PDF Document This white paper provides a description of the aracer.mobi research project from both citizen and government . According to the renown philosopher of technology Jean-Luc Nancy ( ), “From now on there is an . luxury for many, they are well-equipped to realize their indispensable role as agents for change. Jakarta, as the capital city, naturally has the largest proportion . On 15 June , aracer.mobi launched its White Paper online, and now it is available on PDF. During Monsoon Flooding in Jakarta, Indonesia provides description of our project from Year 1 and how we crowd-map.
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Since its debut in (as aracer.mobi), the aracer.mobi In the World Disaster Report In , the Federal Communication Commission. Jakarta: Depar-temen Pendidikan dan Kebudayaan Direktorat Jenderal Http:// aracer.mobi Diakses. peta transjakarta pdf. Quote. Postby Just» Sat Mar 2, am. Looking for peta transjakarta pdf. Will be grateful for any help! Top.
In Jakarta, risk information and coordination through open data protocols is critical to support decision-making about disaster response, emergency planning, and community resilience.
The development of social media and application-driven data collection via mobile devices allows for unprecedented data collection capacities; in order to be effective, these technologies require coordination through robust, enterprise-grade open source software. BPBD DKI Jakarta is regularly faced with the difficult challenge of anticipating and responding to floods hazards and related extreme weather events in Jakarta.
As a research partner, the organization allowed PetaJakarta. By carefully studying the operational procedures, concerns, and ambitions of the Agency, the project developed tools that could be integrated effectively into the existing structure of the organization and its various data flowlines, and transferred incrementally over the course of the research collaboration, thereby ensuring an efficient strategy for both development and implementation. Twitter was selected as the ideal social media platform for the Joint Pilot Study because Jakarta has one of the highest concentrations of Twitter users in the world.
Additionally, a Twitter DataGrant gave the research team unprecedented access to a suite of historical data about flooding in Jakarta, thus allowing the system to be developed and calibrated with large data sets, thereby ensuring its operative functionality during the actual monsoon season in Finally, by working in close collaboration with Twitter, the project benefitted from the advice and mentorship of a group of media experts, engineers, and advocates who were all critical to the overall success of the project.
According to Easterling Thus, a GeoSocial Intelligence Framework depends on four basic principles: The value of social media as a critical tool in the disaster emergency management toolkit has be proven by a number of previous studies Meier ; the move from social media to GeoSocial Intelligence is an especially promising evolution of this prior research and software development.
While the utility of passively-mined social media data can offer insights for offline analytics and derivative studies for future planning scenarios, the critical issue for frontline emergency responders is the organization and coordination of actionable, real-time data related to disaster situations. From this perspective, GeoSocial Intelligence can be reasonably understood as a promising evolution within the disaster risk management information ecosystem because it leverages both the inherent capabilities within ubiquitous mobile devices i.
GNSS-enabled messaging and the network capabilities of social media, through OSS to provide validated and actionable information for citizens and government agencies, thereby improving situational knowledge and increasing response times in disaster scenarios.
The objective of this OSS is to remove redundancies in the study and promotion of urban resilience to extreme weather events as a result of climate change; the elimination of redundancies occurs because CogniCity can be used simultaneously to study patterns and practices of resilience among users and government agencies, and to enable more effective sharing of information between citizens, government agencies, and NGOs.
At the time of writing, CogniCity is the only fully developed and tested OSS platform that uses a GeoSocial Intelligence Approach to urban data collection, analysis, and sharing. Such a system has a wide variety of applications for civic co-management as a strategy for adaptation to climate change, not least because the OSS is designed from first principles to be transferable among various domains of application such as other weather-related hazards, waste management, or crime and other geographies and languages.
The coastal megacity of Jakarta experiences annual flooding during the seasonal monsoon which seriously affects human life, property, and urban infrastructure Hartono et al. The city is situated on a coastal deltaic plain served by 13 rivers which flow from the mountains in the south, northwards through the city to the Java sea [ Fig.
Over forty percent of the city is at or below sea level, necessitating a complex system of hydraulic and hydrological infrastructure to manage the movement of water through the city Hartono et al. According to the renown philosopher of technology Jean-Luc Nancy Cities are constantly evolving into more complex systems of interconnected infrastructure and people; as a consequence, many coastal cities become increasingly vulnerable to the impacts of severe weather events and global climate change Walsh et al.
