Measuring Disaster Recovery: Lessons Learned from Early Recovery in Post-Tsunami Area of Aceh, Indonesia

Abstract

The assessment of post-disaster recovery is often hindered by limited metric and longitudinal data, in addition to the dynamic and long-term processes. Therefore, this study aimed to investigate the early stages after the 2004 Indian Ocean tsunami in Aceh, Indonesia, using the Disaster Recovery Index (DRI). The two initial waves of Study of Tsunami and Aftermath Recovery (STAR) data were used to track the recovery process from 5 to 19 months after the tsunami. The results showed various recovery patterns in three affected areas and five sectors. Furthermore, recovery rates in the medium and heavily damaged areas increased by 2.05 and 7.45 percentage points, respectively, with a 0.33 percentage point decrease in the lightly damaged areas. The social and livelihood sectors showed rapid progress, supported by the establishment of temporary health and education facilities, including Cash-for-Work programs. Meanwhile, other sectors experienced slower recovery due to their complexity. The application of the DRI successfully showed the relative positions across affected areas and sectors over time in a simple way. This confirmed the variety of recoveries in subgroups in the community and suggested the importance of regularly measuring progress using standard metrics to observe long-term conditions.

1. Introduction

A post-disaster recovery effort is a complex, multidimensional, and nonlinear process, incorporating the physical, built, and human environment systems [1,2]. Compared to a linear trend, a post-disaster recovery curve follows an S-shape, with response, reconstruction, and other processes creating path interdependencies that are not strictly chronological [3].

All processes start at the same time after the disaster, but the time of recovery varies significantly based on various conditions [3,4]. This variation is attributed to complexities such as physical and mental health problems, survivors losing the ability to resume pre-disaster work, land ownership conflicts, etc. The duration is dependent on the scale of the disaster and the needs of the survivors [5].

Previous observations showed that by improving communication and transportation, the population can regain pre-disaster levels in the space of 20 years [6] or even 10 years [1]. Studies in several post-disaster areas also found that for 3 to 4 years, economic recovery is characterized by a temporary boost in reconstruction activities, followed by housing settlement [1]. Moreover, 5 years after a disaster, recovery efforts mainly focus on addressing damages in the aspects of housing, livelihood, and health management [7].

Measurements of progress during this period require periodic assessment to assist in making appropriate and effective policy decisions. However, there are limited frameworks and no standard metrics for comparing the recovery outcomes of various disasters across areas over time [1,8]. The difficulties in developing disaster recovery metrics include the absence of publicly available data, particularly in longitudinal settings [9].

Several studies have attempted to develop metrics for measuring disaster recovery. A related review summarized 19 indicators at the community level under four main categories: economic, environmental, infrastructural, and social [2]. Another study in the United States developed 79 recovery metrics at the community level, which were validated through a recovery plan review [8]. These metrics were further used in a case study, suggesting the importance of constructing indicators for individual level analysis to identify vulnerable groups and accommodate their needs [9]. Despite the difficulties in data availability across disaster areas, the metrics are useful for showing recovery characteristics, including problems, and for improving resilience through proactive planning.

A recent study attempted to measure recovery at the individual level using a comparison of perceptions across different degrees of damage in affected areas and at different time points [10]. The measurement successfully identified the stage in the individual recovery process. However, a distortion was found in the long term in longitudinal settings due to the application of individual perception. Despite the numerous studies, there is still a need for post-disaster recovery metrics that are relatively easy to apply.

This study applied a composite index, namely, the Disaster Recovery Index (DRI), to post-disaster longitudinal data to measure recovery progress over time. This index was initially used by several organizations for measuring the recovery and resilience of communities affected by the 2010 Merapi volcanic eruption, including lava floods in Yogyakarta and Central Java, Indonesia. Furthermore, it was constructed in collaboration between the Indonesian National Disaster Management Agency (Badan Nasional Penanggulangan Bencana, BNPB) and the disaster risk reduction forum of Yogyakarta and Central Java, with support from the United Nations Development Programme (UNDP). A previous investigation compared the pre- and post-disaster index values using one-time survey data, while pre-disaster data were obtained through retrospective questions. This study aimed to apply the composite index to longitudinal data, providing a simple method for plotting comparative recovery levels over time and across areas. Consequently, the results can be used by practitioners to monitor recovery progress and communicate with other stakeholders.

