145 research outputs found
Exploring the linguistic landscape of geotagged social media content in urban environments
This article explores the linguistic landscape of social media posts associated with specific geographic locations using computational methods. Because physical and virtual spaces have become increasingly intertwined due to location-aware mobile devices, we propose extending the concept of linguistic landscape to cover both physical and virtual environments. To cope with the high volume of social media data, we adopt computational methods for studying the richness and diversity of the virtual linguistic landscape, namely, automatic language identification and topic modelling, together with diversity indices commonly used in ecology and information sciences. We illustrate the proposed approach in a case study covering nearly 120,000 posts uploaded on Instagram over 4.5 years at the Senate Square in Helsinki, Finland. Our analysis reveals the richness and diversity of the virtual linguistic landscape, which is also shown to be susceptible to continuous change.Peer reviewe
Tracking physical events on social media
Social media platforms have emerged as the widely accessed form of communication channel on the world wide web in the modern day. The first ever social networking website came into existence in the year 2002 and currently there are about 2.08 billion social media users around the globe. The participation of users within a social network can be considered as an act of sensing where they are interacting with the physical world and recording the corresponding observations in the form of texts, pictures, videos, etc. This phenomenon is termed as Social Sensing and motivates us to develop robust techniques which can estimate the physical state from the human observations.
This dissertation addresses a set of problems related to detection and tracking of real-world events. The term ‘event’ refers to an entity that can be characterized by spatial and temporal properties. With the help of these properties we design novel mathematical models that help us with our goals. We first focus on a simple event detection technique using ‘Twitter’ as the source of information. The method described in this work allow us to perform detection in a completely language independent and unsupervised fashion. We next extend the event detection problem to a different type of social media, ‘Instagram’, which allows users to share pictorial information of nearby observations. With the availability of geotagged data we solve two different subproblems - the first one is to detect and geolocalize the instance of an event and the second one is to estimate the path taken by an event during its course. The next problem we look at is related to improving the quality of event localization with the help of text and metadata information. Twitter, in general, has less volume of geotagged data available in comparison to Instagram, which demands us to design methods that explore the supplementary information available from the detected events. Finally, we take a look at both the social networks at the same time in order to utilize the complementary advantages and perform better than the methods designed for the individual networks
Inferring the geolocation of tweets at a fine-grained level
Recently, the use of Twitter data has become important for a wide range of real-time applications, including real-time event detection, topic detection or disaster and emergency management. These applications require to know the precise location of the tweets for their analysis. However, approximately 1% of the tweets are finely-grained geotagged, which remains insufficient for such applications. To overcome this limitation, predicting the location of non-geotagged tweets, while challenging, can increase the sample of geotagged data to support the applications mentioned above. Nevertheless, existing approaches on tweet geolocalisation are mostly focusing on the geolocation of tweets at a coarse-grained level of granularity (i.e., city or country level). Thus, geolocalising tweets at a fine-grained level (i.e., street or building level) has arisen as a newly open research problem. In this thesis, we investigate the problem of inferring the geolocation of non-geotagged tweets at a fine-grained level of granularity (i.e., at most 1 km error distance). In particular, we aim to predict the geolocation where a given tweet was generated using its text as a source of evidence.
This thesis states that the geolocalisation of non-geotagged tweets at a fine-grained level can be achieved by exploiting the characteristics of the 1\% of already available individual finely-grained geotagged tweets provided by the Twitter stream. We evaluate the state-of-the-art, derive insights on their issues and propose an evolution of techniques to achieve the geolocalisation of tweets at a fine-grained level.
First, we explore the existing approaches in the literature for tweet geolocalisation and derive insights on the problems they exhibit when adapted to work at a fine-grained level. To overcome these problems, we propose a new approach that ranks individual geotagged tweets based on their content similarity to a given non-geotagged. Our experimental results show significant improvements over previous approaches.