The rise of social media platforms has taken city planners by surprise because these technologies not only provide unprecedented volumes of data relevant to the analysis of urban systems, but, more fundamentally, they change the way residents interact with each other and with the city at large Townsend As first adopters continue to expedite the uptake of social media in Asian megacities, it is imperative to understand how to best harness the power of networked communication technologies to overcome urban challenges that result from climate change.
Because geospatial information derived from social media is especially valuable and actionable within dense urban environments with a high proportion of networked users, Asian megacities facing the combined challenge of rapid development and climate change are critical sites for the development of new OSS that can turn the noise of social media into intelligent, actionable insights for users and governments alike.
Seasonal flooding has been a part of life in Jakarta since the seventeenth century. By , the Dutch colonial government finally took action and established the Burgelijke Openbare Werke, a task force responsible for flooding in the colonial city of Batavia. Following the major flood of , an integrated master plan was designed to prevent further flooding; the West Flood Canal was built under the supervision of Prof. Herman van Breen. Following these infrastructural measures to combat flooding, Soeharto then planned to widen the canal in , but the project failed to launch.
Instead, the central government and local governments built the Cengkareng Drainage System as a flood control network that was finished a decade later. Despite these efforts, the flood infrastructure was unable to protect Jakarta as major floods paralyzed the city in , , and The plan is to create an outer sea wall, the investment costs for which will be recovered by selling properties of a new waterfront city built on reclaimed land NCICD There are also criticisms regarding the possible damage to the local marine ecology and fisheries, as seen during the development of the Saemangeum Sea Wall in South Korea Lighthouse Foundation In addition, many in the public doubt the feasibility of the project as Public Private Partnership PPP because such schemes are relatively new in Indonesia, and because the country has seen numerous infrastructure failures due to poor project execution and corruption Noviansyah The project will displace over 34, families living along the river bank and widen Ciliwung river to almost double its natural width Rujak Center The measurement of sedimentation in surrounding channels and the Ciliwung River suggests that normalization could damage ecology and fisheries in ways similar to that of the Saemangeum Sea Wall in South Korea Smith As the population of Jakarta, and the surrounding area of Jabodetabek, continues to expand, the impacts of flooding are significantly heightened.
The flood forced 30, people to evacuate; the flood in inundated houses of , residents and claimed the lives of 80 people Taufik At present, Although Jakarta is the main urban area affected by flooding, the hydrological problem can be traced all the way to Bogor, 60 kilometers south of Jakarta. There are 13 major rivers running through Jakarta; most of these rivers originate in the mountainous areas of Bogor. Regrettably, the natural catchment areas of Bogor are decreasing due to weak land use policy enforcement.
For example, based on Regional Regulation No. The villas were forcefully demolished by the Bogor government with funding from the Jakarta government in , only to be rebuilt again two years later Rahmawati Examples like these show the need for an enduring commitment on the part of the governments of Bogor and Jakarta to manage the root causes of flooding upstream, where land use changes have serious effects on the ability of the downstream hydrological system to cope with the annual monsoon rains.
The history of investment in flood infrastructure in Jakarta tends to suggest that new infrastructure rarely guarantees a reasonable cost-benefit analysis; research also suggests that the government should commit to a thorough analysis of all existing facilities and assets to develop an operational overview, in addition to investing in new infrastructure and additional maintenance.
In the Regional Budget, the government allocated as little as 0. With such a modest investment in the maintenance of critical flood infrastructure, it is not surprising that before monsoon season of , for example, it was discovered that out of pumps in Jakarta were not operational Kompas While infrastructure asset analysis is a relatively novel science, it is nevertheless critical for the Jakarta government to devote resources to assessments and maintenance in order to determine the current state of flood control infrastructure and to make evidence-based, targeted investments in new facilities that can reduce risk in the weakest aspects of the current system.
