CRE
construction web application. Chapter 2 provides background on related work elaborating on
user interfaces and experiences, open government data, and comparable CRE construction web
applications. Chapter 3 covers the project methodology. This chapter outlines the necessary
requirements and objectives, data sources, necessary software, data preparation, application
workflow, and broker survey feedback. Chapter 4 walks through the application results. Chapter
5 includes a discussion on the challenges of the thesis project and ways to improve and scale up
the application in future versions.
5

Chapter 2 Related Work
This chapter describes the related work for the construction project web application. Section 2.1
walks through how a simplified user interface (UI) can improve the user experience (UX). Next,
Section 2.2 provides an evaluation on using open government data. Lastly, Section 2.3 goes over
examples of city planning department construction project sites.
2.1. User Interface – User Experience
When developing an application, a well-implemented UI leads to a great UX. Creating a
UX entails researching and defining the user demands, developing a UI so that the user easily
fulfills the requirements, and evaluating if the program meets the user’s needs. The UI describes
how the user interacts with the application. The user must fiddle with the various buttons, icons,
tool placements, and other layouts to obtain the desired information. Usability describes how
easy UIs are to navigate. To have great usability, the UI must be easy to learn, perform tasks
efficiently, achieve tasks with minimal errors, and be pleasant to use. After assessing the
program effectiveness, future versions improve the UI and create a better UX by developing
easier methods to accomplish the user objectives.
The main goal of the construction project web application is to have a central source for
brokers to efficiently access construction project information in Silicon Valley. This application
is akin to having a visual spatial display. These displays can be categorized into iconic,
relational, and hybrid displays that combine the iconic and relational displays (Hegarty 2011).
Iconic displays represent space within the application as space in the world. Properties displayed
like shape and color are also visible in real life. Iconic displays also distort reality by showing
three dimensional buildings or structures such as roads as two-dimensional polygons. Relational
displays have abstract, non-visual qualities like heat or importance represented visually as colors
6

or icons (Hagerty 2011). Hybrid displays combine qualities of both iconic and relational
displays. The web application will use hybrid displays to represent the construction projects.
Icons representing the physical location of the construction projects will be plotted on a basemap
in an iconic display. Colors representing non-visual qualities such as property type and
construction status are shown in a relational display.
Having an easy-to-use interface will help achieve this goal and improve the overall user
experience. Simplicity should be the goal, since more complicated UI causes a less efficient UX
(Horbinski et al. 2020). Too much realism may clutter the UI and make task-relevant information
harder to understand (Smallman and Cook 2010). Adding interactivity also helps add simplicity
to the UI. Interactivity means that the map reader is not passive on how the information is
represented. The user is empowered to change how the information is viewed to best represent
his or her goal (Roth et al. 2017). In the construction web application, the data is filtered by
property type, area, city, construction status, and proximity to mass transit. The user can select
the ideal combination of these variables to create an optimal view of construction data.
Cybulski and Horbinski did an experiment that compared the user experiences with the
Google Maps and OpenStreetMap (OSM) user interfaces. The authors tracked the fixations and
saccadic amplitudes of the users (Horbinski et al. 2020). Fixations are areas where the eye
lingers and correlate with more complex visualizations. Saccadic angles record eye movements
(Horbinski et al. 2020). To make the experience easier to navigate, the program should strive for
lower fixations and higher saccadic amplitudes.
Another way to improve the user interface is to present the information in a familiar
format for brokers like Google Maps. In a different study comparing Google Maps and OSM,
participants were tasked with evaluating button and module placement (Horbinski et al. 2020).
7

The users recognized the Google Maps geolocation button, OSM change layer icon, and the
Search icon. The participants also preferred having the search and geolocation icons in the upper
left of the application. Under the proximity compatibility principle, displays relevant to common
tasks should be rendered near each other to help comprehension (Wickens and Carswell 1995).
For the construction project application, the search and geolocation icons can also be placed in
the upper left. A satellite basemap with street labels can also be used to have landmarks and
major streets help pinpoint the locations of the construction projects.
2.2. Open Government Data
The construction project web application relies heavily on open government data (OGD)
as a source. OGD consists of public sector information or open public data (Saxena 2018). This
is an extremely broad definition that includes different genres such as city officer salaries,
demographic information, police data, or real estate data. This application focuses on OGD
related to construction projects and land parcels. City planning departments provide almost all
the data on construction projects and land parcels. Santa Clara County provides supplemental
information for land parcels. Each department provides the data in different formats and levels of
transparency. These datasets can be categorized based on completeness, formats, and variety
(Wilson and Cong 2020). Complete datasets are evaluated if they are categorized, tagged, or
have a description. Datasets are also judged if they follow open data formats such as CKAN,
Socrata, or ArcGIS Open Data. Variety encompasses city departments such as administration,
public safety, city services, finance, and planning.
OGD can be further categorized into three platform types: cognitivist, connectionist, and
autopoietic (Danneels et al. 2017). The cognitivist platform equates knowledge with information
and that no further interpretation is needed (Danneels et al. 2017). The government worker
8

introducing the data transformation may introduce an unintentional bias that may adversely
affect usage from other stakeholders. Under this approach, raw and unmanipulated data is the
ideal format (Denis and Goeta 2017). The connectionist platform states that data formats and
processing are not universal and vary locally (Danneels et al. 2017). Government agencies may
have different levels of disclosure. Sunnyvale may disclose a parcel owner when releasing
construction project information (Sunnyvale 2020). However, Santa Clara County does not
disclose parcel owners due to privacy concerns (Santa Clara County 2020). The autopoietic
platform states that knowledge cannot be directly conveyed from individual to another and must
be interpreted (Danneels et al. 2017). There are many different OGD stakeholders and uses may
vary widely within the same dataset (Dawes et al. 2016).
OGD has different stakeholders providing their own perspectives. Gonzalez-Zapata and
Heeks describe the four main perspectives as bureaucratic, political, technological, and economic
(Gonzalez-Zapata and Heeks 2015). The bureaucratic perspective wants OGD to improve public
services through a more efficient and effective data management program. The technological
perspective desires an improved data infrastructure. This helps tie in with the bureaucratic
perspective by providing better data management. The political perspective wants OGD for
better transparency, accountability, and participation. The economic perspective wants OGD for
more access to new products, services, revenue, profits, and jobs. The construction project web
application leans more towards the political and economic perspectives. Increased transparency
helps provide more construction project information. This helps brokers potentially generate
more revenue by guiding clients towards new developments.
The four main perspectives of OGD have their goals that benefit the overall community,
but also face major challenges. Governments face staff and funding constraints in collecting,
9

