Healthcare providers must consider stroke survivors needs in order to enable a good quality of life after stroke. This review aimed to investigate the perceived needs of the stroke survivors across various domains of care following their discharge from hospital. A meta-ethnographic review of qualitative studies that reported needs of stroke patients after discharge from rehabilitation services was conducted. Main searches were conducted on the following electronic databases: Ovid Medline (1946 to 2021), CINAHL plus (EBSCO), AMED (EBSCO), PsycINFO (1967 to 2021), the Cochrane Library, and PubMed in June 2022. Main outcomes were related to stroke survivors’ views, experiences, and preferences on physical, psychological, social, rehabilitation needs, and other identified needs. Twenty-seven studies were included in the final analysis. The findings show that existing rehabilitation provision for stroke survivors does not address the long-term needs of stroke survivors. Two main issues were revealed concerning the unmet needs of stroke survivors: (1) a lack of information availability and suitability and (2) inadequacy of care and services. It is crucial to further investigate the needs of patients in Asian countries and the Middle East as there is very limited understanding of patients’ needs in the community in these regions. A stroke can occur suddenly but result in chronic effects in terms of a person’s physical health, emotional health, and social aspects . Around Around 80 per cent of those who survive a stroke are sent home from the hospital to continue to recover . FollowiPractical evaluation of unmet needs can be undertaken by determining whether any help has been provided for particular needs identified by people with strokes and whether this issue has been dealt with in a sufficient manner . QualitaA broader understanding of stroke survivors’ needs has not been reviewed after they are discharged from healthcare services from the perspective of both patient and professional. Existing systematic reviews have focused mainly on specific types of needs that are perceived by both stroke survivors and carers. Examples of perceived needs include educational needs and rehabilitation needs ,16,17. OFollowing their discharge from the hospital, what are the needs of the stroke survivors across various domains of care perceived by patients and healthcare professionals?This review was previously registered (PROSPERO 2021 CRD42021256405).Meta-ethnographic guidelines have supported the methodological reporting of this review. This includes the original guidance from Noblit and Hare  as well The literature review search comprised three parts. The first part was to clarify the focus area of the study. To perform so, a scoping search was undertaken. Following this, the primary searches were carried out in June 2022 on the following electronic databases: Ovid Medline (1946 to 2021), CINAHL plus (EBSCO), AMED (EBSCO), PsycINFO (1967 to 2021), the Cochrane Library, and PubMed. The keywords used within the databases were identified using the PICOS strategy, with alternative spellings and synonyms also searched for see . BooleanEligibility criteria are presented according to the SPIDER  acronym.Studies were included if they represented people with strokes. The target population for the study was adults who had been discharged from hospitals after a stroke, ischaemic stroke, or haemorrhagic stroke, who had finished rehabilitation (either at a rehabilitation service or in the community). Studies that involved a mixed sample, such as patients with traumatic brain injury and stroke, were included if the data for stroke survivors could be extracted. Studies were also included that considered post-discharge needs of people with strokes from the perspective of caregivers and health professionals.Studies were included if they were able to consider the needs of stroke survivors after they had been discharged from rehabilitation. Studies could focus on exploring various needs, for instance physical, psychological, social, political, cultural, environmental, or rehabilitation. Needs had to be reported by the stroke patients themselves and during the post-discharge, subacute, or chronic phases. Mixed method studies were included if the qualitative data could be extracted. Studies that were excluded were those where stroke patients had their needs assessed prior to discharge, those that assessed patient satisfaction rather than perceived needs, those studies which solely assessed carers’ needs, and mixed studies where the qualitative data could not be extracted.All types of qualitative methodology were considered; for instance, this included types of grounded theory, types of phenomenology, types of narrative research, and descriptive or interpretive designs. A mixed methods study that included a clearly identifiable qualitative phase and reported that phase in a way that represented a form of qualitative design were included. Case studies were excluded.All types of methods used in qualitative studies were acceptable; for instance, this could include interviews, observations, field diaries, vignettes, or surveys with open questions. Studies that quantised data or restricted reporting of experiences were excluded.Qualitative or mixed methods designs were included.Studies that were conducted in hospitals, nursing homes, or the community.There was no limitation on dates of publishing. Only articles in English were considered and studies had to be related to humans and have an abstract.Two independent reviewers carried out the study selection process. The titles and abstracts were identified following a search and screening process, where any duplicates were removed. The eligible papers were saved on EndNote. Following the creation of the abstract shortlist, the two independent reviewers (BT and GA) screened the articles using the inclusion and exclusion criteria and compared their results. Disagreements were resolved by a third reviewer. The full text of the papers were read to shortlist the relevant articles.Quality appraisal was undertaken using the Joanna Briggs Institute qualitative critical appraisal checklist. The certainty assessment was carried out by using the GRADE-CERQual assessment tool. The quality of each paper was assessed using two independent reviewers (BT and GA) and a third reviewer (SR) in the case of any disagreements.A pilot data extraction was conducted on two papers . FollowiAn independent reviewer undertook the meta-ethnographic synthesis according to recommended guidelines for synthesising the data ,21. StudIn total, 5345 records were identified from the database search. Forty-three further records were identified from searching references. Following the removal of duplicates, there were 4627 articles, of which, 127 articles were assessed for eligibility. Ninety were excluded and thirty-one were included in the critical appraisal stage . Four stOf the twenty-seven included studies, eleven were conducted in the UK, six in Canada, five in Australia, two in the US, and one in each of China, Iran, Malaysia, and Norway. The sample size of included studies ranged from six participants  to one hThe quality assessment is included within the GRADE-CERQual assessment following the synthesis section . A breakTwo major themes related to the needs of stroke survivors emerged from the data. These included: (1) limited availability and suitability of information, (2) Adequacy of care and services.This theme contained three subthemes including:Subtheme One: information needs related to stroke pathology.This includes stroke definition, symptoms, signs, causes, treatment, complication, and stroke recurrence.The finding in this review revealed a lack of pre stroke information of stroke-risk factors and warning signs ,36,38,41I cannot understand what caused it to happen … I did not know what a stroke was. (p. 85)After stroke, detailed information on prevention of stroke recurrence was desired ,36,37,46That [information] was fairly zero, actually! I would have liked more information about how to prevent another stroke and also … any alarm signals. (p. 5)Subtheme Two: information needs regarding stroke recovery.Participants needs information on stroke treatment including information related to emergency intervention, medication, medication side effects, treatment plan, recovery rate, recovery facilitators, and guidance on available health services.Participants in the included studies explained that, during the immediate post-stroke phase, they needed a great deal of information regarding the subsequent course of action and details of those who can be contacted for help ,40.