Because infrastructure facilities do not exist individually and are highly interdependent Ebrahimy ; Tran et al. Decisions regarding critical flood infrastructure investment should be evidence-based and rely on an understanding of critical interdependencies, energy demands, and an integrated assessment of needs in all related sectors because infrastructure interdependencies bring with them layers of complexity, uncertainty, and risk to urban planning and design Tran et al.
From one administration to another, the most common approach toward flood infrastructure in Jakarta is to focus on new physical construction. Not only are these ventures capital intensive, but projects with large construction budgets are also prone to corruption Yanto, Currently, information and communication technologies ICT are a neglected dimension of flood management infrastructure.
With such a high rate of mobile phone penetration, and exceedingly high rates of social media usage, the Jakarta government is well-positioned to encourage greater public participation in infrastructure monitoring and flood event reporting; these elements of civic co-management could help alleviate much of the burden currently carried borne solely by the government.
However, such an approach requires a new vision of infrastructure that goes beyond canals and pumps, and begins to approach the question of flood management from a holistic view that includes ICT and public utilities for flood reporting.
As a compliment to increased ICT investment, open data allows universal participation where data is available as a whole and and at no more than a reasonable reproduction cost. By providing data under terms that permit reuse and redistribution, including intermixing with other datasets, citizen-users, NGOs, student and advocacy groups, and private developers can generate new tools for information management within the application economy. One excellent example of a government innovation to promote open data is the recently released SmartCity Jakarta website, with its applications Qlue public and Crop government use only.
Jakarta has one of the highest concentration of Twitter users worldwide Semiocast, , and experiences severe seasonal flooding Hartono et al.
The tacit knowledge of local communities, government agencies, and first responders in Jakarta, as well as the dense network of mobile sensors connected via social media, provides a data source of unprecedented resolution for mitigating urban risk.
In the context of DRM, the challenge for information and communication technologies is not to develop new sensors or additional applications for crowd-sourcing data collection, but instead to seed the evolution of social media networks as a mega-city methodology for resilience to extreme weather events and climate change. This section explores the development of PetaJakarta. Resident in East Jakarta using a mobile device during monsoon flooding; January Photography by Ariel Shepherd.
CogniCity is an open source framework for urban data, which harnesses the power of social media by gathering, sorting and displaying real-time situational reports from urgent infrastructure issues such as flooding. The CogniCity toolset, first trialed through the PetaJakarta. CogniCity builds on Geographical Information Systems GIS theory, to gather citizen reports from the social media network Twitter and create geospatial visualisations of this information [ Fig.
Reports are collected in a centralised geospatial database, and served via a data API to a client-side rendered map showing activity across the city in real-time, or as a data layer within external organisation geographical information systems [ Fig.
CogniCity was conceived in response to system testing during a flood event in Jakarta on 5 February , which captured , tweets in Jakarta related to flooding in a 24 hour period [ Table 01 ]. This test was conducted using a proof-of-concept, rapid prototype NodeJS application, connected to the public Twitter Stream application programming interface API , filtering tweets by location and keyword.
The keywords included: Furthermore the test revealed that a little over three percent of the captured tweets had precise coordinate-level geolocation metadata attached.
Table Results of System Testing for twenty-four hours from 5th February The findings from the system testing were subsequently verified by the award of a Twitter DataGrant, which made it possible to demonstrate both the spatial and temporal coverage of Twitter activity in relation to flooding in the city of Jakarta. It is interesting to note that Fig.
The geographical extents for PetaJakarta. Each of the key design requirements is discussed below. Data source: Twitter DataGrant. One of the challenges facing decision makers using citizen reports for DRM is the verification of submitted information, particularly if this information has been harvested from social media in a passive manner i.
Previously verification has been undertaken manually, a time consuming and labour intensive task. In situations where crowd-sourced reports are the primary source of information during a disaster, the classification of reports as verified or not is of critical importance to ensure that decision makers and the public are only interpreting data which is directly relevant to the situation.
Filtering approaches are often adapted to a DRM context from existing practices in information theory, where outlying data is removed as a matter of routine Medina To address these issues, previous DRM projects have used online communities to outsource human analysis of reports in order to classify their validity, and even rank their importance, forming online international communities of digital humanitarians Meier The large volume of data captured during the system testing, and corroborated by the Twitter DataGrant, necessitated that PetaJakarta.