The composite index is commonly used as a quantifiable metric in post-disaster studies, specifically for resilience measurement. Furthermore, it is compiled into one index from a single or particular indicator based on an underlying theoretical framework [11]. The index also generates aggregated measurements from individual variables and shows the relative positions of measured phenomena, illustrating the magnitude and the direction of change evaluated over time [12]. Aggregation through a composite index is a significantly simple and relatively easy method of comparison while maintaining the detail of some subgroups.

In this study, the DRI was applied to the early recovery period after the 2004 Aceh Indian Ocean tsunami. Furthermore, Study of Tsunami and Aftermath Recovery (STAR) data were used to provide a longitudinal representation of populations affected by the earthquake and tsunami along the coast of Aceh and North Sumatra. Located near the earthquake epicenter, Aceh was predominantly damaged by the tsunami and had previously been shattered by separatist conflict lasting 30 years.

Prolonged conflict had a negative impact on the economy, making Aceh one of the poorest provinces in Indonesia [13]. According to a United Nations report, over 90% of households in the region lived below the poverty line in 2002 [14]. After the tsunami, survivors were evacuated and taken to temporary homes and shelters. The already significant number of poor people in the region before the tsunami was quickly multiplied after its occurrence [15]. This disaster concluded the conflict and brought a large amount of recovery assistance to provide an opportunity for Aceh’s development. The huge scale of disaster and recovery resources raised curiosity on how the recovery would progress over time. Therefore, understanding this process is essential to gain insight into both the practices and the limitations that must be addressed to enhance resilience toward future disasters.

2. Materials and Methods

2.1. Data

The collection of satisfactory data in post-disaster conditions is significantly complex [16], requiring huge sums of money and the tracking of displaced survivors. STAR was designed to track the immediate and longer-term consequences of the 2004 Indian Ocean tsunami, as well as recovery efforts. Approximately 15 coastal districts (kabupaten) in Aceh and parts of North Sumatra with more than 33,000 inhabitants were selected as the baseline sample [17].

The STAR sample was obtained from the 2004 National Socio-Economic Survey (SUSENAS), a pooled cross-sectional dataset provided by Statistics Indonesia. Several variables were incomparable with STAR, as the SUSENAS 2004 was not specifically prepared to address the aftermath of the disaster. STAR data are outdated and do not include pre-disaster conditions. However, they were used as a case study due to the scarcity of longitudinal data designed for post-disaster conditions in developing countries.

STAR measures the degree of damage, including light, medium, and heavy, using several methods. Although STAR does not provide a detailed map showing the districts for each damage level, it still enables a comparison of recovery progress among the three areas. The comparative result is advantageous, addressing a gap in previous studies, which often neglected the potential impact of different recoveries in communities after the same disaster.

According to www.stardata.org, six waves of the STAR survey were conducted between 2005 and 2015. However, from 2019 to 2022, only the first three waves were published consecutively. This study used the two initial waves, since most of the variables needed to analyze the DRI are based on existing literature. STAR1 was conducted in May 2005, 5 months after the tsunami, while STAR2 was carried out in July 2006, 19 months post-disaster. Due to the unavailability of some variables in STAR3, further adjustments were required for expanded analysis.

2.2. Constructing the Index

In the initial DRI analysis after the Merapi volcanic eruption, 22 indicators were synthesized to assess community recovery by restoring five sectors, namely, housing, infrastructure, livelihood, social structure, and others [18,19]. The composite DRI is composed of five sector recovery indexes, with each index having a set of indicators and criteria. In post-disaster recovery and resilience study, the indicators are categorized into several themes, sectors, or focus areas, ensuring effectiveness for further analysis [8]; the DRI framework is shown in Figure 1.

The DRI framework commences by determining recovery sectors and indicators, each assigned a weight representing its priority in terms of need. Furthermore, the DRI has two weighting formulas: (1) the indicator weight for each sector is 100%, or (2) the indicator weight for all sectors is 100%, which is distributed proportionally. The indicators and weight are adjusted based on the disaster characteristics and recovery needs.