Next, we explore the predictability of the location of a tweet at a fine-grained level in order to reduce the average error distance of the predictions. We postulate that to obtain a fine-grained prediction a correlation between similarity and geographical distance should exist, and define the boundaries were fine-grained predictions can be achieved. To do that, we incorporate a majority voting algorithm to the ranking approach that assesses if such correlation exists by exploiting the geographical evidence encoded within the Top-N most similar geotagged tweets in the ranking. We report experimental results and demonstrate that by considering this geographical evidence, we can reduce the average error distance, but with a cost in coverage (the number of tweets for which our approach can find a fine-grained geolocation).
Furthermore, we investigate whether the quality of the ranking of the Top-N geotagged tweets affects the effectiveness of fine-grained geolocalisation, and propose a new approach to improve the ranking. To this end, we adopt a learning to rank approach that re-ranks geotagged tweets based on their geographical proximity to a given non-geotagged tweet. We test different learning to rank algorithms and propose multiple features to model fine-grained geolocalisation. Moreover, we investigate the best performing combination of features for fine-grained geolocalisation.
This thesis also demonstrates the applicability and generalisation of our fine-grained geolocalisation approaches in a practical scenario related to a traffic incident detection task. We show the effectiveness of using new geolocalised incident-related tweets in detecting the geolocation of real incidents reports, and demonstrate that we can improve the overall performance of the traffic incident detection task by enhancing the already available geotagged tweets with new tweets that were geolocalised using our approach.
The key contribution of this thesis is the development of effective approaches for geolocalising tweets at a fine-grained level. The thesis provides insights on the main challenges for achieving the fine-grained geolocalisation derived from exhaustive experiments over a ground truth of geotagged tweets gathered from two different cities. Additionally, we demonstrate its effectiveness in a traffic incident detection task by geolocalising new incident-related tweets using our fine-grained geolocalisation approaches
Blending big data analytics : review on challenges and a recent study
With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE
Linking geosocial sensing with the socio-demographic fabric of smart cities
Technological advances have enabled new sources of geoinformation, such as geosocial media, and have supported the propagation of the concept of smart cities. This paper argues that a city cannot be smart without citizens in the loop, and that a geosocial sensor might be one component to achieve that. First, we need to better understand which facets of urban life could be detected by a geosocial sensor, and how to calibrate it. This requires replicable studies that foster longitudinal and comparative research. Consequently, this paper examines the relationship between geosocial media content and socio-demographic census data for a global city, London, at two administrative levels. It aims for a transparent study design to encourage replication, using Term Frequency—Inverse Document Frequency of keywords, rule-based and word-embedding sentiment analysis, and local cluster analysis. The findings of limited links between geosocial media content and socio-demographic characteristics support earlier critiques on the utility of geosocial media for smart city planning purposes. The paper concludes that passive listening to publicly available geosocial media, in contrast to pro-active engagement with citizens, seems of limited use to understand and improve urban quality of life
Developing a Prototype System for Syndromic Surveillance and Visualization Using Social Media Data.
Syndromic surveillance of emerging diseases is crucial for timely planning and execution of epidemic response from both local and global authorities. Traditional sources of information employed by surveillance systems are not only slow but also impractical for developing countries. Internet and social media provide a free source of a large amount of data which can be utilized for Syndromic surveillance.