Only in this way was it possible to provide a situational overview for decision makers and the public in a real-time manner. This approach builds on previous crowdsourcing efforts in response to natural disasters that relied on a people-centric approach to gather local knowledge, translate, and classify information Holderness ; Meier , tasks which are challenging to automate and computerize.
Through the PetaJakarta. Users could then see the results of their contributions, as well as those of other citizens, visualised on a map linked to their network Twitter , in the public domain and in real-time. The map used by both the public and government agencies thus created a two-way communication interface between users, PetaJakarta.
Importantly, users were offered the opportunity to report by text or media in an unrestricted manner within the confines of Twitter platform on the given situation, a method which has been historically proven as more effective at providing reliable data than crowd-sourced requests for highly structured reports Coen In this manner, PetaJakarta.
This process is evident in the typologies of reports generated by users, developed through a process of communicative evolution, which is further described in Section 3. Whilst existing infrastructure exists within the Twitter platform to disseminate specific messages to a targeted audience e. The design of the automated invitation and related outreach campaign are discussed in detail in Section 3.
The results and an evaluation of this process are discussed further in Section 5. The key requirement of the interface for the flood reports was the provision of data in a manner accessible to the project stakeholders: The interface needed to provide actionable information both for decision makers operating at the city scale e. Importantly, these outputs could not be divided into separate products; to allow for transparency between decision makers and users, both groups needed to reference the same map with the same data.
To overcome this challenge, the web-map at PetaJakarta. When viewed on a desktop computer, the web-application scaled the map to show a situational overview of the city. In this mode, CogniCity generates an on-the-fly thematic choropleth representation of the number of reports per administrative area.
In contrast, when viewed on a mobile device, the application scaled the web-map to show the reports at the neighbourhood scale over the past hour only [ Fig.
In effect, the goal was to distil the Tweet reports on a per-user basis so as to minimise the possibility of overwhelming the user with a larger number of reports while still providing relevant and actionable information. These groups operate at different spatial and temporal scales. However, in the interest of transparency, the same interface needed to be accessible by both user groups. Furthermore, the development of the map through a web-interface suitable for both desktop and mobile devices was key to ensuring the widest possible accessibility and reducing the adoption thresholds to PetaJakarta.
The effectiveness of a user-centric, multi-stable cartographic interface to the data is discussed further in Section 5. CogniCity operates a web-based geographic information system using a client-server model. A request is issued by the client device for relevant data, such as the number of tweets per municipal area for the past hour. This request is received by the CogniCity server module, computed in real-time, and returned to the client i.
Rendering of the data on the map interface is the completed on the client device. This structure of the IT architecture was adopted for two reasons; first, so that CogniCity can provide streams of geospatial data in real-time which can be used by any client, not just PetaJakarta.
Second, rendering data on the client device reduces the computational overhead of server processes, and negates the need for the server to generate image based map tiles, which would be computationally-intensive to achieve in near-real time. In this configuration, CogniCity becomes extremely scaleable and affords the opportunity to use cloud services to perform load balancing and on-the-fly server creation to help distribute the load of large numbers of client requests.
The API provides Uniform Resource Locator end-points to current and archived locations of flood reports, aggregate counts of activity over time, and hydrological infrastructure layers.
As such, data stored within CogniCity for the PetaJakarta. However, in relation to PetaJakarta.
The connection to this API was perfomed using the credentials of the verified petajkt Twitter account, with support from Twitter, who removed the limits on the number of tweets which could be sent from the account to enable the automated delivery of high-volumes of invitation messages.
In reviewing the proposed design following the system testing in January , user anonymity in the reporting process was embedded within CogniCity and PetaJakarta. Whilst the data produced by Twitter reports of flooding is in the public domain albeit with the restriction of registering as a Twitter user , the objective of PetaJakarta. For these reasons, CogniCity was designed, as part of its original specification, to anonymise reports collected by separating reports from their respective users.
Furthermore, the text content of tweets is only stored when the report is confirmed, that is, when the user has opted to send a message to the petajkt account to describe their situation.
Similarly, when usernames are stored, they are encrypted using a one-way hash function. The open source nature of CogniCity is a key design factor in this respect.