The DRI assigns a binary value (score of 0 or 1) for each indicator, with ‘Score 1’ representing a recovered indicator when the criteria are met. Furthermore, the percentage value of the recovered indicator is calculated based on the total sample.

$$Percentage value=\frac{recovered indicator}{total sample}\times 100$$

(1)

The percentage value is needed to create a standardized and comparable scale between the indicators. The standardized form is crucial due to the diverse original measurement units of the indicators used [12]. Subsequently, the weighted sector indicator is calculated by multiplying the percentage value by the weight. The total of the weighted sector indicators is the sector DRI.

$$Weighted sector indicator=percentage value\times sector weight$$

(2)

$$Sector DRI=\sum weighted sector indicator$$

(3)

The composite DRI is an aggregation of all sector indicators, obtained by summing weighted all sector indicators.

$$Weighted all sectors indicator=percentage value\times all sectors weight$$

(4)

$$Composite DRI=\sum weighted all sectors indicator$$

(5)

2.3. Indicators and Measurement

A total of three primary criteria were used for selecting community recovery indicators [8]. These included the ability to measure and assess the indicator repeatedly over time, capturing changes in recovery status and among population groups in terms of both geographical and demographic characteristics. Finally, indicators must accommodate both community and individual needs.

Identifying relevant, robust, and representative variables is crucial, as the strengths and weaknesses of the composite index are based on the qualities of the selected variables [12]. Therefore, there is a need to adjust and revalidate indicators when used in other areas or hazard contexts to ensure accurate measurement based on the intended concept [11].

The initial DRI analysis consisted of 5 sectors, 22 indicators, and their weights. The weights of sectors and indicators reflect the priority settings in disaster recovery efforts. Moreover, the determination of weights was made based on the assessments of disaster experts and policymakers included in the formulation of the DRI through prioritization ranking adopting the Analytical Hierarchy Process (AHP) method [18].

In this study, five sectors were maintained, and one was modified in line with the national guideline for post-disaster needs assessment provided by BNPB. This regulation includes a range of housing, infrastructure, livelihood, social, and environmental needs. Furthermore, the fifth sector was modified from “other sectors” in the previous analysis to “environment sector” to adjust to the national guideline and maintain the same weight proportion.

As shown in

Table 1. Sector, Indicator, Weight, and Recovery Criteria.

Sector/Indicator	Weight	Recovery Criteria (Recovery = 1)	Types of Data
Housing	25.83
Wall	23	Brick, wood	Categorical: brick, wood, bamboo, sago palm, tent/tarp/asbestos, other
Floor	28	Not dirt	Binary: dirt, not dirt
Toilet facility	26	Toilet facility (private, shared, public) with septic tank as the final sewage disposal	Categorical: private, shared, public, no facilities, other Categorical: septic tank, pond/rice field, river/lake/ocean, hole, shore/open field, other
Source of drinking water	23	Bottled water, tap water, pump, protected well/spring, or drinking water from aid	Categorical: bottled water, tap water, protected well, unprotected well, protected spring, unprotected spring, river, rainwater, drinking water from aid, other
Infrastructure	18.33
Road surface	32	Asphalt, cement/paving stone, or solid/pebble	Categorical: asphalt, cement/paving stone, pebble/solid, wood/bamboo, soil/sand, other
Public transportation access	23	<Average distance	Continuous
Pubic telephone access	21	<Average distance	Continuous
Traditional market access	24	<Average distance	Continuous
Livelihoods	25.83
Household income (age 15–64 years)	50	Same, increase, or decrease of <20% of income compared to the previous year (calculated based on pre- and post-disaster income)	Continuous (income before and after tsunami)
Labor force participation (age 15–64 years)	50	Worked in the past week	Binary: yes, no
Social	15.83
Access to the nearest health facility	22	<Average distance	Continuous
Self-assessed health (15 years and older)	38	Same or better health condition	Categorical: very good, good, fair, bad, very bad
Access to the nearest education facility	28	<Average distance	Continuous
School participation (age 6–13 years) after tsunami	12	Attending school	Binary: yes, no
Environment	14.18
Planting area	50	Constant or increase in planting area (calculated based on sq meters of planting area compared to the previous year)	Continuous
Productivity	50	Constant or increase in productivity (calculated based on sq meters of cropping area compared to the previous year)	Continuous

, several indicators and weights were specified for each sector, including indicators used in other studies [2]. The sector weighting was adopted from a previous study [18] due to its relevance to the priority setting in an earthquake and tsunami recovery case. Both the housing and livelihood sectors were considered important to the recovery, each carrying a weight of 25.83%. Subsequently, infrastructure recovery had a weight of 18.33%, followed by social at 15.83% and, finally, by the environment, with a value of 14.18%. We also provide descriptive statistics in the Appendix A to give clearer information about the sample.