We propose developing a prototype system for gathering, storing, filtering and presenting data collected from Twitter (a popular social media platform). Since social media data is inherently noisy we describe ways to preprocess the gathered data and utilize SVM (Support Vector Machine) to identify tweets relating to influenza like symptoms. The filtered data is presented in a web application, which allows the user to explore the underlying data in both spatial and temporal dimensions
Using Social Media to Assess the Impact of Weather and Climate on Visitation to Outdoor Recreation Settings
When people post photos on social media, these photos often contain information on the location, time, and date the photo was taken; all of this information is stored as metadata and is often never seen or used by the individuals posting the photos. This information can be used by researchers however, to understand the total number of visitors to parks and protected areas, as well as specific places people visit within those parks and protected areas. The first study in this dissertation reviews all the ways social media has been used to understand visitation and visitors’ experiences in parks. Researchers can connect the photo locations from social media to other datasets to understand how different factors, such as the weather or climate, influence park visitation. Weather refers to the conditions, such as temperature or precipitation, at any given place and time; climate refers to the long-term weather averages at a location, often over a period of 30 years or more. The second paper explores how weather affects where visitors go within 110 U.S. National Parks. Daily temperature and precipitation influence visitors’ elevation and distance to roads, parking areas, buildings, and bodies of water. However, the effect of weather varies in parks with different climates and landscapes. Visitors in some parks may be more able to adapt to adverse weather conditions by visiting park areas with preferable weather. In the third study, I examine how the climate of federal and state-managed public lands impact visitation by season. Across the conterminous U.S., visitation was higher in places with warmer average temperatures in the fall, spring, and winter. However, visitation was higher in places with relatively cooler average temperatures in the summer. Climate has a larger effect on visitation to public lands in the summer and winter, and in the Western U.S. Collectively, these studies provide insight into how visitation to and within parks, protected areas, and public lands in the U.S. may change due to weather conditions and climate change
Pro Web 2.0 Mashups: Remixing Data and Web Services
XXXIII, 603 p. ; 24 cmLibro ElectrónicoEn cub.: Remix the Web to create cutting-edge web applicationsHow many times have you seen a web site and said, “This would be exactly what I wanted—
if only . . .” If only you could combine the statistics here with data from your company’s earnings
projections. If only you could take the addresses for those restaurants and plot them on one
map. How often have you entered the date of a concert into your calendar with a single click
instead of retyping? How often do you wish that you could make all the different parts of your
digital world—your e-mail, your word processor documents, your photos, your search results,
your maps, your presentations—work together more seamlessly? After all, it’s all digital and
malleable information—shouldn’t it all just fit together?
In fact, below the surface, all the data, web sites, and applications you use could fit together.
This book teaches you how to forge those latent connections—to make the Web your own—by
remixing information to create your own mashups. A mashup, in the words of the Wikipedia, is
a web site or web application “that seamlessly combines content from more than one source
into an integrated experience.”1 Learning how to draw content from the Web together into new
integrated interfaces and applications, whether for yourself or for other others, is the central
concern of this book.¿Cuántas veces ha visto usted a un sitio web y le dijo: "Esto sería exactamente lo que quería-
si sólo. . . "Si sólo pudiera combinar las estadísticas aquí con los datos de las ganancias de su empresa
proyecciones. Si tan sólo pudiera tener las direcciones de los restaurantes y colócalas en una
mapa. ¿Cuántas veces has entrado en la fecha de un concierto en su calendario con un solo clic
en lugar de volver a escribir? ¿Con qué frecuencia desea que usted podría hacer todas las diferentes partes de su
mundo digital, el correo electrónico, los documentos procesador de textos, fotos, resultados de la búsqueda,
sus mapas, sus presentaciones, trabajar juntos con mayor perfección? Después de todo, todo es digital y
maleable que la información shouldn't a sólo encajan entre sí?
De hecho, debajo de la superficie, todos los datos, sitios web, y aplicaciones que utiliza podría encajar.
