Walking plays a key role in promoting healthy communities, increasing economic opportunities, and strengthening social connections [1
]. It attracts attentions of urban planners, health officials, geographers, social scientists, and policy makers. Besides, walking is the most common form of physical activity among adults [2
]. The extent to which adults will walk in their neighborhoods relies on a variety of factors in urban environments [3
]. As a popular term in the urban studies and public health literature, walkability is increasingly employed to describe the capacity of a community to support its residents’ walking activity [6
]. The characteristics associated with neighborhood walkability have positive impacts on three aspects.
The first is about health benefits. Walking is an accessible form of physical activity that may be readily adopted by people of all ages, requiring little skill, and presenting a limited opportunity for injury [8
]. This is particularly important in the context of the increasing obesity problem worldwide and in the U.S. [12
]. In 2014, the obesity rate of American adults was over 37% compared to a rate of less than 15 percent in 1960 [13
]. Obesity carries individual as well as collective costs, and it is a major risk factor for cardiovascular disease, diabetes, depression, sleep apnea, hypertension, bone and joint diseases, and various cancers [10
]. Its associated annual medical costs reached $
147 billion in 2008 [15
]. A lack of physical activity is a primary driver of the growing obesity rate [12
]. The World Health Organization reports that 25% of adults have physical activities less than 150 min per week [16
]. This deficit led the U.S. Federal Government published its physical activity guidelines to meet Healthy People 2020 targets [17
]. Increasing the proportion of trips made by walking is one of the fifteen objectives, as recommended by the U.S. Department of Health and Human Service [18
Social benefit is another outcome of increasing walkability. It is found that walkability is linked to the component of social capital and quality of life in many studies [19
]. Social capital is the networks of relationships among people, which can enhance the efficiency of society [20
]. Studies have also found that neighborhoods with better conditions of walking have higher levels of social capital, as people there are more likely to know each other, and to be more socially connected [22
]. In addition, high walkability is always associated with an attractive urban built environment, which can be a driver to restore parts of the cities.
The third is about economic benefit. A highly walkable community could provide a safe and convenient environment for walking and riding, as such, people are likely to spend less on transportation (www.pedbikeinfo.org
). Health cost could be saved due to sufficient physical exercises [23
]. In addition to personal cost, non-motorized travelling mode may reduce public costs for road and parking services, traffic congestion, and environmental damages [23
]. Besides, walkability could affect housing prices to some extent [26
]. In the U.S., houses located in more walkable neighborhoods are about $
4000 to $
34,000 more expensive than similar houses in less walkable areas [29
]. Further, walkability can promote spending on local businesses to accelerate economic development in communities. Major beneficial activities, such as socializing, shopping, or dining, may appear in pedestrian environment. These activities are directly influenced by the quality of the walking environment [23
]. The 2012 Benchmarking Report on Bicycling and Walking in the U.S. [30
] found that pedestrian and bicycle infrastructure projects create eight to 12 jobs per $
1 million of spending, compared to just seven jobs created per $
1 million spent on road infrastructure projects [31
]. An example is that pedestrians spend more money in New York City over the course of a week than users with any other transportation mode [32
To perform in-depth walkability analysis, one option is to objectively evaluate the different aspects of built environment related to walkability. Three dimensions of walking environment in a community, including density, destinations and design (also known as “3Ds”), were summarized in the literature [33
]. Following this fundamental concept, the Neighborhood Environment Walkability Survey was conducted in San Diego, California [6
]. Two major aspects of walkability, including connectivity and proximity to destinations, were characterized in [2
]. Further, an index was developed at the census block group level to assess the neighborhood walkability by considering density, land-use mix and street connectivity [34
]. The Neighborhood Environment Walkability Scale was further improved to measure residents’ perceptions to different environmental characteristics [4
Although existing research effectively reflects walkability to some extent, the metrics in these studies may be further improved in several ways. First, the selection of some variables, for example, the weights used in these indicators, rely heavily on local knowledge, which might be somewhat subjective [34
]. Second, some measures used in the literature may not be readily available in other study areas, due to the limited data access as well as regional differences. Third, most of studies in the literature place an emphasis on cities with a large population [33
], cities with smaller population and at different development stages are understudied. To address these problems, the major purpose of this research is to develop a data-driven framework with open datasets, which is able to adaptively refine variables and to quantify walkability in a data-driven manner. It is expected that this data-driven framework will be applicable and useful to any other study area with different features, especially to those post-industrial cities that rarely attract researchers’ attentions.
2. Study Area and Data
Our study area lies within Broome County, New York, and contains several shrinking cities in the Rust Belt Cities communities historically called the “Triple Cities”, and now more broadly as a metropolitan region characterized as the “Greater Binghamton Area” (see Figure 1
). The Triple Cities lies within two narrow river valleys (i.e., Susquehanna and Chenango Rivers), with older settlements that tended to be linear until residential expansion pushed development into the hills, sometimes on to relatively steep slopes. Most of the homes and businesses, however, sit on flat terrain, including in the flood plain areas, and in dense or concentrated areas. The entire study area has a large percentage of older structures, including homes built prior to 1950. This includes working class homes built by Endicott-Johnson, Inc. for their employees, and older homes later converted for college students and the poorer populations. The climate typically brings long winters and a late spring, with run-off and freeze and thaw, which can have negative impacts on walkways, sidewalks, and streets. While the sidewalks often provide connectivity between neighborhoods and redevelopment areas, climatic factors can provide challenges. All three cities have aged infrastructure, ranging from buildings to streets and sidewalks, and sewage and water lines.