Several indicators were modified from 22 variables in the previous report to 16 in this study due to data availability. Although the adjustment process may be subjective, priority setting in the initial DRI was maintained by distributing the weights of unavailable indicators evenly to those in the same sector.

In the housing sector, the recovery indicators included wall, floor, toilet facilities, and source of drinking water. The inclusion criteria that should be met were walls made of brick or wood, a non-dirt floor, a toilet with a septic tank for final disposal of sewage, and a safe drinking water source, including bottled water, a tap, a pump, or a protected well/spring.

In addition to these criteria, recovered housing means permanent housing. Apart from privately owned houses, houses with other ownership status were considered permanent housing when respondents were residing in the same place as during the tsunami. Therefore, any temporary housing associated with disaster emergency response, namely, camps, barracks, tents, or dormitories, was categorized as not recovered.

Infrastructure recovery was also shown by the type of road, including access to public transportation, a telephone, and traditional markets. The criteria that were identified for roads included asphalt, cement/paving stone, or solid/pebble, while distance was used to represent accessibility for the other indicators. Generally, recovery is determined by a shorter distance compared to the average. In a previous DRI study, bridge criteria were used as an indicator, but these data were not available in STAR, leading to their removal from the analysis and weight redistribution to other indicators.

Livelihood sector recovery was shown by the income and labor force participation of people aged 15–64 years. Income is considered to have recovered when it has increased, remained the same, or decreased by less than 20% compared to that in the previous year. Labor force participation recovery was assessed based on working status of the respondents aged 15–64 at the time of survey. It was recovered if respondent’s status was worked.

Social recovery has four indicators, namely, access to health facilities, access to education facilities, health status, and school participation. The recovery access criteria are the same as for the infrastructure sector, requiring a shorter distance than the average.

The health status recovery indicator used in this study included a self-assessment of health condition, both physical and mental. This indicator was considered recovered when the answer was fair, good, or very good compared to the previous year. Based on self-assessment responses, respondents who answered the question by themselves were selected, while proxy responses were excluded. As there was only one question on health condition, the weight of the two initial indicators was summed. Additionally, school participation was considered recovered based on the attendance of school-age children (age 6–13 years).

In the environment sector, a significant difference was observed compared to the previous study. The two indicators included were the changes in planting area and crop productivity from the pre-tsunami state, with weights in equal proportion in this sector. Furthermore, crop productivity was measured as the ratio of cropping output to the total area. The recovery criterion was a constant or increased planting area and crop productivity.

3. Results

Figure 2 shows the composite DRI values for STAR1 and STAR2, representing recovery 5 and 19 months after the tsunami, respectively. Each affected area has a different recovery progress trend.

This observation showed that the recovery level in the lightly damaged areas decreased by 0.33 percentage points, from 64.86% to 64.53%. However, both medium and heavily damaged areas had increasing levels with different progress. The medium-damaged areas’ recovery level increased by 2.05 percentage points from 64.75% to 66.80%, while that for heavily damaged areas increased by 7.45 percentage points from 55.78% to 63.23%.

The sector DRI values, showing the five sectors’ recovery levels in STAR1 and STAR2 for both damage degrees in the cluster and an aggregate of all areas, is shown in Figure 3. Based on this observation, recovery progress from STAR1 to STAR2 in the aggregate, lightly damaged, and medium-damaged areas showed a pretty similar pattern. For example, the social sector reached the highest level, with a percentage of 80%, followed by housing, reaching 60–70%. Both infrastructure and livelihood reached values of approximately 50–60%, while the environment had the lowest value, with 40–50%.

Heavily damaged areas showed a different pattern. Only the social and livelihood sectors had the same percentage as in other areas, while the remaining sectors showed lower percentages with consistent positive progress from STAR1 to STAR2.

4. Discussion

By measuring disaster recovery efforts at 5 and 19 months after a tsunami using the DRI, we successfully plotted the relative positions of recovery progress across affected areas and sectors over time in a simple approach. In the context of Aceh, the three affected areas showed different trends of recovery. The results showed that medium and heavily damaged areas showed positive progress with different speeds, while lightly damaged areas demonstrated a slight decline in process. Constant or non-positive progress was observed to be normal in early recovery, as depicted by the S-shaped recovery curve [3], with easy navigation of various trends.