Este libro te enseña a forjar esas conexiones latentes a hacer de la web su propio por
información remezcla para crear su propia mashups. Un mashup, en palabras de la Wikipedia, es
un sitio web o aplicación web "que combina a la perfección el contenido de más de una fuente
en una experiencia integrada. "1 Aprender a dibujar el contenido de la Web junto a nuevos
interfaces integradas y aplicaciones, ya sea para usted o para otros, es el centro de
preocupación de este libro.The modern Web is awash with data and services just waiting to be used, but how do you make effective use of all this information? The answer lies in APIs (such as Google Maps, Flickr, and Amazon Web Services) and remixing, or mashups. "Pro Web 2.0 Mashups: Remixing Data and Web Services" teaches you everything you need to create useful, dynamic real-world applications using APIs, web services, Ajax, web standards, and server-side languages. All you need to make full use of this book is basic knowledge of HTML, CSS, and JavaScript, and at least one server-side language (such as PHP or ASP.NET). Highlights include the following: Looks at the overall shape of todays Web from a developers point of view--what are its main features, and what is available for us to use to develop applications? Contains real-world examples of creating mashups using all the major APIs. Contains examples written in multiple server-side languages. What you'll learn Understand how the constituent parts of the modern Web fit together--web standards, Ajax, APIs, libraries, tagging, blogs, wikis, and more. Create different types of mashup, for example mapping mashups, search functionality, calendars, RSS/Atom feeds, social bookmarking, online storage systems, open document formats, and more. Build Web 2.0 applications using HTML, CSS, JavaScript, Ajax, server-side languages, APIs, and libraries Who is this book for? This book is for any web developer who is already comfortable with HTML, CSS, JavaScript, and at least one server-side language and wants to learn how to create Web 2.0 applications. About the Apress Pro Series The Apress Pro series books are practical, professionaltutorials to keep you on and moving up the professional ladder. You have gotten the job, now you need to hone your skills in these tough competitive times. The Apress Pro series expands your skills and expertise in exactly the areas you need. Master the content of a Pro book, and you will always be able to get the job done in a professional development project. Written by experts in their field, Pro series books from Apress give you the hard-won solutions to problems you will face in your professional programming career. Related Titles Beginning Google Maps Applications with PHP and Ajax: From Novice to Professional Beginning Google Maps Applications with Rails and Ajax: From Novice to Professional Building Flickr Applications with PHP Pro DOM Scripting with Ajax, APIs and Libraries Pro Ajax and the .NET 2.0 Platform Pro Ajax and Java Frameworks.About the Author xxi
About the Technical Reviewer xxiii
Acknowledgments xxv
Introduction xxvii
PART 1 Remixing Information
Without Programming
CHAPTER 1 Learning from Specific Mashups 3
Looking for Patterns in Mashups 3
Housingmaps.com5
What Is Being Combined? 5
Why Are the Constituent Elements Being Combined? What’s the
Problem Being Solved? 5
Where Is the Remixing Happening? 6
How Are These Elements Being Combined? 6
Comparable Mashups 7
Google Maps in Flickr 7
What Is Being Combined? 8
Why Are the Constituent Elements Being Combined? What’s the
Problem Being Solved? 8
How Are These Elements Being Combined? 12
Comparable Mashups 13