The old Triple Cities contained the small cities of Binghamton, Johnson City, and Endicott, and still serve as the old urban core of Broome County. These small cities developed proud industrial histories during the industrial era, and some local corporations enjoyed national and global reputations during that period.
Beginning in the late 19th century, producers of candy, cigars, and other products also provided employment, but had less recognition outside of the region. The early 20th Century brought new corporations that received national and international recognition for their industrial quality and offered large employment. The first, Endicott-Johnson Shoe Corporation (E-J), expanded its employment base to 22,000 workers and had more than 20 factories spread throughout the Triple Cities by the 1940. At its peak it produced 50 million pairs of shoes annually that were consumed globally [48
]. EJ was a paternal corporation and created a landscape of social and recreational spaces visible today as testimony of their position in the urban region (parks, carousels, and former industrial buildings that are being converted into reusable spaces).
When E-J fell to global cheap labor and competition, the Triple Cities had the good fortune of the arrival of Thomas J. Watson Sr., a friend of George F. Johnson (the owner of E-J), in Endicott in 1906, where he created a worker’s time clock. This business evolved into the International Business Machines in the 1920s (i.e., IBM Corporation). After WWII, under the leadership of Thomas J. Watson, Jr., IBM rapidly became a computer industry leader. The second Watson led IBM until the 1960s and the family control of the corporation ceased in the 1970s [50
]. IBM had become a high tech corporation that employed nearly 20,000 at its peak. Other tech corporations made the area attractive too, including Link Aviation, Inc. that produced Link’s famous Blue-Box Pilot Trainer. That invention found success when the federal government purchased large numbers of Link’s machines for training WWII pilots. Binghamton factories boomed and eventually Link Aviation evolved into additional high tech products and was purchased by Singer Corporation.
As a result of the industrial employment provided by these corporations among others, both highly skilled and unskilled work contributed to the growth of Broome County and The Triple Cities until 1970, when Broome County reached its peak of 221,815 (according to U.S. Census Bureau, hereafter). Binghamton had reached its peak population in 1950, 80,674, and continued to fall through 2010 to 47,380. Endicott and Johnson City also reached their population peaks, but in 1970. Since that time, the Triple Cities have evolved into shrinking cities. Manufacturing jobs disappeared; out-migration resulted.
Shrinking cities experience dynamic depopulation trends [51
]. Key indicators produce additional shocks that accompany out-migration and continued loss of manufacturing jobs over decades, further reducing chances for revitalization, and include shrinking per capita income, increasing unemployment, declining commercial, and a loss of the tax base. This may be more problematic for small shrinking cities than large ones.
As reported in Table 1
, it is clear to observe the trends of depopulation and job losses in the three municipalities, based on the statistics from the 2010 U.S. Census data, which we consider the most accurate when compared to the range of error in the yearly ACS estimates. These trends indicate that the Triple Cities scored high in negative indicators related to the loss of population and employment opportunities. As such, it is very unique to study walking environment under such a social context.
In terms of data, multiple open datasets were employed to characterize different dimensions of walkability. The amenity data were used to characterize various types of points of interests (POI) that have either positive or negative impacts on walkability. These POI amenities include fine dining restaurants, fast food restaurants, bars, pubs and taverns, groceries, convenient stores, shopping stores, health services, banks, auto services, parks, local landmarks, bus stops, and post offices. Addresses of these amenities data were directly extracted from the website of Yellow Pages (www.yellowpages.com
), and then converted to point data by geocoding. Unlike the POI data used in most studies, which might have been acquired a few years ago, web scrapping of online results can provide the most up-to-date data. More details of web scrapping of these POIs are introduced in the next subsection below. Additionally, the transportation datasets were derived from OpenStreetMap (OSM), where basic geographic information of most countries can be found. They were used to calculate the street density and the street intersections to represent transport connectivity. Digital Elevation Model (DEM) data was used to characterize the elevation and to calculate slope information.
A computational framework for walkability measurement is proposed in this research. Three major steps of this framework include web scrapping of publicly available online data, determining varying weights of variables, and generating a synthetic walkability index. This method was implemented in the Greater Binghamton Area in upstate New York, and the result was compared with the existing walkability metric. The results of this research suggest three major conclusions. First, the proposed data-driven framework provides an explicit mechanism with geospatial big data. Specifically, relevant variables of built environment can be derived from extracted online contents, and their varying weights can be adaptively determined. Second, the synthetic walkability index based on this framework is comparable to the Walk Score, and it even tends to have a slightly higher sensitivity than its counterpart from comparisons. Third, this framework was effectively applied in a U.S. shrinking metropolitan area, which extends the topical area in the walkability literature. It indicates that this approach has the potential to quantify walkability in any city, especially cities with small population where walkability has rarely been quantified and studied. Urban planning strategies, such as land development, street density, and public transit, will indirectly affect urban inhabitants’ physical activity and obesity. The proposed framework, as well as the synthetic walkability index, provides a general, explicit, and comprehensive method to capture different aspects of the walking environment in a city. For areas that have rare walkability studies, researchers can calculate the synthetic walkability index based on the proposed framework, to assist urban planners, community leaders, health officials, and policymakers in their practices to improve the walking environment of their communities. Examples include making new re-development plans and policies in order to improve the public health of urban inhabitants. Despite the aforementioned strengths, future studies with this data-driven method are warranted in different cities outside the U.S. for a more comprehensive understanding of the proposed framework.