Wilderness Search and Rescue: Adopting a New Approach for Finding Missing People Using GIS Techniques in Saudi Arabia

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Wilderness Search and Rescue: Adopting a New Approach for Finding Missing People Using GIS Techniques in Saudi Arabia

https://doi.org/10.21203/rs.3.rs-2583856/v1

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Every year, hundreds of people go missing in the wilderness of Saudi Arabia. There is an immediate need to examine modern geographic techniques for finding these people. Geographical information systems (GIS), for example, play a crucial role in WiSAR, not only in mapping probability areas but also in applying further analysis and modeling methods to reduce time and effort and guide life-saving task forces in the right direction. In a hypothetical case study conducted in Saudi Arabia, this study compares two standard WiSAR models, ring and mobility. In these situations, both models can be used. However, the new approach in the mobility model drastically reduces the possible search areas, from 101,787 km2 in the ring model to 335.34 km of likely trails and unpaved roads, and gives exact directions to where a missing person may be.

Search and Rescue

WiSAR

Geographic Information Systems

mobility model

Search and rescue (SAR) is the operation for locating missing individuals as soon as possible (Jurecka & Niedzielski, 2017). While "Search" is described as the first part of the operation, which entails locating the missing item or subject, "Rescue" is the second phase, which entails saving the missing item or subject after determining its location and returning it to a safer area (Ferguson, 2008). Missing cases (excluding kidnappings) may occur in urban areas and are usually associated with missing children, elders, or mentally ill individuals (Niedzielski et al., 2021). In contrast, other critical missing cases usually occur outside urban areas in the wilderness (WiSAR Cases) and involve hikers, riders, tourists, etc. (Heth & Cornell, 1998; Koster, 2008)

When a person is missing outside of a city, search and rescue personnel in the wilderness, confront the task of locating them. This challenge must consider limited resources and a long search period, which can reduce a person's chances of surviving (Adams et al., 2007). The most common method for locating a missing individual is to create probability maps depicting potential locations. Numerous researchers employ the ring model to create such probability maps (Ferguson, 2008; Doherty et al., 2014).

Since World War I, SAR strategies, and methods have evolved by utilizing dogs to locate wounded soldiers on battlefields (National Search and Rescue Dogs Association). Following World War II, WiSAR techniques evolved to include mathematical frameworks to increase the likelihood of locating missing objects (Ferguson, 2008). Over time, these techniques evolved into different approaches, such as using airplanes and helicopters, which are still employed in many WiSAR missions worldwide (Grissom et al., 2006; Ausserer et al., 2017). In addition, World War II witnessed a rise in the usage of probability maps, created by mathematical calculations or based on lost-person behavior, and resulted in the creation of probability areas identifying the most probable locations where an object may be located (Doherty et al., 2014).

SAR approaches have evolved to incorporate remote sensing techniques, such as drones and unmanned technologies (Wing et al., 2016; Jurecka & Niedzielski, 2017; Christopher, 2017; Karaca et al., 2018; Niedzielski et al., 2021; Martinez-Alpiste et al., 2022; Veelen et al., 2023), as well as various geographical information systems (GIS) and mapping techniques, such as geostatistical and network analysis (Doherty et al., 2014; Loren & Justine, 2018; Drexel et al., 2018; Kroh, 2020). However, a variety of WiSAR-related factors, such as terrain, land cover, climate and weather conditions, and the lack of data, play a crucial role in selecting the most appropriate approach, particularly the behavior of the missing person, which can lead to a more subjective approach (Lin et al., 2010).

This study aims to provide an overview of WiSAR in the Kingdom of Saudi Arabia regarding the methods used by task forces and volunteering groups. Moreover, to adopt a new approach that is more suitable for people in Saudi Arabia and the physical environment via a hypothetical case study in the Al-Quwayiyah governorate in the middle of Saudi Arabia.

Wisar Missions In Saudi Arabia

Between 2009 and 2020, over 2,497 people went missing in the Saudi Arabian wilderness; 112 were found deceased, representing roughly 6% of the total missing (General Directorate of Civil Defense, 2020). On the other hand, according to official reports from volunteer groups (AOUN Association for Search and Rescue, 2020), up to 25% of the missing cases each year end up dead in Saudi Arabian deserts. Moreover, since 2014, WiSAR missions in Saudi Arabia have been led by the police instead of the civil defense force, with the help of volunteers who are primarily untrained and unqualified.