Publicly sharing the source code means that anyone can examine the software to see how unconfirmed reports are discarded and how the one-way encryption process of usernames operates. CogniCity is organised into four components [ Fig. The final component is the CogniCity database, which underpins the entire CogniCity software stack. The database runs on the open source PostgreSQL object-relational database management system and uses the PostGIS extension to support geospatial data, including the locations of Tweet reports.
The following section describes each of the modules and their technical specifications. Following this, Section 2. The CogniCity database is comprised of 14 database tables [summarised in Appendix II ], populated by the flood reports created from the tweets received by the CogniCity Reports Module [ Fig.
The entry of data into the database by the Reports module, and retrieval of information by the Server module, is discussed below in Sections 2. The module collects relevant tweets, adding them to the database as flood reports, and sends tweets to users inviting them to confirm the current flood condition. However, as these reports are unconfirmed e. Upon receipt of an unconfirmed report, the Reports module programmatically sends the user a Tweet in reply, inviting them to confirm the situation.
This ensures that a user never receives more than one programmatically-generated invitation Tweet; before each invitation is sent, the username is compared against the existing list in the database. The invitation message aims to inform the user about the project and provide instructions on how to submit a confirmed report.
The invitation Tweet is limited to characters to allow for Twitter usernames in the reply; however, by using Twitter Cards, the automated Tweet could also contain a short embedded video which explained the project and how to participate.
The design of the video is discussed further in Section 3. Invitation Tweets were either sent in English or Bahasa Indonesian depending on the language as defined in the metadata of the tweet unconfirmed report received by CogniCity:. Upon receipt of a confirmed report as shown in Fig. In addition to confirmed and unconfirmed reports, two other conditions may arise on receipt of tweets by the Reports module. If a confirmed report i. Upon receipt of such a Tweet, CogniCity programmatically sends the user a reminder to enable geolocation and try submitting the report again [ Fig.
Unlike unconfirmed Tweets, no username check is carried out prior to sending a geolocation reminder, as the user has opted to include the PetaJakarta.
This strategy was designed so as to not limit the number of reminders sent to help increase the use of geolocation, and as such the number of confirmed reports received. If present, the user receives a programmatic invite as per an unconfirmed invitation; if not, then the Tweet is disregarded by CogniCity. As described in Section 2. At maximum throughput, CogniCity is capable of handling up to tweets per second, with the complete processing of each tweet including classification, writing to the database, and sending of automated replies taking 30 milliseconds, although these values are subject to the platform on which CogniCity is deployed operationally.
During operation, errors such as disconnections from the PowerTrack API or the database server are logged and the module attempts to reconnect at specified intervals. Within a five minute window, any missing data is recovered using the PowerTrack backfill functionality, and loss of connection to the CogniCity database results in internal caching in CogniCity Reports until the database connection is restored.
Any exceptions which go unhandled result in a notification Tweet being sent to the CogniCity system administrators. The server module also serves the static components of the client interface i. The reports endpoint provides real-time i.
The aggregates endpoint provides real-time counts of the sum of confirmed and unconfirmed reports from the past one, three, six or 24 hour periods at three different municipal scales. The municipal scale is specified by the client when requesting data and includes the city scale i. Lastly, the infrastructure endpoint provides access to data representing the location of waterways, pumps, and floodgates in Jakarta, used by PetaJakarta.
CogniCity serves the endpoints under the domain of the web interface i. For example, to access counts of reports from the last hour, the client makes a request to the following URL https: Apart from the report-count and report-time series endpoints, all endpoints provide data in the GeoJSON format by default.
This provides the data in a format which is readable by the PetaJakarta. TopoJSON reduces data volume by employing a topological representation to minimise the number of geometric vertices and edges which are required to represent vector geometries. To further improve responsiveness of the map when loading data, each of the endpoint requests is cached in computer memory by the CogniCity server application for sixty seconds.
This ensures that users are provided with data in real-time, which is no more than sixty seconds old, but reduces the number of individual database queries executed if a large number of users logon to PetaJakarta. This architecture is particularly relevant for the aggregates layers, which are computed on-the-fly after a request to an aggregates endpoint is received in order to reduce database volume.