LibraryLookup Bookmarklet13
Configuring a LibraryLookup Bookmarklet 14
Invoking the LibraryLookup Bookmarklet 15
How Does This Mashup Work? 16
How Can This Mashup Be Extended?17
Comparable Mashups 18
Tracking Other Mashups 18
Summary 18
vii
CHAPTER 2 Uncovering the Mashup Potential of Web Sites 21
What Makes Web Sites and Applications Mashable 22
Ascertaining the Fundamental Entities of the Web Site22
Public APIs and Existing Mashups 23
Use of Ajax 24
Embedded Scriptability 24
Browser Plug-Ins 25
Getting Data In and Out of the Web Site 25
The Community of Users and Developers 25
Mobile and Alternative Interfaces and the Skinnability
of the Web Site 26
Documentation 26
Is the Web Site Run on Open Source? 26
Intellectual Property, Reusability, and Creative Commons 26
Tagging, Feeds, and Weblogging27
URL Languages of Web Sites27
Some Mashups Briefly Revisited 28
Flickr: The Fundamentally Mashup-Friendly Site 29
Resources in Flickr 29
Users and Photos 30
Data Associated with an Individual Photo 33
Tags 34
User’s Archive: Browsing Photos by Date 36
Sets 37
Collections37
Favorites 37
A User’s Popular Photos 38
Contacts 38
Groups 38
Account Management40
Browsing Through Flickr40
Search 41
Geotagged Photos in Flickr 42
The Flickr Organizer 43
Recent Activities44
Mailing Interfaces 44
Interfacing to Weblogs 44
Syndication Feeds: RSS and Atom 45
Mobile Access45
Third-Party Flickr Apps 45
viii CONTENTS
Creative Commons Licensing 46
Cameras 46
The Mashup-by-URL-Templating-and-Embedding Pattern 47
Google Maps 49
URL Language of Google Maps 49
Viewing KML Files in Google Maps51
Connecting Yahoo! Pipes and Google Maps 51
Other Simple Applications of the Google Maps URL Language 52
Amazon 53
Amazon Items53
Lists 55
Tags 55
Subject Headings 55
del.icio.us 56
Screen-Scraping and Bots 58
Summary 60
CHAPTER 3 Understanding Tagging and Folksonomies61
Tagging in Flickr 62
Tags in Flickr 63
How Tags Are Used in Practice 63
Creating Your Own Tags 64
Syntax of Tags in Flickr 64
Potential Weaknesses of Tags 65
Singular and Plural Forms of Tags in Flickr 65
Hacking the Tagging System: Geotagging and Machine Tags 66
Interesting Apps Using Flickr Tags 67
Tagging in del.icio.us 67
Mechanics of Adding Tags in del.icio.us 68
Dealing with Case and Multiword Phrases 68
Getting More Information 69
Gathering Content Through Tags in Technorati71
Searching Technorati with Tags71
How Technorati Finds Tags on the Web 72
Word Inflections and Syntactic Constraints in Technorati Tags 72
Using Tags to Mash Up Flickr and del.icio.us 72
Other Systems That Use Tagging 73
Relationship of Tags to Formal Classification Schemes 73
Summary 75
CONTENTS ix
CHAPTER 4 Working with Feeds, RSS, and Atom77
What Are Feeds, and Why Are They Important? 78
RSS 2.0 78
RSS 1.0 80
Atom 1.0 82
Extensions to RSS 2.0 and Atom 1.0 84
Feeds from Flickr86
Flickr Feed Parameters 86
Examining the Flickr Feeds 87
Exchange Formats Other Than RSS and Atom 90
Feeds from Other Web Sites 92
Finding Feeds and Feed Autodiscovery 93
Feeds from Weblogs 94
Wikipedia Feeds94
Google and Yahoo! News 95
News Aggregators: Showing Flickr Feeds Elsewhere 96
Validating Feeds98
Scraping Feeds Using GUI Tools 98
Remixing Feeds with Feedburner 99
Remixing Feeds with Yahoo! Pipes 100
A Simple First Pipe with Yahoo! News 101
Google News and Refactoring Pipes102
Wikinews and NY Times: Filtering Feeds 103
Pulling the Feeds Together 104
Summary 104
CHAPTER 5 Integrating with Blogs 105
Integration Scenarios for Blogs 105
Sending Flickr Pictures to Blogs 106
Configuring Flickr for Integration with Blogs107
Blogging a Flickr Picture110
How Does the Flickr Blog Integration Work?110