The WiSAR process in Saudi Arabia usually starts when the relatives of the missing contact the local police in the respective province. Usually, the police in Saudi Arabia start the investigation after 24 hours by calling the local telecommunication company to inquire about the last time the missing person's phone was connected to the network (cell sites), which can take between another one and five hours. Meanwhile, the missing relatives call the volunteering groups, which takes several hours until they meet at the last known point (LKP)/last point seen (LPS), which is generally around the cell site area where the initial planning point (IPP) takes place. In other circumstances, if the missing person does not have a phone, the IPP starts at the point last seen (PLS), where the missing person was seen last (Ferguson, 2008).

According to the AOUN Association for Search and Rescue (2020), the police and volunteer groups in Saudi Arabia use methods and tactics in WiSAR that rely on the rescuers' accumulated experience with the surrounding terrain and land cover from previous tasks, with limited use of spatial technologies and mapping techniques, such as using the Google Earth Engine to explore the spatial nature of the area and send task forces to different destinations based on accumulated information. At this point, the ground force (cars) and the air force (gliders and autogiros) start searching in different directions and linking up with each other using wireless communication and GPS to pinpoint their exact location.

What distinguishes WiSAR cases in Saudi Arabia is that about 90% of the SAR tasks are not about searching for the missing person but searching for the missing person’s vehicle, which is usually an easy object to find compared with searching for a person (AOUN Association for Search and Rescue, 2020). This is primarily because the people who tend to go missing in Saudi Arabia are often local tourists, campers, and herders who have cars to move around on dirt roads. These vehicles can sometimes become stuck in the sand or break down, leading to the individuals becoming lost or missing. Further, due to severe climate and terrain conditions, missing persons are usually found around their cars due to difficulty in walking and moving in such an environment (AOUN Association for Search and Rescue, 2021).

The time a person can spend without water and food is contingent on age, health, and weather conditions, i.e., several hours or days. Theoretical search areas exceeding 5,026 km2 for every 40 km walked per day are possible if the missing person was driving a car. In contrast, the distance that a person can travel on foot varies according to the factors mentioned above and the terrain of the area, from a few hundred meters to up to 40 km per day in the case of a nominal walking speed of 5 km/h for eight hours a day (Doherty et al., 2014). Moreover, many cases of missing people in the wilderness of Saudi Arabia died within only 24 hours of being reported missing (especially those missing during summer). However, the direct distance between the locations of the deceased persons and the nearest safe place in some cases did not exceed 2 to 3 km only (AOUN Association for Search and Rescue, 2021), which may indicate that WiSAR operations often carried out without proper geospatial planning in advance.

Gis Methods In Wisar

In recent years, GIS has been highly significant in the planning of WiSAR missions (Ferguson, 2008; Doherty et al., 2014; Paul et al., 2017; Loren & Justine, 2018; Drexel et al., 2018; Tambassi, 2019, p. 246; Kroh, 2020). In some cases, GIS technologies are too complex to be used in a short-term operations search. Many elements, including cost, training, data, and map sharing, could impact operations (Koester, 2008). However, the ring model, the mobility model, and network analysis are the three most common methods for implementing GIS in WiSAR missions. Due to a shortage of data sources (such as unpaved roads and hikers' trail information), ring and mobility models were addressed instead of network analysis.

Ring Model

The ring model is the most popular technique for creating probability area (POA) maps, often known as the Euclidean distance model (Doherty et al., 2014). It is a primary geostatistical analysis method based on Koopman's (1980) mathematical search theory (Jurecka & Niedzielski, 2017). Euclidean distance is used in the ring model, starting at the ring radius representing the IPP, where PLS and LKP generally exist (Ferguson, 2008). From a 5% possibility of finding the missing person in the first ring to a 95% chance in the final ring, each successive ring signifies an increasing likelihood of finding the person (Tambassi, 2019, p. 246). The approach is novel because it prioritizes the search area, giving greater weight to closer rings. In addition, the ring model can be implemented with little effort in any GIS software or even on a paper map (Doherty et al., 2014). The ring model is limited since it relies on projected data to run in GIS software, which is easily misunderstood when working with GPS data in the field. It ignores additional inputs such as geography and land use distribution.