Whilst this creates increased workload for the database, it reduces database size, removes the need for periodic calculations of aggregates, and ensures that the aggregates data is real-time.
The spatial computation of how many tweets are within a specific municipal district at a given time is achieved using a spatial SQL query embedded within the server module.
As such, the PetaJakarta. The interactive map at PetaJakarta. Users are divided into two groups: For mobile users, confirmed and unconfirmed reports of flooding were added to the map in point form. This cartographic symbology simplified the interface on small screens and reduced the volume of data required when loading the map. Additionally, mobile users only saw the reports from within the last hour, whereas desktop users had the option of seeing aggregate data from the past one, three, or six hours.
Mobile device users were asked to share their location with PetaJakarta. Clicking on a confirmed report allowed the user to see the contents of the original tweet, and any included media are hyperlinked back to the Twitter platform.
The text from unconfirmed reports is not retained by CogniCity see Section 2. If geolocation was not available, or the user was outside of Jakarta, then the map rendered a view of reports at the city scale as shown in Fig. Additionally, to help with user navigation of the data, the map also included a reports tool which shows a listing of confirmed reports ordered by the most recent first. Referring to Fig. Rendering of all data is carried out by the Leaflet library on the client.
This enables the user to pan and zoom around the city to see reports beyond their immediate vicinity. Additionally, asynchronous loading of data from the CogniCity API means that the map is interactive before all the data have completed loading. As a result of these intergrated components, PetaJakarta. Visualisation of the PetaJakarta. Desktop users viewed the same map, but instead of point representations of reports, users saw aggregate counts of reports in municipal areas across the entire city.
This thematic choropleth symbology showed variations in intensity of twitter activity across the city. Primarily aimed at decision-makers such as BPBD DKI Jakarta, the aggregate interface also allowed users to drill down through the different spatial scales of municipal boundaries, with each layer showing the count of tweets at a finer grain resolution, until reaching street level where individual reports were displayed in a manner similar to the mobile user interface [ Fig.
In this way, starting at the city-scale overview, a user could quickly identified hot-spots of activity with high numbers of reports. Importantly, the desktop user could view the same data as the mobile user, but data were represented first in aggregate form to support decision-making at greater spatial and temporal scales than required by the general public.
The only discrepancy between the two interfaces is that the bounding box of tweet capture in the CogniCity Reports module [see Fig. However, both interfaces also include overlays of rivers, pumps and floodgates in Jakarta. It is anticipated that in the future this network data could be supplemented, for example, to tie reports of flooding to failures of specific infrastructure assets, thereby further enhancing the information available for decision-makers.
The PetaJakarta. As shown in Fig. Appendix IV contains further details of the metadata and links to their records. In summary, through the PetaJakarta. Using the NodeJS framework on top of a PostgreSQL database, it was possible to build a rapid prototype of a scalable geographic information system capable of capturing Twitter data and serving them through a web-map interface as confirmed and unconfirmed flood reports in real-time.
NodeJS, combined with a scaleable cloud deployment proved to be a robust solution for such a system, capable of handing more than 3, user requests on PetaJakarta. The following section provides a summary of the design parameters for the development of PetaJakarta.
In developing the PetaJakarta. This concern is directly related to the open source and open data ethos of the project; moreover, because the platform relied on public adoption, it needed to not only operate as a transparent, community-centric information sharing platform, but had to also feel like a community platform [Figs. The design of community inclusion can be summarized effectively by three key objectives: This branding strategy towards community inclusion can also be traced through individual design decisions, including the use of language, color, graphics, and the map interface itself.
Aiming to appeal to first adopters—the young, tech-savvy Twitter-public of Jakarta—the language used in all the outreach materials Twitter replies, the outreach video, graphics, and print advertisements was intentionally casual and concise. Because of the repeated recurrence of flood events during the monsoon, and the continuation of daily activities around and through these flood events, the messages were intentionally designed to be more like normal twitter chatter and less like public service announcements.
Geolocation pins with speech bubbles were used in advertisements to indicate the connection between the map and community expression of concern. The graphic style and color choices were similarly light and youthful, connecting visually to sites used as part of everyday life rather than as emergency response.
