2. Related work

The present study is related to A. studies concerning the sufficiency degree of nursing facilities, B. studies concerning care services provided from nursing facilities, and C. studies concerning facility location problems. The following will introduce the major preceding studies in the above three study areas and discuss the originality of the present study in comparison with the others.

In A. studies concerning the sufficiency degree of nursing facilities, as this topic attracts attention especially in Japan, there were a lot of preceding studies until now. As mentioned in Section 1, the reason for this is that the increase of aging population is especially rapid and the lack of nursing facilities has become a serious social issue in recent Japan. Yamada et al. (2008) [3] grasped the regional difference in demand for nursing facilities by means of interviews. Yamamoto et al. (2015) [4] considered elements such as the positioning of various equipment and nursing modality within the recovery rehabilitation ward as spatial structure and examined the reality of activities of residents in their leisure time. Acker et al. (2015) [5] assessed the nursing facility characteristics, quality ratings, and the views of facility administrators about the implications of an increasing number of foreign-born employees in Washington State in the USA. Takase et al. (2016) [6] grasped the reality of the operation of home care and organized the travel distance of caretakers as well as the service zone using the location and road information of users, caretakers, and offices in Japan. Fujita et al. (2017) [7] organized objections concerning the construction of nursing facilities and examined the changes in social awareness toward such conditions over time in Japan. Hunnicutt et al. (2018) [8] examined and quantified geographic variation in the initiation of commonly used opioids and prescribed dosage strength among older nursing home residents in the USA. Tahara et al. (2018) [9] clarified the attitude of social welfare facility staff regarding the acceptance of evacuees when natural disasters occur and elucidated factors that obstruct the acceptance of such evacuees.

Evaluation of Nursing Facility Locations Using the Specialization… DOI: http://dx.doi.org/10.5772/intechopen.81364

Meanwhile, though the number of nursing facilities and its capacity are increasing, the utilization rate of such facilities remains at the same level and the lack of facilities has not improved. Though subsidy is provided by the national and local governments for the construction of nursing facilities, the amount of subsidy cannot be greatly increased due to the need for more childcare and medical facilities as well, making it difficult to expect a great increase of nursing facilities in the future. Therefore, as a solution to the lack of facilities, the construction of new nursing facilities should be prioritized in areas with greater needs. In order to make this possible, first of all, it is necessary to accurately grasp the areas that lack nursing

Additionally, there are various facility types, and certain characteristics can be

The present study is related to A. studies concerning the sufficiency degree of nursing facilities, B. studies concerning care services provided from nursing facilities, and C. studies concerning facility location problems. The following will introduce the major preceding studies in the above three study areas and discuss the

In A. studies concerning the sufficiency degree of nursing facilities, as this topic

attracts attention especially in Japan, there were a lot of preceding studies until now. As mentioned in Section 1, the reason for this is that the increase of aging population is especially rapid and the lack of nursing facilities has become a serious social issue in recent Japan. Yamada et al. (2008) [3] grasped the regional difference in demand for nursing facilities by means of interviews. Yamamoto et al. (2015) [4] considered elements such as the positioning of various equipment and nursing modality within the recovery rehabilitation ward as spatial structure and examined the reality of activities of residents in their leisure time. Acker et al. (2015) [5] assessed the nursing facility characteristics, quality ratings, and the views of facility administrators about the implications of an increasing number of foreign-born employees in Washington State in the USA. Takase et al. (2016) [6] grasped the reality of the operation of home care and organized the travel distance of caretakers as well as the service zone using the location and road information of users, caretakers, and offices in Japan. Fujita et al. (2017) [7] organized objections concerning the construction of nursing facilities and examined the changes in social awareness toward such conditions over time in Japan. Hunnicutt et al. (2018) [8] examined and quantified geographic variation in the initiation of commonly used opioids and prescribed dosage strength among older nursing home residents in the USA. Tahara et al. (2018) [9] clarified the attitude of social welfare facility staff regarding the acceptance of evacuees when natural disasters occur and elucidated factors that

originality of the present study in comparison with the others.

seen in selecting the location according to the type. For example, commercial establishments such as convenience stores are located in busy areas that are highly populated in order to get more customers and increase profit. On the other hand, educational and public facilities such as schools tend to be located in areas where many people can fairly access them. Similar to the latter case, instead of being concentrated in certain areas, nursing facilities must be located where everyone in need can access it in a fair manner. Based on the background mentioned above, using geographic information systems (GIS) and public open data, the present study aims to quantitatively evaluate the current situation of nursing facility loca-

tions in urban areas within Japan as the target.