In 2005 and 2006, post-disaster recovery in Aceh was dominated by emergency responses [20]. Therefore, recovery efforts focused on distributing basic needs and providing access to deliver assistance to affected areas. Within about a year, all areas, including the lightly damaged areas that were only mildly or not affected, received assistance as part of the emergency response, while assistance to heavily damaged areas was delayed by the difficulty of access. Several months after the tsunami, many areas on the west coast of Aceh could only be accessed by boat, particularly in areas intersected by large rivers [21].

Assistance to lightly damaged areas dissipated in the subsequent year, while the medium and heavily damaged areas became the main focus of the recovery effort. This phenomenon made heavily damaged areas experience the most significant progress, despite having the lowest recovery level. Furthermore, the progress in medium and lightly damaged areas was delayed due to the need for advanced recovery stages. Comparative DRI values amongst the three affected areas confirmed that communities recovered differently from the same disaster, as shown by a previous study [4].

Among the five sectors of recovery, the social aspect was the most prominent due to the highest achievement. However, it showed a declining trend from STAR1 to STAR2, as supported by temporary health and education facilities. This is consistent with a previous study, where several survivors still received services from temporary health clinics for two years after a tsunami [22].

The livelihood sector also received massive temporary programs, showing a positive change through the support of cash-transfer programs. In Aceh, after the Indian Ocean tsunami, various cash-transfer programs were provided in collaboration with the government and non-governmental organizations (NGOs) [23]. Among these programs, Cash-for-Work (CFW) was the most popular, generating jobs and income for affected people. The CFW program was commonly selected to provide cash to people as compensation for their work on clean-up and reconstruction projects in the early recovery stage [24,25]. Approximately 93% of household incomes were attributed to the CFW program several months after the tsunami [24]. However, the positive trend in per capita income was driven by temporary reconstruction activities, which may disappear within a few years [1].

Housing and infrastructure sectors were important priorities in the post-disaster recovery effort. However, rapid progress in reconstruction promotes further analysis, particularly of its connection to the CFW program. In the aggregate area, the housing sector was approximately 60% complete. This result is consistent with another report, where the reconstruction program of housing aid by Oxfam was 63% complete at the conclusion of 2006, and houses continued to be built until 2009 [26].

A rapid increase in housing recovery was observed in heavily damaged areas. Many houses were rebuilt on the West Coast, and despite having no tenants [21], some failed to meet beneficiary satisfaction due to their low quality [26]. This condition was caused by the participation of many funding organizations with limited experience in housing construction as contractors, along with using the number of houses built as a success indicator, instead of safety, security, and livelihood [27,28]. Furthermore, the unskilled workforce learning construction under the CFW program contributed to the low quality, leading to missed deadlines for housing and infrastructure reconstruction [28].

In 2005, the Indonesian government faced several challenges, including the accommodation of local needs, collaborating with international agencies, and resolving the 30 years of conflict with GAM. Consequently, the reconstruction was delayed to the conclusion of the year [29]. Five years post-tsunami, some basic infrastructure in Banda Aceh had still not fully recovered [20].

According to the results, the environment sector had the longest recovery. Approximately 61,816 ha of planting area was contaminated by salt, marine mud, waste, debris, etc. [30]. This triggered soil degradation and damaged some important commodity crops, such as rubber, coconut, cacao, paddy crops, fruits, and vegetable trees [31]. Since soil and groundwater improvement are both costly and time-consuming, the environment sector’s recovery speed was relatively slow after the cleaning phase, which was almost complete.

The results of the DRI analysis showed an early recovery phase, focusing on addressing emergency needs such as safe shelter and economic security [3]. The survey conducted in Aceh during this period identified a substantial temporary surge in reconstruction activities to support income generation and address housing, health, and education problems [1,7]. Furthermore, recovery in the social and livelihood sectors, which were promoted by temporary programs, had the fastest progress compared to others. A long-term analysis might show a different trend to the early stage, where recovery efforts still search for the best form to balance speed with caution when pursuing Build Back Better [6].