A clean, black and white map background with brightly colored report pins was selected as the map interface so that its legibility was optimized on all screen resolutions. Although much of the public engagement with the project occurs through the Twitter platform, the PetaJakarta. As explained in Section 2. From the perspective of the citizen-user, the emphasis was to provide easy access to the map and direct information on how to engage with the project.
Thus, the public engagement with the project is divided into two actions: The default language of the website is Bahasa Indonesian, allow a main navigation tab allows users to switch the platform to English. Jakarta has one of the highest number of Twitter users of any city in the world Semiocast Because of this high adoption rate, the platform was selected as the primary method of public engagement for the project.
Twitter allows users to passively listen to other users, directly engage with single users, and broadcast to a set of self-selected users followers. Twitter also enables additional functionality for public address, including Twitter Cards programmatically attaching predetermined media based on terms included in a tweet and Twitter Video Cards programmatically attaching predetermined video based on terms included in a tweet. Promoted Tweets can also be used to download space on users feeds, although this feature was not used by PetaJakarta.
Public participation is critical to the project. Because of this, developing strategies for capture public attention on Twitter at the time of impact was important. Second, because PetaJakarta is not a passive social-media scraping system, the public was asked to take action in order to participate. This call to action had to be made clear, yet appear unintrusive and incentivized. In response to these challenges, a series of low-contact strategies were used solicit public participation in the project.
The Outreach Video Video 01 was the main method of public promotion. It is a simple, concise, one-minute video that describes the user experience of PetaJakarta.
The video focuses on the call to action: Since the video was accessed through Twitter, and potentially through the Twitter app, it was designed for maximum legibility on a small mobile screen and in noisy urban conditions. The graphics use flat, bright colors on a white background; there is no background music under the calm, direct spoken narrative.
It was important to design the user interaction with PetaJakarta. With this aim in mind, the graphics and language are casual and light in tone. In the video, auto-replies, and print advertisements, PetaJakarta. That is, the communication strategy is not to insist that users should participate, but instead offer users the opportunity to share information through a non-moralizing, opt-in approach. During monsoon season, Jakarta residents participated in sharing real-time information about flooding in the city via petajkt Twitter account, which was co-operated by the CogniCity Reports module and a human project coordinator.
Users immediately took petajkt seriously even before the account was Verified by Twitter and started reporting about flood conditions around their homes, offices, and in traffic situations.
The account gained significant number of impressions during flood events. In fact, it is clear when the flood events occurred just from viewing at the analytics bar of petajkt Twitter account.
Below is a summary of the tweet activity of petajkt in the day period from 2 December public launch of PetaJakarta. The first event is 27 December 27 , which marks the first major flood in Jakarta during monsoon season [ Fig. There were , organic impressions on that day. The third event is 9 February , which marked the peak of the flood events during monsoon season [ Fig. Flood events continued for several days and there was still a high number of impressions: There are several important considerations when evaluating the tweets received by petajkt during the monsoon.
First, many users sent tweets without geo-location information on during flood events. However, these tweets nevertheless had valuable information about flood situations and there was location information in the the body of their tweets. Typically, tweets with valuable information such as these were manually re-tweeted because sharing this detailed, time-critical information was deemed valuable to other users, although it lacked precise geo-location data.
Additionally, CogniCity would issue a tweet to the user which used a Twitter Media Card to include a picture explaining how to activate the geo-location function for the next report. As noted in 2. As a result of these programmatic invitations, petajkt hit the Twitter rate limit on several occasions.
The first time was on 27 December , during a period of heavy precipitation; the second occurrence was on 23 January Consequently, on both occasions the petajkt account needed to wait for one hour to be able to send addition tweets or re-tweets. However, once petajkt was granted the status of a Verified Twitter account, this problem did not occur again.
There were various types of flood reports addressed to petajkt; in what follows, the three types of tweets received by petajkt are classified, and this classification is clarified with example tweets. Flood reports came in several forms. First, flood reports with photos and detailed information, such as the name of the street, village, RT, RW, and the height of the flood, were received [ Fig. In this type of report, flood height information was provided in meters, centimeters, or by the reference to the human body, such knee high, chest high, etc.