“I would like to know what services were available, you know.” (p. 87)Subtheme Three: the means of information delivery, including, the amount, relevance, time, format, and language of information.Amount: One study revealed that healthcare professionals provided insufficient amount of information to stroke patients. Although some information is provided, it was often vague or lacked information specific to the nature of their health status .Relevance: In terms of information content, participants indicated that the standard information packs that they were given had limited relevance, with some patients being given information about acquired brain injuries or all-age stroke groups. Most participants reported a need to receive information that was more relevant to their age group and health diagnosis ,26,33.“didn’t have relevant brochures … not a lot of detail.” (p. 72)Timing: Participants expressed needs for receiving information multiple times across the continuum as information can be difficult to absorb immediately post-stroke due to the stroke side effects, feeling overwhelmed, a chaotic environment, rehabilitation commitments, and memory deficiencies ,35,46.“Participants described the need for multiple repetitions of education over time, across the continuum of care, and into the chronic phase of stroke.” (p. 20)Format: There was a need for different types of education resources, such as written information , verbal information using group discussion and guest speakers, and visual teaching using videos and technology. Verbal delivery was the favoured mode for information delivery as it was suggested as easy to remember . The inf‘I need to learn, sometimes on radio and television they have programs about stroke recovery. I listen and use the information. Some guidelines are very important and can help us to improve our life style after stroke.’ (p. 251)After accounting for the need to tailor information, further factors that influenced stroke patients’ experience of care were reported. These factors can be divided into patient factors and staff factors. Participants preferred healthcare providers who were proactive in their initiation of education. Participants did not seek information because they do not know what to ask, forgot to ask, felt they did not need information, or assumed that information would be given to them ,40,46. P“You were lucky to get a doctor to come and speak with [you].” (p. 74)“The doctors always said … please feel free to ring up … But it’s like all these things, you don’t know the questions to ask. You’ve no idea.” (p. 5)“The physicians here at the hospital did not agree with the stroke rehab physicians about when I was going to start working.” (p. 745)Rehabilitation process experience: In order to return to motor activity as well as a better quality of life, rehabilitation is critical. Several patients indicated that they felt anxious and distressed about the therapy processes that they went through during rehabilitation. In general, they seemed to perceive that rehabilitation services were fragmented and disorganised ,33,43.“They don’t really help you get back into life, do they? They just sort of, you have a stroke, you have physio and that’s it.” Participants had coping issues when transferred back into the community including physical restriction, cognitive issues, and feelings of abandonment and being a burden on others ,28,42,43Subtheme One: Intervention needsIt was reported that rehabilitation programmes need to be more frequent, intensive, long-term, and flexible to meet participants’ goals and be appropriate for their age and health situation. Participants wanted return to work preparation, driving assessment, individualised sex intervention, and resources to support social participation.“there’s no way [the] one hour a week that the government gives you is going to fix people.” 1:1—Family support: A key part was played by participants’ families, who participated in caring roles, rehabilitation, and advocacy for improved services . Carer t“it was almost like [being] throwing in the deep end. I’ve never showered anyone in my life beforep … we just sort of muddled through … maybe like when you’re leaving rehab inpatient, someone probably should go through with the partner of the person about showering, medications.” (p. 78)1:2—Peer support: Stroke survivors stated that stroke recovery groups were an important factor for stroke recovery. Peer groups helped with psychological and emotional factors by having someone to talk to, sharing, understanding, learning from each other, and setting goals ,44. In s“I wish that there would be a therapy group for people who are in the same situation… how we can help each other, what kind of demands can I make at work. I would be open to that after 4 months, when I have come to terms with my situation a bit and it would’ve been OK to share it with others.” (p. 744)2—Income support: Many stroke patients lacked stability as a result of poor support from insurance or a lack of financial help . The rep“Definitely, one of the most important necessities for every human being in the world is social insurance that supports people with stroke so they have a stable community.” (p. 251)3—Travel support: Another important issue highlighted by participants was a lack of transport; survivors lacked access to public transport to different locations or experienced long waiting times for public transport and often the seats were full . Alterna“often the seats on trains and buses were full, disabled parking spots were often taken and that there was increased waiting time associated with public transport.” (p. 277)Subtheme three: PlanningPatients reported needing a clear treatment plan. These plans need to be individualised and relevant to their age and personal history . Further“It would have been good to get a plan earlier. To receive [a rehabilitation] contact earlier.” (p. 744)Subtheme Four: Care continuity1—Ongoing care: One of the most reported needs was continuity of care—participants expected care to be continued post-discharge. However, long waits for rehabilitation to commence post-discharge were reported in a number of the studies ,43,45,46“It’s a long time to wait before they came round, I wanted to get moving because the physio was so good in hospital … but then when you come home there’s nothing … I wanted to just get going and build on what I was doing in the hospital.” (p. 3)2—Follow-up: Post-discharge follow-up was one of the most reported needs. Patients needed follow-up services to have feedback on their progress, to be closely assessed, and receive guidance on performing activities appropriately and safely ,34. Alth3—Communication: Stroke survivors have difficulty in contacting healthcare professionals to obtain support. Participants felt it would benefit their rehabilitation outside of the hospital setting if they had a coordinator they could discuss services and concerns with . In addi“You should get some kind of contact person… someone who calls and checks on you “how is it now, are you experiencing any problems?” could refer you to, well, here or there.” (p. 744)“No automatic follow up on what I am doing. That’s the biggest problem. You’ve got to have some input…There is no follow up unless you do it yourself.” (p. 80)Regarding younger participants’ rehabilitation needs, three studies ,31,33 re“they just treated me like I was 70 years old but I’m actually 35” and “it’s like they put you into a box; you had a stroke, so this is how we deal with people that had a stroke.”  limited availability and suitability of information; (2) adequacy of care and services.The findings of the present review indicate that stroke survivors have a number of diverse educational needs, and many of these needs are currently being unmet. The findings are very much in line with those revealed in Hafsteinsdóttir et al.’s  study. PMoreover, the importance of presenting information in an appropriate way is well-recognised in the literature . The resEducation is paramount in ensuring effective communication. In the present study, participants reported difficulty in finding channels to contact the health services to acquire relevant information on how to manage their lives after suffering a stroke . The parLong-term care is generally considered to be more fragmented than acute stroke care . SeveralIt is important to note that this systematic review has a number of limitations. Firstly, it is a qualitative review that generates issues such as limited generalisability. This is because most studies in the review were conducted in developed countries. Thus, it is incredibly difficult to generalise the findings to developing countries or countries with a non-Western culture. There is a lack of research that has examined the unmet needs of stroke patients in Asia which contributes to poor awareness regarding the needs of stroke patients beyond hospital settings in general, as well as poor care provisions post-discharge. Additionally, the incidence of strokes is very different between developed and developing countries, and they have different healthcare systems, policies, and challenges when it comes to stroke care provision; thus, the extent of reported unmet needs may differ substantially. Nevertheless, a wide range of stroke patients’ needs were shared across developing and developed countries, such as information needs. Further, it is also important to