maintaining, and sharing data (Wilson and Cong 2021). Effective use of OGD also relies on
internet access, user technology skills, usable data formats, user knowledge, community
resources, and formal government structures (Young and Yan 2017). While having a very open
and granular OGD is ideal, privacy concerns may cause cities to not fully disclose landowners or
construction project applicants (Zhu and Freeman 2019).
Collating the data and presenting it in a construction project application is similar to how
civic hackers use OGD for products and social good applications (Young and Yan 2017). OGD
can be evaluated on traceability, currency, expiration, completeness, compliance,
understandability, and accuracy (Vetro et al. 2016). Traceability and understandability rely on
the presence of metadata. Few cities give descriptive metadata, so the construction project data
scores poorly with these metrics. Expiration tracks the ratio between the delay in dataset updates
after the expiration of previous versions. Since each city planning department has its own data
standards, completeness varies. Missing fields are supplemented through trade journals and title
feed programs. Compliance tracks the percentage of standardized columns in a dataset. Although
each city planning department has its own data standards, information about the project name,
address, property types, building area, multifamily units, and hotel rooms are standardized for
every city.
To compile a functional construction project dataset, the OGD needs to be transformed.
Datasets from various government agencies are cleaned into a workable format (Denis and Goeta
2017). To clean the information, missing and abnormal information are corrected. Data from
each government agency is then standardized. The different errors and standards may not be
noticeable within the original datasets, but stand out if uncorrected in the combined dataset
(Denis and Goeta 2017).
10

OGD encompasses a broad range of subjects like census data, neighborhood meetings,
and city finances. The government workers assigned to compile and report the data tend to only
report in their own local environment. For example, San Jose city officials only tend to disclose
OGD pertaining to their city. It is impossible for these officials to cater to all the possible users
of this data. To cobble together the necessary data for this project, the information must be
gathered from many different departments. The city planning departments have the construction
data. Santa Clara County has the parcel and ownership data. The California Secretary of State
has the business data that can help verify property ownership. These data sources also must be
standardized and doublechecked to create an accurate construction project dataset.
2.3. City Planning Department Construction Project Sites
Every city in Silicon Valley differs in how construction projects are tracked and reported.
Cities disclose construction projects via OGD through their planning departments. Some cities
do not disclose construction project information like Newark, CA. Other cities like Fremont and
Sunnyvale are nicely transparent by providing project information with spreadsheets, summaries,
web maps, and printed maps. The construction project web application aggregates the
development information into one site and standardizes the information based on broker and
industry standards.
Fremont and Sunnyvale report on construction projects through spreadsheets and maps
(Fremont 2020; Sunnyvale 2020). Spreadsheets are great to track information about building
type, area, developers, and construction status. However, this information is not expressed
spatially. It is difficult to see how these developments affect local neighborhoods or other cities.
Maps convey the construction project information spatially. Building type, area, and
construction status can be easily determined immediately. Santa Clara presents its developments
11

in an Esri story map as shown in Figure 2. Each development has its land area color coded by
construction status. Pictures of every development are along the bottom. Clicking on the land
area zooms towards the parcel and the corresponding picture appears. The picture also has text
descriptions with links to more information about construction projects (Santa Clara 2020).

Figure 2. Santa Clara development map. Source: Santa Clara 2020
Sunnyvale also has a development project map as shown in Figure 3 that is color coded
by construction status. However, the land areas are not shown. This makes it hard to determine
the scope of a construction project. It is also difficult to see the property type without clicking on
each dot (Sunnyvale 2020).
12

Figure 3. Sunnyvale development map. Source: Sunnyvale 2020
The Cupertino Project Activity Map as shown in Figure 4 tracks development projects in
various stages of construction. The projects are presented upon a 3D Map where users can select
properties by the construction statuses: applied, approved, under construction, and completed.
Clicking the development project by the dropdown or on the map provides a popup with a short
summary the project. There is also a link that provides more information like the developer, site
plans, and a thorough description of the project. The basemap can also be changed to help give
users a better sense of location. Construction projects are also color coded based on status
(Cupertino 2020). The city construction project map provides good ideas like having the land
13

area be represented on a map and having the developments color coded by construction status. 3-
D buildings can be extruded to see how the construction projects look in relation to the rest of the
city. It may be troublesome to create 3-D maps for every development in the study area.
However, each construction project can be represented by an icon that describes the property
type. This icon can be color coded by construction type.

Figure 4. Cupertino project activity map. Source: Cupertino 2020
The San Francisco Development Pipeline Map as shown in Figure 5 has search filters that
the CRE construction project web application can implement. Users can filter by project size via
non-residential square feet (sqft), multifamily units, and affordable multifamily units.
Neighborhoods and circular address buffer searches may also be implemented. The map grants
an option to filter by project status. The user can filter for all projects, under construction,
building permit approved, planning entitled, and proposed (San Francisco 2020).
14

Figure 5. San Francisco development pipeline map. Source: San Francisco 2020
The planning entitled status is unique to San Francisco. It means that the project is
approved by the planning department, but has not been granted construction rights. This stems
from measures such as Proposition M that limits construction approvals of office projects that are
above 25,000 sqft citywide to 950,000 sqft per year. 75,000 sqft of this limit is reserved for
construction projects between 25,000-50,000 sqft. The remainder 875,000 sqft is allocated for
large cap projects above 50,000 sqft. Unused allocations carry over to the next year (SPUR
2020). On March 2020, voters passed Proposition E that tied construction approval limits for
large cap projects to Regional Housing Needs Allocation (RHNA) affordable housing goals (San
Francisco Business Times 2020). For the CRE construction project web application, the planning
15

entitled status is equivalent to the proposed status. The building permit approved status counts as
approved for the thesis project.
Summarized totals based on the search filters are another feature to implement in the
CRE construction web application. If the broker just wants a quick glance of construction project
totals per property type or status, then this feature is very convenient. Options to export the data
as a CSV or PDF file is also useful for future data analysis. Dots representing construction
projects can be clicked for further information that gives multifamily units, office square footage,
and links to planning applications (San Francisco 2020). The user experience can be improved
by having different icons for each property type.
The construction project sites from these cities offer examples in combining construction
project data in different user-interfaces. Santa Clara, Sunnyvale, and Cupertino offer construction
project information where the construction status can be determined at a glance. San Francisco
gives options to filter by construction status, property area, multifamily units, neighborhood, and
proximity to a user-defined address. More information can be gained from clicking on each
project icons from all these websites. San Francisco is the only site that allows project
characteristic filters, but the dots only show project locations. The thesis construction project
hopes to combine the good user-interface examples from these sites like filters to select the
desired view and color to easily determine property type and construction status. The project
scope is also expanded to include the major cities of Silicon Valley so that construction projects
may be presented regionally.
16