Desktop Blogging Tools 111
Combining Feeds and Blogging to Generate Feedback Flows113
Flock: Bringing Together Blogs and Flickr 114
RSD: Discoverability of Blog APIs 115
Linkbacks 116
Wiki Integration at an Early Stage 116
Summary 117
x CONTENTS
PART 2 Remixing a Single Web Application
Using Its API
CHAPTER 6 Learning Web Services APIs Through Flickr 121
An Introduction to the Flickr API 122
What Does This XML Response Mean? 124
What Can You Do with the XML Response? 126
API Documentation, Community, and Policy 128
Terms of Use for the API 128
Using the Flickr API Explorer and Documentation 129
Calling a Basic Flickr API Method from PHP 132
HTTP Clients 133
A Refresher on HTTP 134
XML Processing 138
Pulling It All Together: Generating Simple HTML
Representations of the Photos 143
Where Does This Leave Us?145
The Flickr API in General 145
Using flickr.reflection Methods 146
Querying the Flickr Reflection Methods with PHP 149
Request and Response Formats 154
Flickr Authorization 156
Why Passing Passwords Around Doesn’t Work Too Well157
Authorization for Web Apps 157
Using Flickr API Kits 165
PEAR::Flickr_API 165
phpFlickr 166
Phlickr 168
Limitations of the Flickr API 169
Summary 170
CHAPTER 7 Exploring Other Web APIs 171
XML-RPC172
What’s Happening on the Wire? 176
Using Wireshark and curl to Analyze and Formulate
HTTP Messages 177
Parsing XML-RPC Traffic178
CONTENTS xi
SOAP 181
The Dream: Plug-and-Go Functionality Through WSDL
and SOAP 181
geocoder.us 182
Amazon ECS 191
The Flickr API via SOAP195
Learning About Specific Web APIs 195
Programmableweb.com 196
YouTube 198
GData and the Blogger API 199
Using the Blogger API As a Uniform Interface Based on
HTTP Methods203
Summary 204
CHAPTER 8 Learning Ajax/JavaScript Widgets and Their APIs 205
What You Need to Know206
What Difference Does Ajax Make? 207
Learning Firebug, DOM Inspector, and JavaScript Shell 208
Using the DOM Inspector 208
Using the Firebug Extension for Firefox 208
Using the JavaScript Shell 210
Working with JavaScript Libraries 210
YUI Widgets211
Using the YUI Calendar 211
Installing YUI on Your Host 212
Learning Google Maps 213
Accessing Flickr via JavaScript217
Using Greasemonkey to Access New York Times Permalinks 220
Learning More About JavaScript and Ajax 223
Summary 223
PART 3 Making Mashups
CHAPTER 9 Moving from APIs and Remixable Elements
to Mashups 227
Getting Oriented to ProgrammableWeb 228
User-Generated Data in ProgrammableWeb 228
Can Any Directory of Mashups Keep Up? 228
Learning About the Overall Mashup Scene 229
xii CONTENTS
Directory of Mashups 230
Using Feeds to Track Mashups 230
Using Tags to Describe Mashups 231
API and Mashup Verticals 233
Looking at a Specific Mashup Profile233
Going from a Specific API to Mashups234
Sample Problems to Solve Using Mashups235
Tracking Interesting Books235
Knowing When to Buy Airplane Tickets 239
Finding That Dream House240
Mapping Breaking News 241
Summary 242
CHAPTER 10 Creating Mashups of Several Services 243
The Design 244
Background: Geotagging in Flickr245
Background: XMLHttpRequest and Containing Libraries 248
Using XMLHttpRequest Directly248
Using the YUI Connection Manager250
Building a Server-Side Proxy253
What Happens with XHR and Direct API Calls?253
Building a Server-Side Script for Geolocated Photos255
Building a Simple Client-Side Frame 257
Reading and Writing Elements257
Handling Simple Events to Connect Form Input and
Display Calculations 260
Hooking the Client-Side Framework to Flickr 261
Writing a URL for Querying flickrgeo.php 262
Using XHR via the YUI Connection Manager to
Read the JSON 262
Converting the JSON to HTML 264
Mashing Up Google Maps API with Flickr 266