consider that caregivers are constantly involved in caring for the stroke patient and thus, they often provide unique perspectives. Further, strokes affect patients and caregivers differently and thus, may have different needs. Despite the importance of caregivers, their perceptions were not considered in this systematic review. Further reviews could consider integrating evidences on unmet needs from different stalk holders. In addition, the review was not able to determine how the unmet needs are affected by various levels of physical, intellectual, and personality defects. Research is needed that takes on critical stances to reveal important social, educational, ethnic, and culturally sensitive information that will improve the ability to transfer these findings to different groups of people. This is especially important since socioeconomic and cultural factors influence the outcomes and, hence, the unmet needs of stroke patients.To ensure that education is sufficient and effective, healthcare workers must provide patients with relevant information on a regular basis. To achieve this, several different methods of patient education can be combined. It is also important that stroke patients are assigned a contact person who can help them to locate important information and resources, as well as answer their practical questions and concerns. Medical professionals must provide stroke survivors with discharge plans including follow-up appointments. Ideally, these follow-ups should initially take place six weeks after the discharge and then at the six-month stage. From then on, a yearly follow-up is sufficient . MoreoveIn this review, the needs and desires of stroke patients have been examined. The findings have indicated that the long-term needs of such patients are not being fully met at present. Moreover, the findings show that existing care plans for stroke survivors do not address the long-term needs of stroke survivors. Thus, continuous treatment and therapies are required as well as specially designed programmes that meet the long-term needs of stroke survivors. However, only three studies were included in this review, all of which were carried out in Asia and, thus, it is important to explore the topic in other continents and other settings in order to fully understand the issue at hand.


International organizations recommend mothers practice exclusive breastfeeding (EBF) during the first six months of their infant’s life and introduce complementary feeding (CF) thereafter while continuing breastfeeding. However, the earlier introduction of liquids and foods is common worldwide and may have negative effects on breastfeeding practice, nutrition, and health. In this formative cross-sectional study, we interviewed 143 mothers from semi-rural communities in Tabasco, Mexico, whose infants were 4–6 months old. We explored (1) which feeding practices substituted EBF and (2) which factors were associated with each practice. During the first month of life, 42.7% of infants received formula milk (FM); this proportion increased to 74.5% by the sixth month. Adjusted Poisson regression analyses showed that giving FM was positively related to working away from home  and the perception that FM is an important food to accompany breast milk . Giving FM was negatively associated with not being sure the infant is full after breastfeeding . Regarding CF, less than half (47.5%) of infants had not received it by the fifth month. Factors positively associated with timely CF introduction were: the mother was told during prenatal care visits the optimal age to start CF is 6 months ; she is convinced that giving only breast milk is best for her baby , and a higher infant weight-for-length  and length for age  z-scores at the study visit; conversely, it was negatively associated to the idea that if the infant is not full, she/he should receive formula milk or some other food . In these communities, EBF is lost to the use of FM and early CF. The factors associated with these inadequate feeding practices are related to returning to work, information received during prenatal visits, and the mother’s beliefs and thoughts. This work will guide the design of an intervention on infant feeding practices for these communities and other similar ones. Both the World Health Organization and UNICEF recommend that infants are exclusively breastfed for the first six months of life, which means that no other foods or liquids, including water, are provided to them during that period. Thereafter, age-appropriate and safe complementary foods should be introduced into their diet, while continuing to breastfeed for up to 2 years or beyond, if the mother and child so desire . NonetheThe problem with the introduction and regular administration of FM or any other liquid or solid food is that it reduces breastfeeding frequency as well as sucking time and possibly thoroughness of milk removal from the breast. Eventually, this cascade of events triggers negative effects on breastfeeding, ranging from pain and discomfort in breasts and nipples to reduction in milk volume and early cessation of breastfeeding ,6.The potentially harmful effects of feeding FM on infant health have different forms. Some studies have documented that very early supplementation with cow milk-based formula to infants from susceptible families may sensitize them to develop cow milk protein allergy ,8. ExcluOther adverse effects of inappropriate early infant feeding practices are, on one hand, an increased risk for growth faltering and micronutrient deficiencies which are commonly the result of two predisposing factors: (a) recurrent infections due to the use of contaminated liquids, solid foods, and infant feeding utensils ,13; and In Mexico, the latest nationwide data 2018) show that only 25.2% and 37.4% of children under six months, in urban and rural communities, respectively, receive exclusive breastfeeding (EBF) . Other s18 show tThe optimal and recommended way to carry out infant feeding has been extensively studied, and guidelines and programs have been proposed. However, the proven efficacy of such programs sometimes fails to translate into effectiveness in different contexts . What haIn the present community-based study, we aimed to describe: (1) which alternative feeding practices  substitute EBF; and (2) which sociodemographic, infant, and maternal factors are associated with each type of alternative breastfeeding practice. Factors were selected based on previous research ,24,25. TThis is a secondary analysis of a formative cross-sectional research carried out in semi-rural communities from Tabasco, which is a coastal southeastern state in Mexico. A detailed description of the study setting and demographic characteristics can be found in a recently published paper . BrieflyWe invited women to participate if they: (1) lived within the geographical limits of Villa Luis Gil Pérez or one of the other seventeen communities, (2) received prenatal care at CESSA or one of the FLPHU, (3) had a single and clinically healthy pregnancy, (4) had not been hospitalized for any condition that could be a barrier for breastfeeding initiation; (5) their infants were between 4 and 6 months old at the time of the study, and (6) accepted to participate and signed informed consent.In the original study about exclusive breastfeeding , the samTo construct the study variables, we used items from an ad hoc questionnaire and some previously validated scales, which were applied by the researchers during the study visit. The questionnaire can be found in the To describe the age at which different liquid and solid foods were first consumed by the infants, we asked: the infant’s age in months, the first time she/he received FM and other liquids  as well as the first time they received CF . From these questions, we constructed two distinct outcome variables: (1) consumption of FM (yes/no) and (2) early or timely complementary feeding (ECF/TCF). The ECF group included women who gave CF at four months or earlier, while the TCF group included those who did it timely, i.e., from the beginning of the fifth month or later . This laPotential influencing factors (independent variables) were selected based on previous research ,24,25, aMaternal sociodemographic factors included age (years), schooling (number of completed years of school), living with the infant’s father (yes/no), family type , number of live births (no previous liveborn neonate/at least one previous liveborn neonate), and occupation (stays at home/works away from home). For estimating household socioeconomic level, we used the AMAI 8 × 7 Socioeconomic Level (SEL) Index, which was a questionnaire developed by the Mexican Association of Market Intelligence and Public Opinion Agencies (AMAI). It is based on a statistical model that classifies Mexican households into seven levels, according to the head of household’s ability to satisfy its members’ needs . AccordiTo estimate household food security, we used the Latin American and Caribbean Food Security Scale (ELCSA) adapted for the Mexican population. It consists of 15 questions with answer options “yes” or “no” addressed to the head of the family or to the woman in charge of preparing food at home. It enquires about experiences of hunger by household members and concerns about lack of food security, referring to the three months prior to the interview. Depending on the number of affirmative responses to each question, the ELCSA classifies households into four categories: (1) food security, (2) mild food insecurity, (3) moderate food insecurity, and (4) severe food insecurity .We considered mode of delivery  and maternal cigarette and alcohol use at the time of the study visit (yes/no). Maternal weight and length were measured in triplicate at the study visit with equipment available at the CESSA or FLPHU using standardized protocols . The meaAt the study visit, infants’ weight and length were measured using a Tanita baby and mommy scale model 1582 and a portable SECA infantometer model 207. Infant’s length-for-age (LAZ) and weight-for-length (WLZ) z-scores were calculated using the WHO Child Growth Standards with the Anthro Survey Analyzer .We asked several questions to learn about maternal previous and present breastfeeding and CF experience beliefs and thoughts.We constructed the categorical variable “previous EBF experience” based on our previous observation that early EBF abandonment is common in this population, with almost 60% of women having abandoned it by the first month . TherefoAccording to WHO/UNICEF, all forms of feeding breast milk count as breastfeeding ; therefoOther categorical yes/no maternal factors considered were: not EBF at hospital discharge; the mother became sick and stopped breastfeeding; pain or discomfort on her breasts after hospital discharge; received free FM, bottles, or pacifiers during their prenatal visits or hospital; was told during their prenatal care visits that the optimal duration of EBF is six months and that CF should start at that age; and whether or not she received BF information or support during postpartum visits to the CESSA or FLHU.“Formula milk is an important food to accompany breast milk before six months”.“I am convinced that giving only breast milk until the baby is six months old, without giving any other food, is the best for her/him”.“When the baby is not full, you should give her/him powdered milk or some other food, even if she/he is less than six months old”.“If the baby is ‘small in size’ she/he should receive other foods to grow faster”.“When breastfeeding, you are always sure that your baby under six months is full”.“When it is very hot you should give your baby under six months water or some other liquid to drink”.To obtain some insight into what women believe and think about some aspects of infant feeding, and the influence these beliefs and thoughts had on the time they introduced CF, we asked them whether they agreed with the following statements:Mothers could respond on a Likert scale with five options ranging from “strongly agree” to “strongly disagree”, and a sixth option: “don’t know”. These six options were then narrowed down to two categories: “agree” and “disagree”, which were used in the analyses. t, or Mann–Whitney’s U test depending on variable type and distribution. Independent variables that were at least marginally (p ≤ 0.10) associated with the outcome variable were included in a Poisson regression computed through a General Linear Model considering a log-link function and a robust covariance estimator. We checked the uptake of variables for collinearity and accepted correlations <0.40; tolerance > 0.75; and FIV < 1.5.We first performed bivariate analysis to establish the association between the outcome variables and each independent variable. To select the variables for the models, we used Chi-square, Student’s-All statistical analyses were carried out in SPSS version 21.n = 24), their babies were older than 6 months (n = 22), presented complications during birth (n = 8) or did not accept to participate (n = 3). Therefore, a total of 143 women were included in the final sample.We invited 200 women to participate; 28.5% of them did not meet the inclusion criteria because they did not receive their prenatal care at CESSA (n = 3) drank alcohol around the time of the study visit.Most households belonged to the low SEL category. According to AMAI classification, such households range from a total lack of space and sanitary infrastructure to barely covering them. They also allocate a significant proportion of their income (between 42 and 52%) on food, leaving other needs uncovered.Previous Infant Feeding Experience, Breastfeeding Information, Difficulties, and Thoughtsn = 98) of the study sample. Most women with at least one previous live birth had breastfed their previous child , but only around half of them had practiced EBF beyond the first month of life . Women in the “no previous EBF experience” group comprised 68.5% (n = 87) said they received information on EBF duration and 45.5% (n = 65) were told the optimal breastfeeding duration is 6 months. Regarding CF, 43.4% (n = 62) reported receiving information about the time for introducing CF but only in 32.9% of cases (n = 47) was such information correct . In addition to the information received at health centers, women also reported receiving information on infant feeding from the television (50.0%), magazines (44.1%), radio (23.1%), newspapers (23.1%), and other sources . However, we did not inquire about the type or quality of such information.We asked women if during their prenatal care visits, they had received information on EBF and the introduction of CF. More than half ; only 2.8% (n = 4) reported receiving free formula, bottles, or pacifiers during their prenatal visits or hospital stay. During the following weeks, 14% (n = 20) of women said they suffered a disease that forced them to stop breastfeeding, although most were able to resume breastfeeding again (n = 14). Around half of our study sample  experienced pain or discomfort in breasts and/or nipples, but only one-fifth  received support or breastfeeding information at the CESSA or FLHU.After giving birth, most women left the hospital practicing EBF  and “When the baby is not full, you should give powdered milk…” . One-third of them disagreed with the ideas “I am convinced that giving only breast milk… is best for her/him”  and “When you finish breastfeeding… your baby is full” . Some agreed that “If the baby is small should receive other foods…” .An important proportion of women in our sample had beliefs or thoughts that may increase the risk of introducing FM and ECF. Around half of the mothers agreed that “Formula milk is an important food…”  received FM during the first month, 66.5% (n = 95) by the third month, and the proportion reached 74.5% (n = 107) by the sixth month.n = 35) of the infants during the first month, the proportion rising to 61.5% (n = 88) at three months and continuing to increase to 92.2% (n = 132) by six months. Other liquids such as juice, broth, and nutritive liquids were given to a small percentage of infants during the first three months, their consumption increasing at a slower pace. At six months of life, these liquids were given to around 40 to 55% of infants. Water and tea were also given to almost one-quarter (n = 110) agreed with the sentence: “When it is very hot you should give your baby under six months water or some other liquid to drink”.Since the climate in Tabasco is very hot most of the year, it is common to hear those infants receive water and tea to keep them hydrated. Most of the participants  to those who had never given it . More women in the formula group had at least one previous live birth , worked outside their home , and were not living with their infant’s father . Maternal BMI, mode of delivery, infant’s sex, LAZ, WLZ, and the sources of information about infant feeding were not different between groups.We wanted to understand the factors associated with the use of FM, since it is the earliest and most frequently given liquid food to infants. We compared women who had given FM at any given point of their infant’s life . These women were told during their prenatal care visits to practice EBF during the first six months of their infant’s life . There was no difference between groups in any other study variable. Regarding previous infant feeding practices, more women in the formula group had never breastfed or, if they did, they had practiced EBF for less than one month  and “When the baby is not full, you should give powdered milk…” . In contrast, more women who had not given FM agreed that “When you finish breastfeeding… your baby is full”  and “I am convinced that giving only breast milk… is best for her/him”  Regarding the participants’ thoughts about FM and breastfeeding, more women who gave formula agreed with the phrases: “Formula milk is an important food…”  of infants had initiated CF. All infants were 6 months old or younger.At the time of the study visit, 89.9% (n = 5) or second  