Chapter 3 Methodology
This chapter describes the methodology and requirements necessary to construct the CRE
construction project web application. Section 3.1 outlines the requirements and objectives
necessary to complete the web application. Next, Section 3.2 describes the data sources. Section
3.3 presents the software used to create the application. Section 3.4 depicts the data preparation
necessary to make it accessible in the web application. Section 3.5 goes over the application
workflow. Section 3.6 gives a description of survey questions and how broker input shaped the
application.
3.1. Requirements and Objectives
The project objective is to create a web application for CRE brokers to track construction
projects within the Bay Area. Spatial comprehension is increased when brokers can see the
developments plotted on a map. The developments can be filtered by property type, size, city,
construction status, and proximity to mass transit. The following requirements are necessary to
meet these objectives.
3.1.1. Functional Requirements
Functional requirements for the web application are the specific tasks that users must be
able to complete. These tasks were chosen to help users fulfill the business goals of the author’s
brokerage. The overarching goal of the web application is for users to keep track of construction
projects within Silicon Valley. To do so, the web mapping application was designed to serve the
following functional requirements:
• Allow users to view a map of properties under construction in the Silicon Valley area, with clear
icons that represent property type and construction status
17

• Allow users to search for properties by inputting addresses into a search bar
• Allow users to zoom and pan around the map
• Allow users to click on property icon and obtain information on:
• address,
• property name,
• property type,
• owner,
• construction status, and
• property size.
• Allow users to filter the map by:
• property type,
• property size,
• city in which the property is located,
• construction status, and
• proximity to mass transit.
3.1.2. Accessibility
The application must be shareable throughout the organization over the internet. Brokers
are going to use the construction project web application on a desktop. Mobile formats do not
have screen real estate to adequately show the development filters and the map. Next, brokers
only need an internet connection to use the device. No additional applications are necessary.
Lastly, brokers should be able to use the application with minimal training.
3.1.3. Software and Platform
This application is the first draft of a web application that increases in scope and
functionality over time. The software used to create the application must be able to show the
18

construction projects on a map to fulfill the spatial requirements. The software should also
ideally be available and easily accessible for free. Software with a generous freemium tier meets
these requirements. The chosen software should also support data visualizations and be
accessible enough for future developers to learn the features quickly.
3.2. Data
The CRE construction project application requires spatial and construction project data.
The main spatial datasets consist of parcel shapefiles and point locations of the construction
project data. The construction project data includes the project name and details. The spatial and
construction project components must be joined to present the information spatially and via
popup descriptions. The following sections describe each component more thoroughly. Table 1
summarizes each dataset.
Table 1. Summary of data
DATA SET
SPATIAL
RESOLUTION
TEMPORAL
RESOLUTION
SOURCE
DATA
FORMAT
Construction
Projects
N/A
Projects with
Construction
Statuses: Proposed,
Approved, Under
Construction from
September 2020 –
March 2021
City Planning Departments
within Santa Clara County
Text
Parcel
Ownerships
N/A
Active Companies
from September
2020 – March 2021
Planning Departments,
Landvision, California
Secretary of State
Text
Land Parcels Parcels
September 2020 –
March 2021
Santa Clara County Polygon
CalTrain
Stations
CalTrain Stations
September 2020 –
March 2021
CalTrain Point
BART
Stations
BART Stations
September 2020 –
March 2021
BART Point
VTA Light
Rail Stations
VTA Light Rail
Stations
September 2020 –
March 2021
VTA Point

19

3.2.1. Construction Project Data
The construction project data has been collected from multiple city planning departments.
Each city has its own database and discloses the construction project data in different formats.
Some cities do not disclose construction project data, while others report the data in ArcGIS
online maps, PDF, CSV, or Excel formats. This data is then manually gathered to obtain the
construction project name, owner, property type, property area, multifamily units, hotel rooms,
assessor parcel number (APN), address, and construction status.
3.2.2. Spatial Data
Construction projects are built on land parcels. The spatial data for these parcels are
collected as shapefiles from Santa Clara County. Some construction projects may not have an
address to geocode and provide accurate coordinates. By matching the construction project APN
to the Santa Clara County parcel shapefiles, accurate coordinates for the development can be
obtained.
Spatial data is also collected for mass transit stations from Bay Area Rapid Transit
(BART), CalTrain, and Valley Transit Authority (VTA) Light Rail. This point data is necessary
for proximity to mass transit searches.
3.3. Software
This section describes the software necessary to create the construction project
application. Mapbox is the main application programming interface (API) used for this web
application. Microsoft Excel and ArcGIS Pro are used to organize and process the data into a
geographic JavaScript object notation (GeoJSON) format usable by Mapbox. Landvision is a
proprietary web application used to confirm parcel and ownership information. The data is then
organized with Microsoft Excel or ArcGIS Pro. Atom is a text editor used to program the web
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application with JavaScript. Section 3.3.1 provides a quick summary of Microsoft Excel, and
Section 3.3.2 does the same with ArcGIS Pro. Section 3.3.3 lays out the benefits of using
Landvision. Section 3.3.4 gives a short description of Atom. Section 3.3.5 elaborates about the
specific Mapbox features used for this project.
3.3.1. Microsoft Excel
Microsoft Excel is a spreadsheet software program created by Microsoft. While there are
free options available like Google Sheets, excel is installed on my work and personal computers.
Spreadsheet functions used to convert spreadsheets into a CSV format can be done with either
open source spreadsheet programs like Google sheets or proprietary ones like Microsoft Excel.
3.3.2. ArcGIS Pro
ArcGIS Pro is a proprietary desktop GIS application from ESRI. This application is
provided for free through the USC program. While free GIS applications such as QGIS can
provide the necessary project tasks, the author is more familiar using ArcGIS Pro. Helpful
tutorials also help explain the spatial programming steps for the construction application.
3.3.3. Landvision
Landvision is a proprietary spatial application from LightBox that provides real estate,
sales transaction data, and government information overlaid on a web map (Lightbox 2021). This
program is given to all employees at my real estate brokerage and is useful to confirm current
ownership and parcel data. Figure 6. provides a screenshot of the Landvision application.
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Figure 6. Landvision Screenshot. Source: Landvision 2021
Ownership needs to be validated for three reasons. Santa Clara County gives parcel
information for free, but ownership is hidden due to privacy concerns. City planning departments
may disclose the initial developer or owner that submitted the project application. Another
developer or owner may purchase the development as it gets built. City planning departments
may also disclose just the subsidiary company tied to the current parcel. Landvision may provide
more information to uncover the actual developer that owns the subsidiary company.
22