Geographic Information Systems and Science

obstruct the acceptance of such evacuees.

24

facilities.

2. Related work

For B. studies concerning care services provided from nursing facilities, there were a lot of preceding studies especially in the USA. Yun et al. (2010) [10] developed and validated an algorithm to identify the use of nursing facility services and differentiate short- from long-term care using Medicare claim data. Walsh et al. (2012) [11] analyzed potentially avoidable hospitalizations (PAHs) for dually eligible beneficiaries receiving long-term or post-acute care services to inform the development of health policies and better care programs and outcomes for this population. Cherubini et al. (2012) [12] examined resident and facility characteristics associated with hospitalization in a cohort of the older nursing home residents in Italy. Onder et al. (2012) [13] assessed the nursing home residents in Europe, focusing on the services and health for the elderly in Long TERm care (SHELTER) study. King et al. (2013) [14] examined how skilled nursing facility (SNF) nurses the transitional care of individuals admitted from hospitals, the barriers they experience, and the outcomes associated with variation in the quality of transitions. Neuman et al. (2014) [15] measured the association between SNF performance measures and hospital readmissions among Medicare beneficiaries receiving postacute care at SNFs. Fry et al. (2018) [16] used robot cats to reduce the total number of falls in the facility quality improvement methods (strategy for improvement, design, setting, participants, interventions, measurements, and evaluation). Yamaguchi (2018) [17] grasped the relationships between received quality of care and information sharing among workers in nursing facilities for the elderly.

For C. studies concerning facility location problems, Segawa et al. (1996) [18] developed a system that can simulate factors related to childcare facility improvements, such as the extension of childcare hours and the location of new childcare facilities. Nagashima et al. (2014) [19] proposed an algorithm that derives the best location for electric vehicle (EV) power stations by means of the mean field approximation. Ozgen et al. (2014) [20] combined a two-phase possibilistic linear programming approach and a fuzzy analytical hierarchical process approach to optimize two objective functions (minimum cost and maximum qualitative factor benefit) in a four-stage (suppliers, plants, distribution centers, and customers) supply chain network in the presence of vagueness. Munemasa et al. (2015) [21] used the linear relaxation method to propose a method that derives the best solution for minimizing travel costs for the urban model made up of residential and business areas. Zhang et al. (2016) [22] investigated a facility location problem incorporating service competition and disruption risks, developing a new binary bilevel linear programming (BBLP) model. Ohdate et al. (2017) [23] considered relocation of facilities for the management of public facilities and categorized them based on building, function, and location to create an evaluation method for them. Nagai et al. (2017) [24] proposed an agent-based urban model in which the relationship between a central urban area and a suburban area was simply expressed. Usui et al. (2018) [25] theoretically investigated the relationship between the continuous walking distance distribution and the density of resting places.

On the other hand, in recent Japan, there are distinctive preceding studies that adopted an economic method into C. studies concerning facility location problems. Tanaka et al. (2015) [26] applied the quintile share ratio (QSR), which is an indicator showing the degree of bias in income, to the facility locational analysis for linear cities. Additionally, with QSR as a reference, Tanaka et al. (2016) [27] used the median share ratio (MSR), which is an equity measure, to develop a facility location evaluation model in a linear city with one or two facilities, as well as a uniformly distributed population. Furuta et al. (2017) [28] used a method that generalized the QSR and proposed a solution to optimize multiple facility locations in cases where the demand and candidate facility locations are discrete.