Setting Up a Basic Google Map 267
Making the Map Respond to Changes in the
Viewport of the Map268
Bringing Together the Flickr and GMap Code 269
Wiring Up the Bounding Box of the Google Map270
Making the Pictures Show Up in the Map 272
Google Mapplet That Shows Flickr Photos 277
Summary 281
CONTENTS xiii
CHAPTER 11 Using Tools to Create Mashups 283
The Problem Mashup Tools Solve284
What You Are Making in This Chapter 284
Making the Mashup: A Step-by-Step Example286
Familiarizing Yourself with the Google Mashup Editor287
Reading and Displaying a Feed (Simple Template) 288
Introducing a Custom Template289
Using Yahoo! Pipes to Access Flickr 291
Displaying Flickr Photos Using 292
Adding JavaScript to the Mashup 294
How to Persist Feeds and Use Tabs 299
The Final Product: Showing the Saved Entries on a Map 304
Analysis of Trade-Offs in Using GME and Yahoo! Pipes309
Other Mashup Tools 310
Summary 311
CHAPTER 12 Making Your Web Site Mashable313
Why Make Your Web Site Mashable? 314
Using Techniques That Do Not Depend on APIs 314
Use a Consistent and Rich URL Language314
Use W3C Standards to Develop Your Web Site 315
Pay Attention to Web Accessibility315
Consider Allowing Users to Tag Your Content 315
Make Feeds Available 315
Make It Easy to Post Your Content to Blogs and
Other Web Sites 316
Encourage the Sharing of Content with Explicit Licenses317
Develop Extensive Import and Export Options for
User Content 317
Study How Users Remix Your Content and
Make It Easier to Do So 317
Creating a Mashup-Friendly API 317
Learn From and Emulate Other APIs318
Keep in Mind Your Audiences for the API 318
Make Your API Easy to Learn 318
Test the Usability of Your API 319
Build a Granular, Loosely Coupled Architecture So That
Creating an API Serves You As Much As It Does Others319
Embrace REST But Also Support SOAP and XML-RPC
If You Can 320
xiv CONTENTS
Consider Using the Atom Publishing Protocol As a Specific
Instantiation of REST 320
Encourage the Development of API Kits: Third Party or
In-House320
Support Extensive Error Reporting in Your APIs 321
Accept Multiple Formats for Output and Input 321
Support UI Functionality in the API 321
Include a Search API for Your Own Site 321
Version Your API 322
Foster a Community of Developers322
Don’t Try to Be Too Controlling in Your API322
Consider Producing a Service-Level Agreement (SLA) 322
Help API Users Consume Your Resources Wisely 323
Consider Open Sourcing Your Application 323
Easy-to-Understand Data Standards 323
Summary 324
PART 4 Exploring Other Mashup Topics
CHAPTER 13 Remixing Online Maps and 3D Digital Globes327
The Number of Online Maps 328
Examples of Map-Based Mashups329
Making Maps Without Programming 329
Mapbuilder.net 329
Google My Maps 331
A Mashup Opportunity: Mapping Yahoo! Local Collections332
Transforming the Yahoo! Local XML into CSV for
Mapbuilder.net 334
Collection Building in Microsoft’s Live Search Maps 336
Summary of Making Maps Without Programming 338
Data Exchange Formats 338
CSV338
Microformats and Metatags for HTML 338
GeoRSS 339
Yahoo!’s Use of GeoRSS and Yahoo! YMaps Extensions 341
KML 345
Interoperability Among Formats: GeoRSS vsKML346
CONTENTS xv
Creating Maps by API Programming 346
Google Maps API 347
Yahoo! Maps API351
Microsoft’s Live Search Maps/Virtual Earth354
Geocoding356
Yahoo! Maps 356
Geocoder.us 357
Google Geocoder 358
Virtual Earth 361
Geocoding Non-U.SAddresses363
Google Earth and KML 364
Displaying and Handling KML As End Users 364
KML 368
Programming Google Earth via COM and AppleScript374
Mapstraction and OpenLayers 376
An Integrative Example: Showing Flickr Pictures in Google Earth376
KML NetworkLink 379