Parcel information also needs to be validated. As the construction project progresses
through the construction pipeline, parcels tied to the construction project may be combined into
one larger parcel or split into many parcels. These parcel changes warrant new addresses. During
the data capture period, the construction project may have recorded the old parcel or one of the
newer ones. Landvision helps verify that all parcels tied to a construction project remain tracked.
3.3.4. Atom
Atom is a free, open source text editor from Microsoft that can edit code in various
programming and presentation oriented languages. This editor has plugins and customizable UIs
that can make web development more user friendly.
3.3.5. Mapbox
Mapbox provides mapping and location data services through their own proprietary tools
like Mapbox Studio and Javascript APIs such as Mapbox GL JS. Mapbox helps people design
mapping tools that can be shown over browsers on desktop computers, laptops, or phones. The
free tier is generous with up to 50,000 map loads per month (Mapbox 2020). Mapbox also has a
temporary geocoding API that allows 100,000 single search queries for places, address, or points
of interest per month (Mapbox 2020). The API, shapefiles, and tile sets may be manipulated via
JavaScript or through Mapbox Studio. Tutorials are also available to help guide developers on
how to use the API.
3.4. Data Preparation
This section recounts the steps to acquire the data and convert it into a format usable for
the web application. The main categories of data are spatial data representing the construction
projects, spatial data representing mass transit stations, and tabular data describing the
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construction projects. Section 3.4.1 describes how the spatial data is obtained and manipulated.
Section 3.4.2 recounts how the tabular data is chosen, collected, and verified. Section 3.4.3 goes
over how the data is formatted into a GeoJSON to present in a web application. Figure 7 shows a
flowchart providing an overview of the data preparation process.

Figure 7. Data Preparation Flowchart
3.4.1. Spatial Data
Land parcel spatial data is obtained from Santa Clara County. This data is stored as
polygon shapefiles. Each of the parcel polygons have their own APNs. These are matched with
the construction project APNs via a process described in more detail in Section 3.4.2. The
matched parcels are then converted into point data. If a construction project contains multiple
parcels, then they are merged into one polygon and converted into a representative point. These
points serve as the map marker locations for the construction projects.
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Mass transit station spatial data are obtained as point shapefiles from the major mass
transit options such as BART, Caltrain, and VTA Light Rail. Bus stops are ignored since almost
all construction projects are within a half mile of a bus stop. The mass transit station locations
are imported into ArcGIS Pro. Quarter-mile and half-mile buffer zones are applied to each
station location using the Buffer tool. The quarter-mile distance was picked since this was close
to the 500 meter (0.31 miles) ideal distance that people were willing to walk to stations
(Zacharias and Zhao 2018). The half-mile distance was chosen since this is the mean distance
that people walked to stations in the U.S. (Agrawal, et al. 2008). These buffer zones are used to
assign proximity values shown in Table 2 to construction projects.
Table 2. Mass transit proximity values

BART Caltrain VTA Light Rail
Half Mile halfmbart halfmct halfmlr
Quarter Mile quartermbart quartermct quartermlr

3.4.2. Tabular Construction Project Data
Construction project data are obtained from city planning websites. These projects are on
plots of land that contain one or more parcels. Each parcel is given an assessor parcel number
(APN). The spatial link occurs by tying the APN provided by the city planning websites to the
county shapefiles.
This APN needs to be standardized and verified. The APN may have dashes, spaces, or
just expressed as a number. APNs are standardized by making them numerical with no dashes or
spaces. APNs may also change over time. Cities may split parcels or reassign APNs through the
life of the construction project (San Jose 2020). The construction project application only keeps
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the most recent set of APNs. The correct construction project APNs are cross checked with
addresses obtained by construction project information and title websites like Landvision.
Non-spatial data include the construction project attributes like property type, developer,
multifamily units, hotel rooms, building area, and land area. The project applicant is represented
as a legal entity through a limited liability company (LLC) or limited partnership (LP). Multiple
LLCs or LPs may rollup to one developer. The next data collection step is finding the true
developer or landowner behind the LLC or LP. The California Secretary of State site has a
corporation search program that gives the managing members behind these legal entities. The
managing members tend to be executives of the developer or landowner companies. Finding
these connections helps ensure that all developer activity is tracked accurately within the study
area.
CRE construction projects are chosen based on broker interests. The property types are
offices, industrial, retail, hotel, and multifamily. New office, industrial, and retail developments
above 5,000 sqft are chosen (Datastar 2020). These criteria are based on quarterly report
analytical guidelines for the author’s brokerage. Offices range from multiple high-rise complexes
to short 1-story buildings. Industrial properties are manufacturing sheds and distribution
warehouses. Retail properties may include mega-malls, grocery centers, or the first-floor
restaurants of new apartment or office buildings. Purely retail developments are currently rare, so
most retail space tracked are part of a larger mixed-use project that includes offices, hotels, or
multifamily properties.
Cities disclose hotels and multifamily projects differently. Building areas for these
property types are usually not disclosed. Hotels are described with the number of rooms, and
multifamily properties are expressed by the number of units. The hotels in the area tend to be
26