months of life. Thereafter, the proportion increased, and by the fourth month, more than half  had had some solid food. Fresh vegetables, fruits, and cereals were the most given solid foods, which was followed by dairy products (petit-Suisse cheese and yogurt), commercial baby puree, and, to a lesser extent, meat, and legumes.As n = 120) about their reasons for doing so. The most common responses were: “the baby was hungry” , “the baby craved for food” , “the baby was ready/was old enough to receive them” , “a friend or family member recommended it’’  and “the doctor recommended it’’ . Most mothers who responded “the baby was ready/was old enough to receive them” were in the timely complementary feeding (TCF group) ; no other answer showed a difference between early complementary feeding (ECF) and TCF groups.We asked women who had begun CF  and TCF  groups. Women in the ECF group were marginally younger than their counterparts in the TCF group . Other sociodemographic characteristics did not differ between groups. Women were almost evenly distributed between the ECF  and tended to have EBF their previous infant for at least one month . The groups were not different in the proportion of women who became sick and stopped breastfeeding, experienced pain, or discomfort in breasts and/or nipples, gave FM to their infant or received breastfeeding information or support during postpartum.In the TCF group, there was a higher proportion of women who had been advised to start CF at six months during their prenatal care visits . In contrast, more women in the ECF group agreed with “When the baby is not full, you should give powdered milk…”  and “If the baby is small, she/he should receive other foods…” . Regarding the influence of maternal beliefs and thoughts about the introduction of CF, more women in the TCF group agreed with the thought “I am convinced that giving only breast milk… is best for her/him”  and WLZ . Women in the TCF group had higher BMI at study visit . There was no difference in the proportion of girls and boys between ECF and TCF groups. Infants in the TCF group had higher mean LAZ  by the sixth month. Almost one-third of the women began CF around the third month of age by giving their infants solid food for the first time.In Mexico, FM is most frequently introduced as the first liquid food into the infants’ diet, as has been described by national surveys and previous studies. Data from the ENSANUT  2012 included 1015 infants younger than 6 months, of which 56.7% received FM; 29% were also breastfed, and 26.69% no longer received human milk . In the p < 0.01) and those who lived in rural vs. urban areas  [More detailed information indicates that FM introduction during the first month is common, and figures very close to the 43% we observed in this study have been reported both in national surveys ,35 and i< 0.001) . Other s< 0.001) .We identified some maternal sociodemographic characteristics and thoughts that promote or discourage FM use in our study population. Infants are more likely to receive FM if the mother works outside her home, agrees that “Formula milk is an important food to accompany breast milk before six months”, or disagrees with the thought “When you finish breastfeeding, you are always sure that your baby under six months is full”. These findings are consistent with the evidence from studies in Mexico and other low- and middle-income countries (LMIC). Working outside the home has been linked to the reduction in EBF ,38,39 byHowever, it has been previously described that it is not the maternal place of work, but rather specific characteristics of employment, that influence infant feeding practices. Some employment conditions related to FM use are separation of the infant’s mother, non-flexible shifts, difficult transportation conditions, lack of childcare, and/or an exclusive and properly equipped breastfeeding room where women may breastfeed or express and store milk, and maternity leave policies ,43. To pThe belief that FM is an important food to accompany breastfeeding  has been documented in previous studies, showing the belief’s role as a barrier to EBF and its association with the introduction of FM to the infant’s diet . StudiesIn Indonesia, many caregivers also believe that FM feeding and breastfeeding are equally good options to feed their infants, and mixed milk feeding in infants under 6 months is widespread. A study documented that for participant mothers, the most important motivators for FM use were the perceived benefits on child growth, intelligence, and immunity. The study also revealed that there was almost universal exposure to the marketing of breast milk substitutes. The authors proposed that health and nutrition claims from FM vendors may influence caregivers and their feeding choices .Regarding exposure to FM marketing in our study, we only asked women if they had received free FM, bottles, or pacifiers during their prenatal visits or hospital stay. Very few (3%) women answered affirmatively. However, we did not explore their exposure to inappropriate marketing strategies outside the health system, either through publicity in mass media, points of sale, or the presence of nutritional and health claims on the products’ labels. These are the places where most of the FM promotion has been observed in Mexico and other countries ,44.The mother’s perception that the infant is still hungry after breastfeeding, suggesting there is insufficient milk production to make her infant feel full, has been consistently recognized as a reason for feeding FM ,44,45,46Regarding lactation physiology, most women experience normal lactogenesis and have the potential to produce an abundant milk supply for their infants from the start. Nevertheless, inadequate breastfeeding techniques, practices, or difficulties interfere with constant and efficient milk removal from the breast. This soon leads to diminished milk production. Factors that have been shown to interfere with milk removal are scheduled feeds, the introduction of FM or other liquids (without the corresponding expression of milk), and the use of pacifiers that reduce the time of breast sucking, among others ,48. The Other factors such as socioeconomic level, maternal schooling, area of residence , type of health unit (private or public), and age group (adolescents vs. adults) have also been identified to influence feeding FM ,42. HoweThe early introduction to the infant’s diet of water, tea, or other non-nutritive liquids has been previously observed in Mexico and other LMICs. In Mexico, a national survey in 2012 documented that 51% of infants younger than 6 months received water and 25.7% received water-based drinks  . A smallIn our study setting, where the climate is very hot, women usually give water to their infants, believing it will relieve their thirst. However, since breast milk is at least 80% water, especially the milk that first comes at each feed, even in hot climates, breast milk is the best liquid to satisfy the infant’s thirst . AdditioWe did not explore if water or the other non-nutritive liquids were given for other reasons different than those already discussed. In order to develop an intervention with an assertive and respectful message for discouraging the early introduction of water and water-based beverages to infants, it is necessary that we investigate this topic further.As with the use of FM and other liquids, the early introduction of CF is a common practice in Mexico and other countries. A study in the Mexican state of Hidalgo reported that a large proportion of infants (57%) started receiving CF at three months of age . In QuerOur study showed that four factors were strongly associated with TCF: (1) the mother was told during prenatal care visits that the optimal age to start CF is 6 months; (2) the mother disagreed with the phrase “When the baby is not full, you should give powdered milk…”; (3) or agreed with the phrase “I am convinced that giving only breast milk… is best for her/him” and (4) higher infant size: LAZ and WLZ. It was foreseeable that the information about CF women received during their prenatal care visits would influence their practice, as had been previously documented in other low-resource communities in Mexico . It is nAlthough we did not explicitly explore maternal breastfeeding self-efficacy , two beWith respect to infant size, in our study, smaller infants were prone to receive ECF. However, due to the cross-sectional design of the study, we were unable to distinguish between two possibilities that may explain this association: either infants who received ECF suffered weight faltering or smaller infants are more likely to receive ECF. In underdeveloped countries, it has been described, on one hand, that while infants grow steadily when breastfed exclusively, when CF is begun, they may suffer growth faltering as a result of infections and poor-quality liquid and solid foods. On the other hand, it has also been observed that in countries where undernutrition is common, ECF to small