A significant challenge in pursuing Build Back Better is the coordination among NGOs and agencies included in complex tasks and the dynamics of the situation in early recovery. The Indonesian government established the Rehabilitation and Reconstruction Agency (Badan Rehabilitasi dan Rekonstruksi, BRR) in 2005. However, a large number of actors compounded the establishment with responsibilities, particularly giving a free hand to all agencies [32]. The intention of donors to assert profiles led to unfair competition and excluded community participation [27]. Some even interpreted “build-back better” as “build-back faster”, leading to an oversupply of housing reconstruction without considering the beneficiaries’ needs and preferences [33].

Various patterns of recovery progress showed that needs assessments should be conducted periodically in different affected areas. This is because lightly damaged areas recover faster, and early recovery programs are not relevant there. Assessment results may lead to different recovery policies for each affected area. Furthermore, a different approach based on recovery needs will help to balance recovery targets and achieve the target of “Build Back Better” instead of “Build Back Faster”.

This study focused on the early stage of Aceh post-tsunami recovery, but the results are also useful for sustainable post-crisis recovery [13]. Moreover, long-term analysis tends to produce a different perspective and relative position. For example, disaster risk reduction conducted in 2015 [34] reported that several programs were performed in Banda Aceh to prepare for tsunamis in the future. Another investigation [35] found that vulnerable coastal areas were not protected by natural protectors such as mangroves.

Regarding early warning systems, it was observed that the government had established six early warning system alarms and conducted feasibility tests annually. The alarms can be heard within a radius of 500–700 m, reaching 2–2.5 km with additional sound amplification. Residents are also provided with disaster preparedness training by walking and running along evacuation routes. This training was conducted to disseminate knowledge to the community regarding disaster risk reduction. However, it is also important to frequently upgrade early warning systems with faster and more accurate tsunami forecasting methods to protect coastal populations from future tsunamis [36].

Due to the variation in results, future study is recommended to track long-term recovery using the standard metrics. Long-term analysis with standard metrics also makes it easier to communicate the results of recovery progress to policymakers and other stakeholders. Furthermore, the DRI provides a basic framework to simply assess community recovery using aggregated individual-level data to make a composite index. When applied to STAR data, the DRI shows the relative position of recovery, as suggested by a previous study [12], addressing the gap in limited public use data in post-disaster studies [8,9].

In this study, STAR analysis was limited to two waves or approximately 19 months after the disaster. This did not allow us to show long-term trends, such as recovery curves, that can be used as good lessons to prepare for future disasters [37]. For example, the actual trend of education and health recovery was not identified, as supported by temporary facilities. Since STAR data are longitudinal, they can be used for long-term analysis when the following waves have been published. Variable adjustment may also be needed due to the different availability of variables. From these limitations, it was discovered that data availability and consistency between waves in post-disaster settings should be maintained to monitor recovery progress. Despite the limitations in data availability, the simplicity of the DRI method enhanced the application for various users.

Another limitation of DRI analysis using STAR is the absence of pre-disaster data, as several variables were unavailable in SUSENAS 2004. Consequently, there might not be an accurate measurement of the changes in conditions from before to after the disaster, including changes to houses and roads. These conditions should be considered in comparison with other available disaster data settings, ranging from before to after the event, to provide a comprehensive record of the recovery process [9].

5. Conclusions

In conclusion, this study aimed to measure recovery after the 2004 tsunami in Aceh by applying the DRI to STAR data. The DRI successfully plotted the relative positions of recovery progress across affected areas and sectors over time in a simple method. The recovery rates in the medium and heavily damaged areas increased by 2.05 and 7.45 percentage points, respectively, with a decrease of 0.33 percentage points in the lightly damaged areas. Furthermore, recovery in the social and livelihood sectors, which was promoted by temporary programs, was the fastest as compared with other sectors.

Approximately 5 to 19 months after the disaster, recovery progress was in the early stage of reconstruction. However, it played an important role in correlating the relief and strengthening of the state’s capacity to sustainable post-crisis recovery. During the transition from early recovery, policymakers should be careful in moving from support by temporary boosters to more sustainable recovery programs.

The results suggest measuring recovery progress periodically to achieve implementation based on the different relative positions and needs assessments over time. This would also help to balance recovery targets in pursuing “Build Back Better”.

Periodic measurements in long-term settings are an important agenda for future study. Using the DRI, this study contributed a simple method to present the relative positions of recovery progress and communicate with stakeholders. The DRI provides a basic framework that enables development using STAR or other longitudinal data from different areas. Despite several limitations, STAR shows potential for practical development of the public use of longitudinal data for pre-disaster and post-disaster assessment.