Mobility Model

The International Search and Rescue Incident Database (ISRID) defines mobility as "the amount of time the subject was moving." This is the time a person spends walking away from the IPP (Drexel et al., 2018), which is an essential factor in WiSAR missions. The mobility model is a method to make POA maps. It is similar to the cost–distance analysis often used in building infrastructure, wildlife habitats, and studying anthropology (Kobler & Adamic, 2000; Doherty et al., 2014). The mobility model uses raster data, like DEM, land use, and land cover data, which are calculated using map algebra to make speed and resistance raster layers. These layers show where the missing object could be by leaving out areas where the missing person cannot go because of a steep slope or the way the land is covered (Johnson, 2016). Unlike the ring model, this method considers different things when figuring out where the missing object is. However, this method is very subjective because it mostly depends on human decisions when calculating inputs (Doherty et al., 2014).

Study Area

The Al-Quwayiyah Governorate (Fig. 1) is a large region in the central Saudi province of Arriyadh. It covers an area of 51,725 square kilometers. The Al-Quwayiyah Governorate alone occupies about 14% of Arriyadh Province, which makes it the sixth-largest governorate in Saudi Arabia. Despite the vast area of the Al-Quwayiyah Governorate, the total population is less than 89,544. Topographically, the Al-Quwayiyah Governorate is a desert area with only several urban regions on the Arriyadh–Makkah Highway, which passes through the area at an elevation of around 606–1,505 m. In the summer, however, temperatures exceed 48°C with an annual rainfall of less than 100 mm. All these severe climates and topographic conditions make it almost impossible for people to survive in a desert without adequate water, food, and shelter.

Though a missing person's chances of survival drop precipitously over time (Doherty et al., 2014), there are still two crucial considerations to bear in mind:

1) The probability of area (POA)

2) The probability of detection (POD)

Both of which increase the probability of success (POS) as follows (Koopman, 1999):

$$POS=POA \times POD$$

For instance, GIS methods can aid in increasing POS by utilizing models like ring and mobility to improve the estimation of POD (Drexel et al., 2018) or by decreasing the area of search to increase POA (Doherty et al., 2014). So, this paper's contribution is more than just lowering the POA. Instead, it is about using the mobility model and cost-distance analysis to understand better where the missing person might be going.

This paper uses a hypothetical case study for a missing person in his late fifties driving a 4x4 car, which matches the profile of tens of missing persons in Saudi Arabia each year. The missing person PLS was at the coordinates of 23°11’38’’N, 44°41’53’’E in central of the Al-Quwayiyah Governorate; at this point, the IPP takes place at the same point.

Ring Model

We used multiple ring buffer analysis in ArcMap 10.8 to design six separate buffer zones around the IPP, assuming that the missing individual was traveling away from the PLS in his 4x4 vehicle at an average speed of roughly 30 km/h before the vehicle broke down. Using Euclidean distance, we can divide the search area into concentric rings, with a 15% chance of finding the missing person within the first 30 km away from the IPP (an area of 2,827 km2) and a 90% chance within 180 km away from the IPP (a total of 101,787 km2) (Fig. 2).

Mobility Model

The mobility model used in this study serves two primary purposes

1) Decreasing the POA

2) Estimating the exact POD that represents the possible final resting place of the missing person.

This approach is built on the assumption that a missing individual will head straight for the next hamlet, bypassing any complex terrain like steep inclines. Using the USGS 30 m digital elevation model, we divide the research region into nine categories (Table 2), ranging from easily traversable locations with surfaces of 0 to 2 degrees to those that are completely impassable, such as mountains and cliffs, with slopes of 30 to 70 degrees (Table 1).

Table 1

Used data characteristic.
Data	Source	Type	Resolution
Digital Elevation Model (DEM)	United States Geological Survey (USGS)	Raster Datasets	30 m
Saudi Arabia province / governorate boundaries	General Authority for Statistics (SA)	Vector Datasets	-

Table 2

Slope classes used in the mobility model.
Slope	New Class	Accessibility
0–2.1	1	Easy
2.1–3.8	2
3.8–5.8	3
5.8–8.4	4
8.4–11.9	5
11.9–16.3	6	Moderate
16.3–22.0	7	Moderate
22.0–30.0	8	Almost impossible
30.0–70.6	9	Almost impossible

After categorizing the slopes of the study area into nine distinct classes, cost–distance analysis was used to determine the least-cumulative cost distance for each cell, as well as backlink raster analysis to determine the direction and identify the next neighboring cell (the succeeding cell) along the least-accumulative-cost path from a cell to its least-cost source. Then, cost pathways were computed from the IPP to the five closest villages in different directions where the missing individual may be found (Fig. 3). Five villages comprise the IPP: Halban in the northwest, Ruwaydah in the north, Umm Jadder in the east, Al Hafirah in the southeast, Moses in the south, and Qiran in the southwest.