business or extended stay hotels where workers can stay for conferences or when working in
company branches. Multifamily properties include condominiums, townhomes, and apartment
complexes with five or more units (HUD 2020).
These construction projects are tracked through all steps of the construction pipeline. The
initial step is when the development application is proposed. Next, the project must be approved.
Afterwards, the project is under construction. Lastly, the construction project is complete.
Construction projects that do not get approved are removed from the application. These
construction statuses serve as the project timeline steps since the time to completion may vary
based on project scope, finances, or political concerns. Each step within this construction
timeline may take months or even years to complete.
Throughout these steps, the developers and development scope may change. Developers
or investors may sell or buy partial interests or the whole construction project. Neighborhood
feedback may warrant project changes in the construction development. Construction project
building sizes, multifamily units, or hotel rooms may be altered to satisfy traffic concerns or
community feedback. Parcels tied to the development may also change. Multiple small parcels
may combine into one parcel for a large development. The Miro multifamily construction project
in downtown San Jose combined nine parcels into one (San Jose 2017). The number of
apartment units also increased from 610 to 630 for this development (San Jose 2020).
Conversely, one large parcel may be split into multiple small parcels for condominiums or
rowhouses. The Nuevo in Santa Clara is a 988 unit multifamily development. The condominium
and townhome portions have been split into individual parcels to sell to the public (Santa Clara
2020). To simplify data collection, only the most current developers and development
characteristics are tracked.
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Stevens Creek Promenade in San Jose, CA is one example where the developers and
development scope changed. Originally proposed by Fortbay in 2016, the development originally
was planned to consist of 233,000 sqft of office, 10,000 sqft of retail, and up to 582 multifamily
units (San Jose 2020). This development was approved in 2019 by the San Jose Planning
Commission (Hall 2019). Miramar Capital purchased Stevens Creek Promenade from Fortbay in
2020 (Avalos 2020). The new owners kept the name but changed the property into a 637
multifamily unit, 250 room hotel, and 6,200 sqft retail development (Niksa 2020; San Jose
2021). Due to the major construction project changes, Steven Creek Promenade changed its
construction status from an approved development to one pending approval (San Jose 2021). The
application only shows the most current version of the development. Past project characteristics
are noted in the project information popup comments.
3.4.3. GeoJSON
The construction project data is stored in the web page in a GeoJSON format. This format
stores spatial and tabular features based on the JavaScript Object Notation Format. Figure 8
shows an example of a GeoJSON representing a construction project. The GeoJSON contains
fields for the developer or owner, project name, property type, APN, building area, construction
status, hotel rooms, multifamily units, and proximity to mass transit. These construction project
property characteristics and mass transit data are taken from a period from September 2020 to
March 2021. The web application pulls data from the GeoJSON file to filter, present, and
provide information for the construction projects.
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Figure 8. GeoJSON
To create the GeoJSON, the construction project data is collated in Excel. Columns are
created for the project name, construction status, owner, APN, address, city, and property area.
More columns are added to accommodate the mass transit proximity values. Columns are also
added to organize construction projects based on size for each property type. The office, retail,
and industrial construction projects are divided into thirds by size. Multifamily projects are
divided into thirds by units. Hotel projects are divided into thirds by rooms. Table 3 shows
examples of the size values for the property types. This CSV file is imported into ArcGIS and
geocoded into points using the Geocode File tool. Construction projects within the quarter-mile
and half-mile buffers are assigned proximity values as shown in Table 2. The construction
project points are exported as a GeoJSON using the Features to JSON tool.
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Table 3. Size values

Lower Third Middle Third Upper Third
Office off1 off2 off3
Multifamily m1 m2 m3
Hotel h1 h2 h3
Retail r1 r2 r3
Industrial i1 i2 i3

This GeoJSON file is then converted back to a CSV file using the website,
convertcsv.com. This CSV is further manipulated in Excel to define the nested klass key within
the properties key. The web application relies on the klass values to select and filter construction
projects. The klass values consist of a string containing information about the city, property type,
construction status, size value, and proximity value. Figure 9 gives an example of a klass value.
Each value within the string is separated by a space.

Figure 9. Klass example
The filtering process and styling are elaborated further in Section 3.5.2. Once the klass
key is defined, the columns for the size and proximity values are not needed anymore and
removed. Afterwards, the CSV file is converted back into a GeoJSON.
3.5. Application Development
This section recounts the major code sections of the construction project web application.
JavaScript is a programming language used to make web pages dynamic. Filtering construction
project views based on characteristics like property type, construction status, city, and proximity
to mass transit is not possible without this programming language. JavaScript allows the use of
functions, which consist of code snippets that can perform repeated tasks and execute complex
actions. JavaScript can be further enhanced with application programming interfaces (APIs).
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Section 3.5.1 describes the Mapbox API. Next, section 3.5.2 goes over the filtering code. Section
3.5.3 goes over the cascading style sheets (CSS) filters. Section 3.5.4 goes over the construction
project information popups. Lastly, Section 3.5.5 goes over the tips and credits sections.
3.5.1. Mapbox API
Mapbox is a third party API that enables mapping functions in JavaScript. This code is
not built into the browser and must be recalled from the Mapbox servers using an access token.
To create the web application, Mapbox is used to load the basemap, add the construction projects
as a data layer, and define map markers from this data layer for further manipulation with the
JavaScript functions. Appendix C contains screenshots of the major Mapbox functions used for
this web application.
The map is created by using a function that defines a container id, “map”, and setting
other options like the style, center point, and zoom level (Mapbox 2020). Maps are composed of
tilesets, which are comprised of vector or raster tiles. Raster tilesets are a collection of images at
different zoom levels that make a map. Vector tilesets contain geographic, geometric, and
metadata. These tilesets do not inherently have style properties (Mapbox 2020). The raster and
vector tilesets can be manipulated through style documents, formatted in JSON, that defines how
the map should be drawn. Colors, labels, and other features can be manipulated through custom
map styles. Draw order can also be manipulated to highlight information. The preset map style
“satellite-streets-v9” is chosen so that brokers can use freeways, major streets, and landmarks to
increase the spatial awareness of the construction projects and neighborhoods. Feature labels
help confirm the construction project location. The basemap center point and zoom level are set
to have all the construction projects appear when the web application loads. The basemap
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combines the raster tileset containing satellite imagery with the vector tileset containing the
labels, street, and highway data.
User interface elements are also added to the map and serve as a way for the user to
manipulate or observe the uploaded construction data. This application uses the following user
interface elements: map marker, popup, navigation controls, and geocoder. These elements are
all added with separate functions upon map load.
The geocoder and navigation controls are added to the basemap to improve the user
experience and ease the navigation of the web application. The positions of these controls within
the map can be defined. The geocoder is set to the top left map corner, and the navigation control
is to the top right map corner. The navigation control has a compass that points towards true
north plus two buttons that lets the user zoom in and out. The compass and zoom buttons can
have their visibility toggled to be hidden.
The geocoder uses the Mapbox temporary geocoding API to move the map view to the
user defined location. A circle is created and added as a point layer. When the user inputs a
location into the geocoder function, the map view moves towards the user defined location. The
circle is placed on the center of this map view. A bounding box is also defined for the geocoder
that completely encloses Santa Clara County.
The map marker and popup user interface elements are based on the construction project
data. To create the data layer, the construction projects are saved as a GeoJSON and defined as a
variable, “stores”, in the web application. Each construction project is represented as a point
feature within the GeoJSON. “Stores” is then loaded to the map as a point feature data layer and
converted into a vector tileset.
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The next step is to create map markers from this construction project data layer. A
function is used to create a map marker for each construction project. The map marker pulls the
coordinates from the geoJSON and plots each one on the map. A class name and event listener is
also added for each map marker. The class attributes are styled with CSS and elaborated more in
section 3.5.3. Event listeners activate a function based on a user action (Mozilla 2020). In this
case, the event listeners, activate information popups that present more information about the
construction project when clicked. Popups are described in more detail in section 3.5.4.
3.5.2. Filtering Code
The construction project visibility is controlled by the checkbox section of the web
application. The checkboxes are organized by property type, construction status, city, and
proximity to mass transit. Each checkbox section has its own name attribute. When the user
selects a combination of property type, construction status, city, or proximity to mass transit, a
JavaScript function gathers all the selected checkboxes from each section. This function then
toggles the visibility of the map markers that correspond to the checked inputs. To clear all the
selected checkboxes, the user can click on the “Unselect All” button. When clicked, this button
activates a function that refreshes the web application and returns the page view to the initial
state.
3.5.3. CSS Filters
This section describes how CSS is used to change the visibility of the construction
mapmakers using the class attribute. Section 3.5.3.1 describes how class attributes are assigned
to each marker via the GeoJSON. Section 3.5.3.2 shows how colors are assigned via the class
attribute.
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3.5.3.1. Linking Class to Map Markers
Each construction project is defined as a point feature within the GeoJSON. The CSS for
this web app refers to the nested “klass” key within the “properties” key. In Figure 10, the
construction project is assigned the following values for the “klass” key: Sunnyvale, office,
approved, off2, halfmlr.