The petajkt account also received flood reports with photos and information, such as the name of the street, village, RT, RW, and the height of the flood, as well as additional requests for needed evacuation supplies, such as portable pumps, boats, or food supplies [ Fig.
Additionally, the petajkt account received flood reports without photos, but with useful information such as the name of the street, village, RT, RW, and the height of the flood [Figs. The petajkt account also received flood reports from outside the bounding box of Jakarta [Figs. The petajkt account also received requests for help and evacuation support. These messages sometimes originated from government agencies and were addressed to affected residents.
Usually, Dinas Sosial DKI Social Services Agency tweeted petajkt to share what they were doing to help flood victims, such as delivering food, building common kitchens, etc. The petajkt account also received from organized communities to residents [Figs. Finally, the petajkt account also helped to relay peer to peer information being shared public on Twitter platform by re-broadcasting relevant information once it was confirmed [Figs.
Users also sent reviews and feedback regarding petajkt and PetaJakarta. The most frequent question directed to petajkt on Twitter was about how to activate the geo-location function for tweets. So far, this question has been addressed manually by sending a reply tweet with a graphic instruction describing how to activate geo-location functionality.
Another question that is frequently asked, often outside the Twitter platform, is about information accuracy and validation.
Although ensuring the information sent by Twitter users is challenging, there a number of ways PetaJakarta. First, during a flood event, users continuously report areas that are flooded. In one scroll of the mention tab, the PetaJakarta. This allows PetaJakarta. Second, PetaJakarta. By monitoring these accounts, additional cross-checking of crowdsourced petajkt reports is possible.
Third, PetaJakarta. The final aspect for verifying reports is the recognition of active users that frequently tweet to petajkt with reliable information; as PetaJakarta.
An additional remark on verification is critical. According to Harlan Hale of USAID Hale , the most critical resource in disaster scenarios is information; therefore, it is important to encourage users to share flood data that is relevant, well-formatted, and accurate. In the context of social media, this can occur through the mechanisms described above; however, there is also a naturally-occurring filter of self-selection. That is, users who do not have valuable information, or who are not willing to make a detailed report albeit one not exceeding characters , are disinclined to send replies.
In this way, PetaJakarta. Importantly, the overall aim of sending programmatic messages is not to simply solicit a high volume of replies, but to reach active, committed citizen-users willing to participate in civic co-management by sharing nontrivial data that can benefit other users and government agencies in decision-making during disaster scenarios.
The most frequent complaint to the petajkt account was regarding the programmatic reply feature. Many replies to petajkt about the programmatic replies used humor or ironic criticism to explain that they were not, in fact, in the vicinity of a flood event. However, petajkt also received a number of positive responses and messages of gratitude from users who were not affected, but who still saw the value of the programmatic messaging. The programmatic reply sent by CogniCity helpd to awareness about the existence of PetaJakarta.
Many people discovered PetaJakarta. Importantly, the programmatic replies also allowed PetaJakarta. During the monsoon season, the petajkt sent approximately 90, programmatic replies. Unfortunately, there were also several negative aspects that resulted from using the programmatic reply functionality. Just five years later, Fatahillah , a Demak general, attacked and conquered the area and drove out the Portuguese, renaming the region Jayakarta, which became part of the Sultanate of Banten.
Dutch ships began arriving in Jayakarta in , and in , the English East India Company made its first voyage and built a trading post, which became the center of English trade in the country until the late 17th century.
As relationships between Prince Jayawikarta and the Dutch soured, his soldiers and the English attacked the Dutch fortress and were defeated by the Dutch, who burned the English fort in return. This victory allowed the Dutch to consolidate power and rename the city Batavia in Growing opportunities in the new capital of the Dutch colony attracted Chinese and Indonesian immigrants.
By , the city of Batavia had a population of more than , Jakarta Population Growth With only legal residents counted, the population of Jakarta doubled from 4. The census found that all areas within the DKI Jakarta had a positive growth rate in the last decade, with the slowest growth in Central Jakarta.
Jakarta is now becoming starved for resources by its ever-growing population. Jakarta was designed to handle , people when founded by the Dutch, although it is now home to up to 12 million people during the work week, with , new residents coming to the Jabodetabek region each year.