infants is considered a “good thing” to promote catch-up growth, survival, and better health .From these two possibilities, we consider that the second explanation may be appropriate for our study population, since another associated factor with TCF was the mothers’ disagreement with the phrase “If the baby is ‘small in size’ she/he should receive other foods to grow faster”. A similar observation was previously documented in a study in the Gambia, where higher weight-for-length predicted the introduction of CF at an older age. The authors suggest that the influence of infant size on the initiation of CF may be due to the mothers’ choosing to continue EBF when their infant is growing well .There are some limitations to our study, such as its cross-sectional design, which entails a key disadvantage: the retrospective nature of data collection that raises a possible recall bias, specifically about the precise moment in which participants introduced CF to their infant’s diet. However, infants’ age at the time of the study was near the time of introduction, possibly reducing such bias.The study sample was rather small, and due to the study’s objectives and design, it was also quite homogeneous. The latter would imply that most of our observations may not represent women living in other settings. However, together with data from national surveys and information reported from other regions of Mexico ,21,35, oThe use of FM, water, and the early introduction of complementary foods are common infant feeding practices that substitute EBF in the community we studied. We identified a number of factors associated with these practices. Not all women in the community receive support and information on infant feeding practices during prenatal and postpartum visits, and when they do, sometimes, such information is incorrect. This reveals the need to further study the infant feeding knowledge and beliefs health providers have, design a training strategy for them, and integrate counseling on infant feeding as a central part of women’s care.Women in the community have beliefs that negatively influence their infant’s feeding practices, reflecting a lack of understanding about the physiology of breastfeeding and incorrect or unrealistic expectations about the behavior and growth of breastfed babies. Such beliefs include giving water and other non-nutritive liquids to relieve thirst in the hot climate or the idea that FM is an important food in an infant’s diet. The latter being so widespread an idea suggests that FM is being normalized and possibly considered a desirable practice. More qualitative research is needed to delve into this hypothesis and propose strategies to modify how FM is perceived in this population. Additionally, the mother working outside the home was the factor most strongly associated with the use of FM. It is important to deepen the research to know the type and conditions of maternal employment and be able to design an intervention that responds to the specific needs of working women in this community and makes breastfeeding feasible for them.In general, we found that beliefs about infant feeding are the factors that ultimately influence the way infants are fed. It is important to design and implement strategies that respectfully modify beliefs and promote an environment that normalizes breastfeeding through the information available from health services and the media, among other influential sources.


The model leverages (i) available OSM tags capturing non-spatial attributes, (ii) geometric and topological properties of the building footprints including adjacent types of roads, proximity to parking lots, and building size. The model is trained and tested using ground truth data available for the three study areas. The results show that our approach achieves high accuracy in predicting building types for the selected areas. Additionally, a trained model is transferable with high accuracy to other regions where ground truth data is unavailable. The OSM and data science community are invited to build upon our approach to further enrich the volunteered geographic information in an automated manner.Having accurate building information is paramount for a plethora of applications, including humanitarian efforts, city planning, scientific studies, and navigation systems. While volunteered geographic information from sources such as OpenStreetMap (OSM) has good building geometry coverage, descriptive attributes such as the type of a building are sparse. To fill this gap, this study proposes a supervised learning-based approach to provide meaningful, semantic information for OSM data without manual intervention. We present a basic demonstration of our approach that classifies buildings into either  Volunteered geographic information, which leverages local knowledge to map the geometries and attributes of both natural and urban features, is widely used for humanitarian crises3, city planning4, scientific studies6, and navigation systems7. For example, OSM building footprints as well as streets, roads, rivers, and basic community services have been used to support urban planning and land administration, especially in parts of the world with little traditional data availability8.OpenStreetMap12. However, even in the data rich locations, the semantic information that records the type and function of these features is very sparse such that the vast majority of features mapped have little to no descriptive attributes.For many locations, OSM geometries delineating streets, natural features, and building footprints are highly complete and accurate, often matching or overtaking traditional data sources such as the Central Intelligence Agency (CIA) World Factbook and United States Census Topologically Integrated Geographic Encoding and Referencing (TIGER)/Line dataresidential and non-residential buildings in OSM  with the ground truth data. OSM correctly labels 12.84% of residential and 19.26% of non-residential buildings for Fairfax County, 9.33% of residential and 10.48% of non-residential buildings for Mecklenburg County, and 67.75% of residential and 42.23% of non-residential buildings for the City of Boulder. Note that the total number of labeled buildings and the number of correctly labeled buildings for each type are almost the same. Thus, despite the lack of completeness in building type information, the number of misclassified buildings is less than 1%.To illustrate this, Table residential or non-residential. This particular semantic information is of limited availability at the building footprint level in, both, OSM and official datasets across the United States (US) and globally.Figure First, we use existing high quality data available through OSM to derive geometric attributes for each building footprint  as well as available descriptive attributes in three study areas in the US, as follows: Fairfax County in Virginia, Mecklenburg County in North Carolina, and the City of Boulder in Colorado. Our choice of location has been dictated by available ground truth data and to provide a mix of urban, sub urban and rural areas. Next, based on existing and newly derived building footprint attributes, a set of models are trained to classify the building type using ground truth data obtained from official sources from each study area. Upon comparing to ground truth testing data, we show that our learned models yield high accuracy in all three locations. The learned models from each study area are then transferred to classify building types in alternative study areas. As we will show, this approach again yields high accuracy. The results demonstrate that our approach (1) exhibits high accuracy in regions where authoritative ground truth data is available to train the model, and (2) transfers to new regions for which no ground truth may be available for training. We note that this is just one application of our approach and thus invite the OSM and data science community to build upon it in order to enrich such volunteered geographic information without extensive manual efforts.Before the methodology, we first present the main findings of our approach. Figure https://github.com/heykuldip/osm_buildings_classification and a repository of the data used is at https://osf.io/3j46v/.Recall that given the sparsity of the semantic information encoded in OSM building data, our objective is to predict the type of buildings based on data available in OSM. Below we describe (1) ground truth and OSM data acquisition for each of the study regions, (2) data processing, (3) feature extraction from OSM building footprints, and (4) building classification. For reproducibility, all our code is available at We selected three study areas for which we were able to obtain ground truth data including Fairfax County, Mecklenburg County, and the City of Boulder. We included the City of Boulder to examine model differences in a city versus suburban setting  to these two classes. For OSM data, we aggregate building types based on the building tag values to create three