Figure 10. “Klass” key example
3.5.3.2. CSS Color Assignments
Depending on the class combinations, the map marker may be assigned various colors.
The map markers consist of one color for each property type with a border signifying the
construction status.
Editing the different color combinations for property types and construction status
quickly becomes tedious. To make future edits easier, the colors for property types and
construction statuses are set as global variables. Changing these global variables quickly change
any color assignments that reference the global variables. Blur and spread are also defined as
global variables. Blur controls how quickly the colors fade in the background. A blur of zero has
no effect. Spread controls the size of the map marker border. Appendix A contains examples of
global variables and a CSS example.
3.5.4. Construction Project Information Popups
This section describes the coding behind the information popups. Each marker is
assigned an event listener. When clicked, a function is activated that zooms the map closer to the
construction project. An information popup appears that states the construction project name,
address, city, property type, owner, and construction status. Information about the property area,
34

hotel rooms, and multifamily units are saved separately in the GeoJSON. To account for the
different construction project property type combinations, a filter is added to not include property
area, hotel rooms, or multifamily units with a zero value.
3.5.5. Tips and Credits
The tips section gives two short instructions on how to use the web application. The first
suggestion is to click on the checkboxes to filter by property type, construction status, city, and
proximity to mass transit. The second suggestion reveals that the icons can be clicked for more
information.
Clicking on the Credits button activates an activated slideout menu that provides links to
the city planning departments, Santa Clara County parcel shapefiles, and title data. These links
are stored as hyperlinks in the web application. The city name corresponds to the planning
department. Santa Clara County links towards Santa Clara County GIS page that provides links
to the parcels. Selecting Landvision leads the user towards the parcel database used to help verify
construction ownership and address data.
3.6. User Survey
A survey was sent to brokers in the author’s San Jose office to gauge the requirements for
a construction project web application. The survey was sent with an initial draft of the
application to help give an idea of the finished product. Figure 11 shows the survey questions.

35

1. What property types are you most interested in (office, R&D, industrial, retail, hotel,
multifamily, etc.)?
2. What property characteristics would you like to be able to search by? Currently, I plan to
allow users to search the web map by property type, city, construction status, building area, and
ownership.
3. What cities or regions are you most interested to track?
4. If you would like to be able to search by square footage, what size ranges are you most
interested in?
5. If you would like to be able to search by hotel rooms or multifamily units, what room/unit
range are you most interest in?
6. Are you interested in proximity searches that are near freeways, mass transit, etc?
7. If you are interested in proximity searches, what distances do you believe will be most
relevant (within ½ mi., within 1 mi., within 5 mi., etc).
8. How can I make this construction project more useful for your needs?
Figure 11. Survey
Three brokers responded to this survey. The most popular property types were office,
research and development (R&D), and industrial. R&D properties consist of a property type that
combines office and industrial characteristics. The local brokers discriminate between R&D and
industrial property types. Since there are few industrial developments and even fewer R&D
developments in Silicon Valley, this application lumps R&D property types with industrial
property types. This designation conforms with R&D property definitions from other regions.
Los Angeles defines R&D property types as a flex property subset of industrial properties
(LaCRE 2017).
Other property types such as retail, hotel, and multifamily are included since they are
common property developments in Silicon Valley. The brokers mainly wanted Silicon Valley
construction projects. The common desired size ranged from 10,000 to 50,000 sqft. Most
construction projects were larger than this, so this range fell within the lower third of the
property size selection area. Larger developments were included to help other brokers in the
local branch. Common property characteristics requested were property type, location, and
building area. When asked about mass transit, every broker answered that they wanted it in the
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application. This added the checkbox selection for construction projects near mass transit options
such as BART, Caltrain, and VTA light rail.

37

Chapter 4 Results
This chapter provides an overview of the construction project web application. The app may be
accessed here: http://gis-web.usc.edu/abidog/draftchecki.html. Section 4.1 goes over the initial
view of the application when it first loads. Section 4.2 provides a walkthrough of the checkbox
inputs that select different combinations of property types, cities, construction statuses, and
proximity to mass transit. Section 4.3 describes the construction project information popups.
Section 4.4 goes over the source credits.
4.1. Main Map View
The local branch of the author’s brokerage is based in San Jose, CA. This city is within
Santa Clara County, so the application is centered over it to minimize scrolling. A satellite
basemap with street labels is chosen so that brokers can use freeways, major streets, and
landmarks to increase the spatial comprehension of the construction project and neighborhood.
Geocoder and navigation controls are included to increase the user friendliness of the
web application. The navigation controls consist of a module containing a plus and minus icon
with a compass icon on the bottom. The navigation controls are placed on the upper-right of the
map. The broker has the option to zoom in by clicking the plus icon or zoom out by clicking the
minus icon. Scrolling the mouse wheel forwards and backwards also accomplish the same effect,
but this may not be evident to the broker. The compass icon rotates to show the direction of true
north to the user.
The geocoder control allows the broker to input an address and create a place marker on
the requested site. This control is placed in the upper-left corner. The search control finds the
location with Mapbox’s geocoding service and zooms in to a view that includes the inputted
location and the nearest development. Brokers may want to see which construction projects are
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near the selected address. The geocoder control is set to geocode a rectangular area that
completely contains Santa Clara County. This helps prevent any stray address choices and keeps
the focus on the major business areas of the San Jose office. This helps prevent any errant
addresses that are too far from the construction projects and focuses the map view. “Silicon
Valley Addresses” is also defined in the geocoder control to suggest to users to only input
addresses within the Silicon Valley region. Figure 12 shows the application when it first loads.