Generating the KML for the Photos382
The flickrgeo.php Code383
Summary 393
CHAPTER 14 Exploring Social Bookmarking and Bibliographic
Systems 395
The Social Bookmarking Scene 396
Using Programmableweb.com to Examine the
Popularity of APIs 396
del.icio.us 397
Using the del.icio.us API 398
Third-Party Tools for del.icio.us405
Third-Party API Kits 405
Yahoo! Bookmarks and MyWeb407
Connotea408
A Flickr and del.icio.us Mashup 412
Summary 416
CHAPTER 15 Accessing Online Calendars and Event Aggregators 417
Google Calendar 418
Setting Up Google Calendar As an End User 418
Exploring the Feed Formats from Google Calendar 420
xvi CONTENTS
Using the GData-Based Calendar API Directly 426
Using the PHP API Kit for Google Calendar 434
Using the Python API Kit for Google Calendar 437
30boxes.com 438
An End User Tutorial 439
30boxes.com API 439
Event Aggregators 443
Upcoming.yahoo.com 443
Eventful.com452
Programming with iCalendar 458
Python and iCalendar 458
PHP and iCalendar 460
Exporting an Events Calendar to iCalendar and Google Calendar461
The Source: UC Berkeley Event Calendars 462
Creating an iCalendar Feed of Critic’s Choice Using Python462
Writing the Events to Google Calendar464
Summary 471
CHAPTER 16 Using Online Storage Services 473
Introducing Amazon S3 473
Rationale for S3 474
Conceptual Structure of Amazon S3 475
The Firefox S3 Extension Gets You Started with S3476
Using the S3 REST Interface 477
Listing Buckets Using the REST Interface 480
Using the SOAP Interface to S3481
Amazon S3 API Kits 482
PHP 483
Python 484
Summary 486
CHAPTER 17 Mashing Up Desktop and Web-Based Office Suites 487
Mashup Scenarios for Office Suites 487
The World of Document Markup 488
The OpenDocument Format488
Learning Basic ODF Tags 497
Create an ODF Text Document Without Any Styling of
ODF Elements 499
Setting the Paragraph Text to text-body 503
CONTENTS xvii
Formatting Lists to Distinguish Between Ordered and
Unordered Lists504
Getting Bold, Italics, Font Changes, and Color Changes
into Text Spans 505
API Kits for Working with ODF 507
Odfpy 507
OpenDocumentPHP 516
Leveraging OO.o to Generate ODF 518
ECMA Office Open XML (OOXML) 519
Viewers/Validators for OOXML522
Comparing ODF and OOXML 522
Online Office Suites523
Usage Scenarios for Programmable Online Spreadsheets 523
Google Spreadsheets API 524
Python API Kit 524
Mashup: Amazon Wishlist and Google Spreadsheets Mashup528
Zend PHP API Kit for Google Spreadsheets 533
A Final Variation: Amazon Wishlist to Microsoft Excel
via COM 535
Zoho APIs 536
Summary 536
CHAPTER 18 Using Microformats and RDFa As Embeddable Data
Formats537
Using Operator to Learn About Microformats 537
adr (Addresses) 540
hCard (Contacts) 541
hCalendar (Events)542
geo (Locations)543
tag (Tagspaces) 543
Definitions and Design Goals of Microformats 543
Microformats Design Patterns545
rel-design-pattern 545
class-design-pattern 545
abbr-design-pattern 546
include-pattern546
Examples of Microformats 547
rel-license 547
rel-tag 548
xfn548
xviii CONTENTS
xFolk549
geo 549
hCard and adr550
hCalendar 551
Other Microformats 551
Microformats in Practice 552
Programming with Microformats 552
Language-Specific Libraries 552
Writing an Operator Script 553
Studying the Tutorial Script 554
Writing a Geocoding Script556
Resources (RDFa): A Promising Complement to Microformats 557
Reference for Further Study 558
Summary 558
CHAPTER 19 Integrating Search 559
Google Ajax Search 559
Manipulating Search Results 559
Yahoo! Search 561
Yahoo! Images 563
Microsoft Live.com Search 564
OpenSearch 568
Google Desktop HTTP/XML Gateway 570
Summary 571
APPENDIX 573
INDEX 57
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