Regarding studies related to A, though the studies grasped the awareness of the local residents and awareness and behavior of users, as well as the actual condition of operation for nursing facilities, the location of the facilities was not considered. Regarding studies related to B, though the studies investigated the care services provided from nursing facilities, the location of the facilities was not considered. Regarding studies related to C, Nagashima et al. (2014) [19] and Munemasa et al. (2015) [21], respectively, considered EV power stations and business-andresidential distribution as their study subject and proposed a method to derive efficient locations. Though Segawa et al. (1996) [18] conducted simulations with the assumption that the facilities will be relocated, as there are currently many existing nursing facilities in cities of Japan, the above simulations cannot propose realistic solutions for such facilities. Additionally, though Tanaka et al. (2016) [27] and Furuta et al. (2017) [28] focused on the equity concerning the facility location evaluation method, it has only been applied to virtual cities and not to any actual cities. Therefore, with the results of the preceding studies mentioned above as a reference, the present study will demonstrate the originality by considering the lack of nursing facilities, which has become a serious social issue, and quantitatively evaluating current facility locations.

3.1.4 Evaluating nursing facility location using the specialization coefficient of the

Evaluation of Nursing Facility Locations Using the Specialization…

Using the distribution maps of the aging population obtained in section 3.1.1 and the shortest route calculated in section 3.1.3, the evaluation points are calculated for

3.2.1 Creating the distribution maps of the aging population and nursing facilities as well

The age group being evaluated in the present study is those over the age of 75. There are many cases where those over 65 can use nursing facilities. However, according to Hashimoto (2015) [29], the life expectancy and health span in Japan have become higher in recent year, with the latter being 71.19 for men and 74.21 for women. Therefore, assuming most users of nursing facilities are over 75, the age group was set for those over 75. As for the evaluation target area, in order to calculate evaluation points according to each area, GIS is used to display the distri-

GIS is used to display the distribution of nursing facilities on the digital map. While nursing facilities include facility types such as admission type, commuter type, and other related facilities, the present study will only consider admission

As for the distance between nursing facilities and each area in the present study, the road network distance is used instead of the linear distance. This is because the linear distance may be extremely short compared to the distance when traveling along the roads to the nursing facilities, and this may cause the estimate of the travel

First, GIS is used to display the road network map of the evaluation target area in digital map form. Next, the node closest to every nursing facility and the representative point in each area will be set up on the digital map. The representative point is the central point of the area, and the nodes are the intersections and endpoints of the roads. This is set up as the distance is calculated according to each node. In the present study, the node set as the representative point of the area is the start node,

3.2.2 Calculating the specialization coefficient of the population aging rate and adding it

In the present study, the distance is weighted so that the road distance of areas with a large demand is longer, while the road distance of areas with a small demand

3.2.2.1 Introducing the specialization coefficient of the population aging rate

population aging rate

DOI: http://dx.doi.org/10.5772/intechopen.81364

as road network maps

3.2.1.1 Distribution map of the aging population

bution of the aging population on the digital map.

distance of users to be shorter than it actually is.

and the node set as the nursing facility is the end node.

3.2.1.2 Distribution map of nursing facilities

type of nursing facilities.

3.2.1.3 Creating a road network map

to the road network map

27

3.2 Evaluation method

each area.

### 3. Evaluation framework and methods

#### 3.1 Evaluation framework and process

For the evaluation method of nursing facility locations, PostgreSQL Ver. 9.6.1 and ArcGIS Pro Ver. 2.0 of Environmental Systems Research Institute (ESRI) were used. The evaluation framework and process are as mentioned below:

## 3.1.1 Creating the distribution maps of the aging population and nursing facilities as well as a road network map

The distribution maps of the aging population and nursing facilities as well as a road network map are created in digital map form using GIS. These three types of digital maps are superimposed, and the closest road node from a representative point from each nursing facility and area (divided by town and street) is set on the road network map.

### 3.1.2 Calculating the specialization coefficient of the population aging rate and adding it to the road network map

The specialization coefficient of the population aging rate is calculated using the aging population in addition to the total population data from each area, and the results are added to the road network map.

#### 3.1.3 Calculating the shortest route using A\* algorithm

By applying the data obtained in section 3.1.2 to A\* algorithm, which is explained in the following section, the shortest route between each nursing facility and each area is calculated.

Evaluation of Nursing Facility Locations Using the Specialization… DOI: http://dx.doi.org/10.5772/intechopen.81364

3.1.4 Evaluating nursing facility location using the specialization coefficient of the population aging rate

Using the distribution maps of the aging population obtained in section 3.1.1 and the shortest route calculated in section 3.1.3, the evaluation points are calculated for each area.