meta-categories—residential, non-residential, and unknown  indicating the specific road class . We hypothesise that this information is a useful predictor to classify the type of nearby buildings.The road network is one of the most exhaustive features in OSM that has been used as an effective method for identifying Residential Roads: Using tag values ‘residential’ and ‘living_street’; (2) Highways: Using tag values ‘primary’, ‘secondary’ and ‘tertiary’; (3) Motorways: Using tag values ‘motorway’ and ‘trunk’; (4) Service roads: Using tag value ‘service’.For this purpose, we enrich each building in OSM with multiple dichotomous indicator variables that discriminate whether or not each building falls in range of four road meta-categories: (1) residential roads, (2) highways, (3) motorways, and (4) service roads. The OSM ‘highway’ tag defines the road types according to their types and capacities, varying from pathways to expressways. The road type tags in OSM map to our meta-categories as follows: (1) For each meta-category of roads, we add three indicator attributes to each building, where a value of 1 indicates that the building is located in a 0–30 m, 0–60 m, and 0–90 m range of the road network and a value of 0 indicates that it is not. This yields a total of twelve indicator attributes for each building where indicators 0 to 3 correspond to residential roads, 4 to 6 to highways, 7 to 9 to motorways, and 10 to 12 to service roads. For example, the indicator values  indicate that a building falls within a 0–30 m radius of a residential road (indicated by the first indicator variable) and thus, also in a 0–60 m and 0–90 m radius (indicated by the second and third indicator variable). The building is not in range of any highways or motorways. However, the building falls within 0–90 m distance of a service road (indicated by variables twelve), but not within 0–30 m or 0–60 m.To efficiently compute the indicator variables for each building, we create corresponding buffers  around each road in OSM. Then, we perform a spatial join between these buffers and the polygons of the buildings in OSM. For each intersection, depending on the ‘highway’ tag of the road, the corresponding indicator variable (using the mapping above) is set to 1.non-residential building would also increase. We extract the parking lot geometries from the OSM data using the ‘amenity’ tag having a value of either ‘parking’ or ‘parking_space’. We first examine the distribution of parking lot size across the study region and create three classes of parking lots based on the natural breaks of the parking lot size distribution using the Fisher-Jenks algorithm22.We hypothesize that distance from parking lots of various sizes can be used to predict building type. For example, we would expect that as parking lot size increases, the likelihood that the building is a Next, we enrich each building in OSM with parking lot indicator variables that indicate whether or not each building falls in a 30 m, 60 m, or 90 m range of three parking lot categories: (1) small, (2) medium, and (3) large, yielding a total of nine additional indicator variables. To compute the parking lot indicator variables for each building, we create corresponding buffers around the parking lots. We then perform a spatial join between these buffers and the polygons of the buildings in OSM.23. Therefore, in addition to the road network and parking lot buffers, we compute the area based on the building footprint geometry and use the area as another  feature for our decision tree model.The size of a building footprint can a key predictor of building type24. Therefore, we extracted polygons having the ‘landuse’ tag in the OSM data and spatially joined them with the building footprints, resulting in another feature for our machine learning model.OSM data includes the geometries and descriptive attributes for different underlying land use upon which the buildings are located. This data may explicitly contain information on the use of the land that the buildings are built on, thus providing insight into the use of the building itselfIn addition to geometry, each building has a set of associated tags, which describe features using pairs of unique keys and corresponding values. Besides the above derived features, we utilized the tags from the OSM data that we deemed relevant for accurately categorizing the buildings. The tags are: ‘building’, ‘name’, ‘source’, ‘addr:street’, ‘building:levels’, ‘shop’, ‘website’, ‘brand’, and ‘amenity’. With the exception of the ‘building’ tag, each of the tags themselves are treated as a binary indicator variable where buildings have a value of 0 if they do not have a tag and 1 if they do. For the ‘building’ tag, we utilize the tag value rather than the presence or absence of the tag itself and encode each of the values as a nominal indicator variable. Since there are theoretically an infinite number of building tag values, we select the most common values, namely the values ‘apartments’, ‘church’, ‘civic’, ‘commercial’, ‘construction’, ‘detached’, ‘dormitory’, ‘garage’, ‘garages’, ‘greenhouse’, ‘hospital’, ‘hotel’, ‘house’, ‘industrial’, ‘kindergarten’, ‘office’, ‘parking’, ‘public’, ‘residential’, ‘retail’, ‘roof’, ‘school’, ‘semidetached_house’, ‘service’, ‘shed’, ‘static_caravan’, ‘terrace’, ‘warehouse’, and ‘yes’. We create a separate nominal variable called ‘miscellaneous’ that includes all the remaining unique building values across the three study areas.In general, we manually selected these tags based on their relevance to distinguish building types while making sure that the model is capable of transfer learning independently of any geographic area. For example, if a building contains a website address, it seems more likely to be classified as ‘non-residential’. It is worth noting, however, that our model is flexible to handle any tags available in the OSM raw data, the hand-picked tags are a proof-of-the-concept of our proposal.25 to recursively find the attributes that yield the highest information gain to construct the decision tree. To train the decision tree, we use the authoritative ground truth building type obtained from the respective counties and city. Our choice of using a decision tree for classification was made due to it’s interpretability, allowing us to understand where and why classification errors are made to guide our search for discriminatory features to separate the residential and non-residential classes. To parameterize our decision tree, we use Gini-index26 which is commonly used as a measure of impurity between classes. We prune the decision tree when no additional decision criterion increases the impurity of a node by no more than 0.01%.Using the features described in the previous sections, we use a classic C4.5 binary decision tree classifiernon-residential and 155,600 are residential. This decision tree has a total of 148 nodes including 72 leaf nodes. Note that the root of the tree starts by using the area of the building, indicating that it is the most discriminating feature. This result confirms existing work, which has shown that the size of the building footprint area is an important predictor of a building type23. Specifically, the decision-tree first checks whether the area of the building is less than 1319.7 square meters. Given that there is a larger proportion of residential buildings in the group with the small building footprints , the decision-tree learns that smaller buildings tend to be residential. In contrast, given that there is a larger proportion of non-residential buildings in the group with the larger building footprints , the decision-tree learns that larger buildings tend to be non-residential.Figure residential buildings . However, in the case that buildings are (1) smaller than 1319.7mnon-residential land use tend to be non-residential buildings . In fact, for this branch, the decision tree concludes that other attributes no longer provide sufficient reduction of impurity (as measured by Gini index) of this node, thus that this node is a leaf node, thus predicting all such buildings as non-residential.In both resulting branches, the decision tree then checks the ‘landuse’ tag, confirming our intuition that the type of land use is a useful predictor of the type of a building. But it is nowhere near sufficient to classify the type of all buildings. The decision tree learns that buildings that are (1) smaller than 1319.7mresidential buildings . Based on the Gini index, the decision tree decides this node to be a leaf node, thus classifying such buildings as non-residential buildings. The interested reader may refer to the high resolution complete decision tree in our Github repository at https://github.com/heykuldip/osm_buildings_classification for Fairfax County to understand which attributes and their values guide this decision tree model.Other branches are longer, up to a length of 17. An important branch of this tree is the branch in which the decision tree learns that buildings that are (1) small, (2) of