Unlike the ring model, the mobility model is dependent on the information gathered from relatives about a missing person's possible location in order to boost POS by decreasing POA and determining the exact POD to save time and energy (a similar result was found in Doherty et al. [2014]). In contrast, the mobility model narrowed the POA down to 335.34 km of unpaved roads where the missing person's automobile could have broken down, from 101,787 km2 in the ring model. In addition, the mobility model reduced the time it would take the missing individual to drive the car from 21 hours to 9:51 hours, assuming a speed of 30 kilometers per hour (Table 3). Therefore, the mobility model managed to reduce the POA and the possible mobility time the missing person traveled by almost 50% and determined the exact direction.

Table 3

Comparison of the search area and mobility time in-ring and mobility model.
POA %	POA in Ring Model (Area in km²)	Mobility Time in Ring Model* (Hour)	POA in Mobility Model (Distance in km)	Mobility Time in Mobility Model* (Hour)
15	2827.42	1	35.39	1:10
30	8482.28	2	52.56	1:45
45	14137.15	3	53.40	1:46
60	19792.02	4	59.63	1:58
75	25446.88	5	62.62	2:04
90	31101.75	6	71.72	2:23
Total	101787.52	21	335.34	9:51

* at an average speed of 30 km/h.

Although the ring model has several benefits, including speed and simplicity, it is evident that its results cannot go beyond the job of organizing prospective search areas, as other researchers have pointed out (Tambassi, 2019; Doherty et al., 2014). However, as noted, the mobility model can provide helpful information about the missing person's last known location and activity, which is an essential factor in WiSAR missions (Drexel et al., 2018). The advantages and disadvantages of the two models are summarized in (Table 4).

Table 4

Advantages and disadvantages of ring and mobility models.*
POA %	Ring Model	Mobility Model
Advantages	Easy analysis Cheap and fast No additional information is necessary	Considers terrain Driving speed included Detect POD Reduce time and efforts in SAR
Disadvantages	No additional information (terrain, vegetation) included No linear features (street/trails) included Takes too long time to search each zone	Many information layers are necessary Equal walking speed regardless of driving up or downhill Resolution depends on input data Expertise and software are needed

* Adapted from Drexel et al. (2018).

This research introduces a novel method for determining search areas and trails in Saudi Arabian WiSAR operations. This approach considers the physical and anthropogenic environment and behavior of missing persons in Saudi Arabia. In this paper, we use a hypothetical case study set in Saudi Arabia to compare and contrast the strengths of the two most popular models for WiSAR—the ring model and the mobility model. Compared to the ring model's 101,787 km2, the mobility model's 335.34 km of probable routes via which the missing person may have traveled significantly reduces the POA, and the time the missing person has to travel decreases from 21 hours to 9:51 hours. As a result, either model can be used successfully in WiSAR. The mobility model, on the other hand, can identify the POD of a missing person to save time and improve the POA.

Overall, additional research is needed on the patterns of missing person behavior in Saudi Arabia, which is a factor in WiSAR operations and cutting-edge technology like unmanned aerial vehicles (UAVs) as the primary instrument for locating them. Moreover, research efforts should be directed toward georeferencing missing cases in geodatabase systems and employing various analyses to increase our geographical understanding of these issues.

Conflict of Interests: The authors declare no conflict of interest.

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No competing interests reported.

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Wilderness Search and Rescue: Adopting a New Approach for Finding Missing People Using GIS Techniques in Saudi Arabia

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Abstract

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Introduction

Wisar Missions In Saudi Arabia

Gis Methods In Wisar

Ring Model

Mobility Model

Study Area

Materials And Method

Ring Model

Mobility Model

Results Discussion

Conclusion And Future Research

Declarations

References

Additional Declarations

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