Figure 12. Initial map view

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4.2. Checkbox Selection
The web application loads all the construction projects at once, with the default view
showing every construction project on the map. This helps convey nearby developments nearby a
property represented by a broker. This view is filtered by the section on the left as shown in
Figure 12. The broker can choose between different property types, construction statuses, cities,
and proximity to mass transit. The user can further select between different size ranges of the
property types. Clicking on these checkboxes filters the map view to show only the construction
projects meeting the chosen criteria. Everything else is hidden. The user can also choose to clear
the selection by clicking on the “Unselect All” button.
Property types and construction statuses are color coded to help show the differences for
each one. Figure 13 shows an example of a map marker following this color scheme. In this
example, the map marker represents a multifamily development currently under construction.

Figure 13. Multifamily under construction
Construction projects that contain more than one property type are called mixed
developments. This is represented by creating a map marker that places each property type side-
by-side as shown in Figure 14. In this example, the mixed development comprises of
multifamily, retail, and hotel property types.
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Figure 14. Mixed development
Figure 15 gives an example of a search of all construction properties that meet the
following criteria: all multifamily developments, under construction, in Santa Clara and
Mountain View, within a half-mile of CalTrain.

Figure 15. Search example
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4.3. Information Popup
For more information, brokers can click on the construction project icon. Each marker is
assigned an event listener. When clicked, the map zooms closer to the construction project and
an information popup appears. The popup has information that states the construction project
name, address, city, property type, owner, and construction status. Figure 16 shows a popup
example.

Figure 16. Information popup
In this case, the construction project is purely a retail property type. For mixed property
types, the different property types are separated by commas in the property type. This is how it is
saved in the GeoJSON. Figure 17 shows an example of a mixed property type popup that
contains retail, hotel, and office property types.
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Figure 17. Mixed property information popup
4.4. Tips and Credits
To obtain more information about the data sources, brokers may click on the credits link
on the lower left of the web application. This activates a slide out menu with links to city
planning websites and a short data collection methodology. The links highlight when the mouse
hovers over it to signify that it is clickable. Planning websites offer a way to gather more
information about a construction project. The methodology shows that all office, retail, and
industrial construction projects above 5,000 square feet are tracked. Also, all multifamily
projects above four units and hotel projects above four rooms are recorded. Figure 18 shows the
credits slide out menu.
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Figure 18. Credits slide out menu

44

Chapter 5 Discussion
The purpose of this project is to create a construction project web application for commercial
real estate brokers. This is achieved by selecting OGD and software that enables users to easily
filter by property type, size, city, construction status, and proximity to mass transit. Section 5.1
discusses the challenges and obstacles in creating the web application. Next, section 5.2 reviews
options on how to improve the application by scaling it up to track construction projects in more
offices within the author’s brokerage, suggestions on how to automate the workflow, and other
usability features to improve the UX of the web application.
5.1. Challenges
Creating the application to fulfill the requirements was more difficult than expected. The
main challenge involved programming and providing workarounds to make the program
function. City updates became more infrequent due to COVID-19 staffing measures. Data
collection also proved to be very tedious.
5.1.1. Programming
Lack of programming experience was the main challenge for this project. The proposal
initially had construction projects stored in a backend database. The backend refers to the part of
the web application that the user does not see. The frontend refers to the UI (Techterms 2020).
The UI then requests the information using structured query language (SQL). The user could
filter the construction data based on developer, property type, city, and area by predefined SQL
commands.
This approach failed and the construction projects were stored in a GeoJSON format.
This method provided a convenient way to store the construction project data. The GeoJSON has
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separate fields for the property type, developer, building size, city, proximity to mass transit, and
style commands. The application also became read-only where the construction projects are
predefined and formatted in the GeoJSON file. Since there is no way to define new construction
projects, filtering by GeoJSON fields became the optimal technique.
The GeoJSON conversion process can also be shortened. After assigning mass transit
proximity values and size values, the field representing the “klass” key can be edited using the
Calculate Field tool in ArcGIS Pro. This tool concatenates the strings representing the city,
property types, mass transit proximity values, and size values. This shrinks the original workflow
of exporting the construction points as a GeoJSON, converting this GeoJSON into a CSV,
editing the klass column by concatenating the strings, and changing the CSV back into a
GeoJSON.
Another challenge arose when programming numerical ranges for property area,
multifamily units, or hotel rooms. The initial approach was to let the user define minimum and
maximum amounts for these values. This failed since there were reference problems in storing
the user defined amounts and using these variables to define the minimum and maximum ranges.
A workaround was to predefine the property area, multifamily units, or hotel rooms into thirds.
These construction projects are then given different class values that correspond to their area.
The final product may not work as ideally as the San Francisco construction project site with
number inputs for the minimum and maximum values for property area, multifamily units, or
hotel rooms, but it is currently a crude approximation (San Francisco 2020).
5.1.2. City Updates
Before COVID-19, city planning departments stuck to a consistent schedule with few
delays. Most of the cities disclosed construction updates monthly. After COVID-19, updates
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became more infrequent. Cities may go months between disclosures due to staffing issues from
COVID-19 remote work measures. City staffers may not have the resources to provide frequent
updates. Construction projects may also change construction status, but are not disclosed in the
city planning websites. San Jose recently approved a planned office development, but did not
disclose it on their city planning website. The Mercury News, a local newspaper, disclosed the
construction project update (Angst 2021). To get the most current information, OGD like city
planning websites may have to be supplemented by local newspapers and trade journals.
5.1.3. Data Collection
Spotty data updates from city planning departments delay construction project
information. Data cleanup also causes notable delays. This is inherent since every city discloses
construction project information differently. Significant amounts of time are spent standardizing
every city’s disclosures into a format usable for this web application (Miller 2020). COVID
measures may reduce work by shrinking the total amount of construction projects, but this is
countered by searching newspapers and trade journals for missing information.
5.2. Future Work
This web application is an ongoing project that helps make it easier to track construction
developments. The next steps are to expand the areas to track construction projects, speed up the
workflow by automating as many processes as possible, and correct the programming issues to
make the application more user friendly. Section 5.2.1 goes over methods on how to scale up
tracking construction projects for more regions. Section 5.2.2 walks through steps that can be
automated. Section 5.2.3 goes over new program features to make the application more user
friendly.
47