non-residential land use, (3) are not within a 60 m range of Category 1  roads, (4) do not have a building name, (5) do not have ‘misc’ land use, (6) are not within a 60 range of a Category 4 (service) road, (7) are not have the value ‘miscellaneous’ in the ‘building’ tag, (8) does not have the value ‘commercial’ in the building tag, (9) does not have the value ‘amenity’ in the building tag, and (10) does not have the value ‘office’ in the building tag tend towards accuracy of the classification, defined as the fraction of correctly classified building types across all buildings. However, some of the study regions such as Fairfax County have a class imbalance, where the number of residential buildings far outweigh the number of non-residential buildings :precision of a class, defined as the number of buildings correctly predicted as that class divided by the total number of buildings predicted as that class. Intuitively, the precision of class corresponds to the probability that a building that is predicted to have class X actually has class X.The recall of a class, defined as the number of buildings correctly predicted as that class divide by the total number of buildings in that class (in the ground truth). Intuitively, the recall of a class corresponds to the probability that a building have class X is correctly classified as class X.The -score of a class, which is the harmonic mean of precision and recall of a class.The To quantitatively evaluate our model, we first measure the Recall that in the “residential class. This comes at a cost for the non-residential class, having a precision of non-residential are predicted incorrectly. We also observe a recall of only non-residential buildings are incorrectly predicted as residential buildings. For City of Boulder, where the overall accuracy is lower than for Fairfax County, we see that the non-residential class is classified more accurately, indicated by higher precision and recall values. Summarizing, we observe that our model is very accurate at predicting residential buildings, evident by an residential class. However, our model does make more errors predicting the non-residential class, evident by non-residential class.We observe that Fairfax County has the highest accuracy at 28. This type of data is commonly used for a variety of applications ranging from geospatial modeling and analysis and urban planning applications. To this end, we designed our pre-trained classification model so that it can be applied to any study area, requiring only OSM data as an input. To demonstrate the effectiveness of the pre-trained model, we tested each of the three models  on the alternative study areas. The result of the transfer learning experiments (that transfer the model trained on one region to another region) are presented in Table Our study was motivated by the fact that it is hard to find descriptive attributes (such as building type) in both, official and OSM data sourcesOverall, we observe that transferring models reduces their accuracy and residential class are still very good, with non-residential class, having values between 0.5929 and 0.7704 for the models trained in Fairfax County and Mecklenburg County. For the model trained for the City of Boulder, the results of transferring the model to Fairfax and Mecklenburg County are substantially worse. We mainly attribute this bad performance to the small size of the City of Boulder dataset, having an order of magnitude fewer buildings  of a building does not substantially increase the overall accuracy, but it does increase the average non-residential buildings. Looking at our decision tree in Fig. non-residential, which appears to be an intuitive rule learned by the decision tree. Finally, we also include information on the distance to roads and parking lots (as described in the “Methods” section) to obtain our full model, which yields a very high boost in all metrics.In the next experiment, we increase the complexity of the decision tree model by adding other selected tags  data46. Semantic analysis is also coupled with the random forest method to classify urban buildings from images into finer categories48. A natural language processing (NLP)-based approach is used to classify point-of-interest (POI), land use, and roads data extracted from Baidu Maps that can infer building types49. Similar methods are used for correcting OSM building annotations50, street labels predictions51, autonomous robot navigation52, 3D building models53, and land cover classification55. However, these approaches either use region-specific or proprietary datasets that are hard to obtain for applying the models in different places. Our model relies only on the OSM features, eliminating the bottleneck of unavailability or incompatibility of additional data for certain regions. Therefore, the novelty of our approach lies in incorporating geometric, topological, and non-spatial features including distance to surrounding roads of different types, distance to parking lots of different sizes, underlying land use, the area of a building polygon, and a wide variety of user-generated OSM tags.Machine learning approaches are successfully employed to extract building footprints from satellite and aerial images18. Our hypothesis is that by combining multiple attributes derived from OSM data, we can improve the quality of such a classification. Thus, we extracted various spatial and non-spatial features of buildings with surroundings and trained a decision tree classier to interpret the generated rules. We corroborated the effectiveness of our approach by applying our model to Fairfax County, Mecklenburg County, and City of Boulder, and comparing the results with corresponding ground truth data. An important outcome of this study is that our model transfers, thus allowing to apply a model learned in one region to a different region, while maintaining high accuracy. Specifically, we observe that a model learned using data from Fairfax County achieves an accuracy of 96.4% when applied to buildings in Mecklenburg County. This result indicates that the models learned in the three regions for which building type data is publicly available can be used for other regions where no authoritative ground truth data is available.Previous work shows that attributes such as shape and area are effective for building classification58. Furthermore, it remains unclear how well our proposed building type classification model transfers to regions and cities outside of the United States. As OSM data is available globally, and tags of buildings, road types, and parking lots are available globally, we theorize that our models should be applicable globally. But further studies using regions outside the United States is needed.Our analysis shows that although OSM building footprints coverage is extensive, the geometries are still incomplete, observed in cases where building footprints exist in authoritative data sources but not in OSM data. For example, the ground truth data we used from Fairfax County Geographic Information System (GIS) and Mapping Services has 269,366 buildings, but OSM data for the same region includes only 204,672 buildings, which means  24% of buildings are still missing in OSM. Other studies also highlight the incompleteness of OSM buildings compared to authoritative data sourcesresidential or non-residential. Our solution for classifying building types broadly using OSM data has applications in systems that use OSM data, for example for semantic mapping59. By enriching OSM data with accurate building types, our model improves the input data for such applications.The lack of standards for OSM user attribute tags and values results in sparse and heterogeneous attribute information, limiting the usage of otherwise rich and accurate OSM data for a variety of applications. Using the approach developed in this work, we can enrich existing OSM data and achieve complete and highly accurate semantic information for such data. Specifically, we designed a supervised learning model that uses OSM raw data to predict building types as 60, may further improve building type prediction. Additionally, we formulated the classification of buildings as a two-class problem and predicted the labels according to their functionality. It could limit the usefulness of our approach, especially for the non-residential class, which can be further classified as commercial, industrial, public, etc. As a future line of research, we are interested in extending this work to a multi-class classification problem. We hope that the OSM and data science communities will support this automated enrichment effort and increase the value and usefulness of volunteered geographic information. There is a lot of ‘intrinsic’ information in OSM that is waiting to be uncovered!For this work, we chose decision trees to allow for easy interpretability of the resulting classification models. Future work may investigate whether more sophisticated classification methods, including deep learning modelsSupplementary Information.