5.2.1. Scaling Up
More people are necessary to scale up the application. To ensure that every relevant
feature gets tracked, the survey detailed in Section 3.6 can be distributed to other offices within
the author’s brokerage. There may be regional quirks that one office may prioritize over another
like the planning entitled status in San Francisco (San Francisco 2020) or R&D property types in
the Silicon Valley (Datastar 2020). Once the construction project qualities are defined, the
specifications can be sent to the local team of research analysts within the author’s brokerage for
data collection. The research analysts are then tasked with gathering the construction project data
from their area and maintaining it for any construction updates. These updates include changes in
construction status, project type and size, or even developer if the project sells. This data is then
aggregated into one large GeoJSON and fed into the web application.
Another approach to scale up and hasten the workflow process is to introduce the
application amongst my peers in the local CRE research analyst group, Datastar. Every
researcher in the group already tracks construction project developments. Sharing access to the
application benefits everyone involved and greatly speeds up data collection and maintenance.
Local trade journals also track development projects. For example, the Silicon Valley Business
Journal (2021) has the “Crane Watch” web map that tracks major developments in San Jose.
Sharing the application expands the scope to include all CRE developments in Silicon Valley and
greatly expands the size of the audience from solely within the author’s brokerage to the public.
Trade journals report on larger developments quicker than city planning departments. This
application collects details about smaller projects throughout the region that may not be as
newsworthy. Combining these updates provide convenient access to construction project
information that increases public awareness of future regional developments.
48

The web application also needs to be hosted on company or third-party servers such as
Amazon Web Services, Google Cloud, or Microsoft Azure. This thesis project is currently stored
in USC servers and will be offline at the end of the GIST Masters program. A workaround is to
store the web application on an individual computer, but the application cannot be shared to
other teams of researchers and brokers. This severely hampers the usability of the web
application.
5.2.2. Automation
To save time, the manual data entry and maintenance steps of the web application needs
to be minimized or removed. Automation can remove these repetitive tasks though functions that
let the computer accomplish these tasks. This frees up time and resources to work on other
aspects of the application. In ArcGIS Pro, ModelBuilder can be used to automate tasks such as
finding out which construction projects fall within a half mile or quarter mile of a mass transit
station, the geocoding process, splitting the property types into thirds by area, defining the klass
key in the GeoJSON for the CSS filters, and exporting the features as a GeoJSON.
Automation also makes it easier to timestamp data updates. As a project moves through
the construction pipeline, it is easier to keep track of when the construction status changes. If the
property type or size is modified, then timestamping makes it easier to track these edits.
Silicon Valley cities currently disclose construction projects in formats that are not
machine-scrapable friendly. As of the current version of the application, only Sunnyvale releases
an up-to-date version in a Microsoft Excel spreadsheet format (Sunnyvale 2020). As this
application scales up, other cities like Fremont discloses construction projects in machine
readable formats (Fremont 2020). Automating the construction project updates from cities that
49

provide updates in machine readable formats can greatly speed up the data entry and
maintenance of the web application.
5.2.3. New Program Features
Application updates may be added to increase the functionality and user experience.
Initial changes are directed to improving the search filters. Numerical input boxes may be used
instead of checkboxes to enable users to define the preferred ranges for property area,
multifamily units, hotel rooms, or proximity to mass transit. Text inputs for name searches can
also be added to search for all projects that involve a particular developer. Polygon searches can
also be used so that the application can show only projects within a user-defined area or
neighborhood. The usability of these different search options may be further augmented by
letting the user export the results in a CSV or pdf file for further analysis.
Other usability features to include involve data layers such as parcels and passenger
counts in mass transit stations. The construction projects are currently represented by point icons
on the web application. Adding a layer of all parcels involved for each construction project helps
communicate the scope of the development to the user. Construction projects near mass transit
stations help optimize usage as people use public transportation for their work commutes, reduce
road traffic, and create walkable urban areas (Zykofsky 1998). Multifamily, hotel, office, and
retail developments benefit from increased valuations and asking rents if they are in walkable
urban areas and attract future development in these districts (Loh et al. 2019).
50

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55

Appendix A CSS Examples
Global Variable

Single Property CSS Example

56

Mixed Property CSS Example

57

Appendix B Checkbox Section
These screenshots contain the checkbox code within the sidebar on the left-hand side of the
application.

58

59

60

Appendix C Major Map Functions
Map load with zoom control

Geocoder control

61

Assigning map markers to construction projects

62

Information popup code

63

Appendix D Visibility Filter Code

Abstract (if available)

Abstract The Santa Clara County economy has fueled demand for commercial real estate (CRE) developments. CRE brokers capitalize on this demand by attracting tenants to newly constructed buildings or by helping property owners sell to developers for future redevelopment opportunities. City planning departments disclose construction project information within their boundaries. Each city has its own property methodology and data release schedule with few sources recording these sources regionally. The main objective is to create a CRE construction web application that tracks construction projects within the major cities of Santa Clara County. This helps save time by standardizing the project data and aggregating all the information into one program. The progress of the CRE development is tracked throughout the pipeline as it goes through the following statuses: pending approval, approved, and under construction. The database also focuses on the following property types: office, hotel, multifamily, retail, and industrial. This construction data is organized and presented to brokers via a web GIS application to enable spatial searches in an easy-to-use interface.

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Asset Metadata

Creator Abidog, Erwin (author)

Core Title Silicon Valley construction project web mapping application

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Degree Conferral Date 2021-08

Publication Date 07/16/2021

Defense Date 05/14/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Cascading Style Sheets,Construction,CSS,database,GeoJSON,GIS,Javascript,Mapbox,OAI-PMH Harvest,OGD,Open Government Data,Real Estate,Silicon Valley,user experience,user interface

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Sedano, Elisabeth (committee chair), Duan, Leilei (committee member), Swift, Jennifer (committee member)

Creator Email abidog@usc.edu,eabidog@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15600909

Unique identifier UC15600909

Legacy Identifier etd-AbidogErwi-9757

Document Type Thesis

Format application/pdf (imt)

Rights Abidog, Erwin

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu